Understanding the heterogeneity of depression through the triad of symptoms, course and risk factors: a longitudinal, population-based study

Using digital phenotyping to capture depression symptom variability: detecting naturalistic variability in depression symptoms across one year using passively collected wearable movement and sleep data

Major Depressive Disorder (MDD) presents considerable challenges to diagnosis and management due to symptom variability across time. Only recent work has highlighted the clinical implications for interrogating depression symptom variability. Thus, the present work investigates how sociodemographic, comorbidity, movement, and sleep data is associated with long-term depression symptom variability. Participant information included (N = 939) baseline sociodemographic and comorbidity data, longitudinal, passively collected wearable data, and Patient Health Questionnaire-9 (PHQ-9) scores collected over 12 months. An ensemble machine learning approach was used to detect long-term depression symptom variability via: (i) a domain-driven feature selection approach and (ii) an exhaustive feature-inclusion approach. SHapley Additive exPlanations (SHAP) were used to interrogate variable importance and directionality. The composite domain-driven and exhaustive inclusion models were both capable of moderately detecting long-term depression symptom variability (r = 0.33 and r = 0.39, respectively). Our results indicate the incremental predictive validity of sociodemographic, comorbidity, and passively collected wearable movement and sleep data in detecting long-term depression symptom variability.

Depressive Symptom Subgroups and Their Association with Prevalent and Incident Cardiovascular Risk Factors in the Coronary Artery Risk Development in Young Adults (CARDIA) Study

BACKGROUND

We sought to identify depressive symptom subgroups in a community sample of young adults, investigate their stability over time, and determine their association with prevalent and incident cardiovascular disease (CVD) risk factors.

METHOD

Participants were 3377 adults from the Coronary Artery Risk Development in Young Adults study. Using latent class and latent transition analysis, we derived subgroups based on items of the 20-item version of the Center for Epidemiologic Studies Depression Scale in 1990, and examined patterns of change over a 10-year period (1990-2000). Cox regression models were used to examine associations between subgroup membership and prevalent (2000) and incident (2000 to 2016) obesity, hypertension, and diabetes.

RESULTS

Three baseline subgroups were identified and labeled: "No Symptoms" (63.5%), "Lack of Positive Affect" (PA, 25.6%), and "Depressed Mood" (10.9%). At 10-year follow-up, individuals in "No Symptoms" subgroup had the highest probability (0.84) of being classified within the same subgroup. Participants classified as "Lack of PA" were likely (0.46) to remain in the same subgroup or be classified as "No Symptoms." Participants in the "Depressed Mood" were most likely to transition to the "Lack of PA" subgroup (0.38). Overall, 30.5% of participants transitioned between subgroups, with 11.4% classified as "Worsening" and 19.1% as "Improving." Relative to the "No Symptoms Stable," other subgroups ("Depressed Stable," "Worsening," and "Improving") were associated with prevalent obesity and hypertension.

CONCLUSION

We identified distinct depressive symptom subgroups that are variably stable over time, and their change patterns were differentially associated with CVD risk factor prevalence.

Trajectories of antepartum depressive symptoms and birthweight: a multicenter and prospective cohort study

BACKGROUND

Antepartum depression is a prevalent unhealthy mental health problem worldwide, particularly in low-income countries. It is a major contributor to adverse birth outcomes. Previous studies linking antepartum depression to birthweight have yielded conflicting results, which may be the reason that the depressive symptoms were only measured once during pregnancy. This study aimed to explore the associations between trajectories of antepartum depressive symptoms and birthweight.

METHODS

Depressive symptoms were assessed prospectively at each trimester in 3699 pregnant women from 24 hospitals across 15 provinces in China, using the Edinburgh Postpartum Depression Scale (EPDS). Higher scores of EPDS indicated higher levels of depressive symptoms. Associations between trajectories of depressive symptoms and birthweight were examined using group-based trajectory modeling (GBTM), propensity score-based inverse probability of treatment weighting (IPTW), and logistic regression.

RESULTS

GBTM identified five trajectories. Compared with the low-stable trajectory of depressive symptoms, only high-stable (OR = 1.35, 95% CI: 1.15-2.52) and moderate-rising (OR = 1.18, 95% CI: 1.12-1.85) had an increased risk of low birthweight (LBW) in the adjusted longitudinal analysis of IPTW. There was no significant increase in the risk of LBW in moderate-stable and high-falling trajectories. However, trajectories of depressive symptoms were not associated with the risk of macrosomia.

CONCLUSION

Antepartum depressive symptoms were not constant. Trajectories of depressive symptoms were associated with the risk of LBW. It is important to optimize and implement screening, tracking, and intervention protocols for antepartum depression, especially for high-risk pregnant women, to prevent LBW.

Symptom clustering of major depression in a national telehealth sample

BACKGROUND

Major depressive disorder (MDD) is a heterogeneous disorder whose possible symptom combinations have not been well delineated. The aim of this study was to explore the heterogeneity of symptoms experienced by those with MDD to characterize phenotypic presentations.

METHODS

Cross-sectional data (N = 10,158) from a large telemental health platform were used to identify subtypes of MDD. Symptom data, gathered from both clinically-validated surveys and intake questions, were analyzed via polychoric correlations, principal component analysis, and cluster analysis.

RESULTS

Principal components analysis (PCA) of baseline symptom data revealed 5 components, including anxious distress, core emotional, agitation/irritability, insomnia, and anergic/apathy components. PCA-based cluster analysis resulted in four MDD phenotypes, the largest of which was characterized by a prominent elevation on the anergic/apathy component, but also core emotional. The four clusters differed on demographic and clinical characteristics.

LIMITATIONS

The primary limitation of this study is that the phenotypes uncovered are limited by the questions asked. These phenotypes will need to be cross validated with other samples, potentially expanded to include biological/genetic variables, and followed longitudinally.

CONCLUSIONS

The heterogeneity in MDD, as illustrated by the phenotypes in this sample, may explain the heterogeneity of treatment response in large-scale treatment trials. These phenotypes can be used to study varying rates of recovery following treatment and to develop clinical decision support tools and artificial intelligence algorithms. Strengths of this study include its size, breadth of included symptoms, and novel use of a telehealth platform.

Trajectories of depressive symptoms during pregnancy and risk of premature birth: A multicenter and prospective cohort study

Previous studies only assessed the association between depressive symptoms and risk of preterm birth (PTB) at a time-point during pregnancy, resulting in inconsistent or contradictory results. Therefore, we aimed to explore the associations between the trajectories of depressive symptoms during pregnancy and risk of PTB. In total, 7732 pregnant women were included in 24 hospitals from 15 provinces of China. The Edinburgh Postpartum Depression Scale (EPDS) was used to evaluate depressive symptoms in the first, second, and third trimesters. Associations between depressive symptoms and risk of PTB were performed by group-based trajectory modeling (GBTM), propensity score-based inverse probability of treatment weighting (IPTW), and logistic regression. GBTM identified five trajectories: compared with persistently low-stable trajectory of depressive symptoms, women with moderate-stable (OR = 1.23, 95% CI: 1.02-1.76), high-falling (OR = 1.35, 95% CI: 1.11-2.21), moderate-rising (OR = 1.38, 95% CI: 1.06-2.04), and high-stable trajectory of depressive symptoms (OR = 1.40, 95% CI: 1.16-3.28) had an increased risk of PTB. In addition, the associations between trajectories of depressive symptoms and risk of PTB were most significant in multiparous women with a history of PTB. There was no difference in the risk of early-moderate PTB among different trajectories of depressive symptoms and only the risk of late PTB was different among different trajectories. In conclusion, the depressive symptoms of pregnant women were not constant during pregnancy, and different trajectories of depressive symptoms were associated with different risks of PTB.

Differential Associations Between Depressive Symptom-Domains With Anxiety, Loneliness, and Cognition in a Sample of Community Older Chinese Adults: A Multiple Indicators Multiple Causes Approach

Background and Objectives

Depressive symptoms are common in older adults, and often co-occur with other mental health problems. However, knowledge about depressive symptom-domains and their associations with other conditions is limited. This study examined depressive symptom-domains and associations with anxiety, cognition, and loneliness.

Research Design and Methods

A sample of 3,795 participants aged 60 years and older were recruited from the community in Hong Kong. They were assessed for depressive symptoms (Patient Health Questionnaire-9 [PHQ-9]), anxiety (Generalized Anxiety Disorder 7-item), loneliness (UCLA 3-item), and cognition (Montreal Cognitive Assessment 5-Minute Protocol). Summary descriptive statistics were calculated, followed by confirmatory factor analysis of PHQ-9. Multiple Indicators Multiple Causes analysis was used to examine the associations between mental health conditions in the general sample and subgroups based on depressive symptom severity.

Results

A 4-factor model based on the Research Domain Criteria showed the best model fit of PHQ-9 (χ2/df = 10.63, Root-Mean-Square Error of Approximation = 0.05, Comparative Fit Index = 0.96, Tucker-Lewis Index = 0.93). After adjusting for demographics, 4 depressive symptom-domains were differentially associated with anxiety, loneliness, and cognition across different depression severity groups. The Negative Valance Systems and Internalizing domain (NVS-I; guilt and self-harm) were consistently associated with anxiety (β = 0.45, 0.44) and loneliness (β = 0.11, 0.27) regardless of depression severity (at risk/mild vs moderate and more severe, respectively, all p < .001).

Discussion and Implications

The consistent associations between the NVS-I domain of depression with anxiety and loneliness warrant attention. Simultaneous considerations of depressive symptom-domains and symptom severity are needed for designing more personalized care.

Clinical Trials Registration Number

NCT03593889.

The genetic basis of major depressive disorder

The genetic dissection of major depressive disorder (MDD) ranks as one of the success stories of psychiatric genetics, with genome-wide association studies (GWAS) identifying 178 genetic risk loci and proposing more than 200 candidate genes. However, the GWAS results derive from the analysis of cohorts in which most cases are diagnosed by minimal phenotyping, a method that has low specificity. I review data indicating that there is a large genetic component unique to MDD that remains inaccessible to minimal phenotyping strategies and that the majority of genetic risk loci identified with minimal phenotyping approaches are unlikely to be MDD risk loci. I show that inventive uses of biobank data, novel imputation methods, combined with more interviewer diagnosed cases, can identify loci that contribute to the episodic severe shifts of mood, and neurovegetative and cognitive changes that are central to MDD. Furthermore, new theories about the nature and causes of MDD, drawing upon advances in neuroscience and psychology, can provide handles on how best to interpret and exploit genetic mapping results.

[The impact of depression symptoms on postmorbid adaptation of patients who have undergone SARS-CoV-2 infection (assessment from the position of the multimorbidity model)]

OBJECTIVE

To evaluate the impact of psychosomatic and anxiety symptoms of depression on the formation of postmorbid adaptation in patients who have undergone SARS-CoV-2 infection.

MATERIAL AND METHODS

The data of 54 patients, aged 18-55 years, with a current depressive episode who had outpatient SARS-CoV-2 infection were analyzed. The Hamilton depression rating scale (HDRS) was used and somatic condition was assessed using a general assessment of the patient's function limitation at the stage of reconvalescence. A linear regression analysis was performed to assess the association of psychosomatic and anxiety symptoms with somatic condition in SARS-CoV-2 infection, regardless of other factors.

RESULTS

The multimorbidity model took into account, in addition to SARS-CoV-2 caused by organic somatic disorders, psychosomatic manifestations (B=-1.9, p=0.004) and anxiety (B=-3.7, p=0.04) symptoms of depression. Somatic anxiety was significantly correlated with a pronounced impairment of postmorbid readaptation (p<0.05), while mental anxiety did not demonstrate such a relationship (p=0.46). The observed trend seems to be due to the overlap of manifestations of somatic anxiety with the construct of residual somatic symptoms, even though there is no multicollinearity between them.

CONCLUSION

Identification of clusters of symptoms associated with reduced opportunities for full recovery in the SARS-CoV-2-postmorbid period can significantly help in providing a high-quality and targeted psychopharmacological care.

Larval Zebrafish as a Model for Mechanistic Discovery in Mental Health

Animal models are essential for the discovery of mechanisms and treatments for neuropsychiatric disorders. However, complex mental health disorders such as depression and anxiety are difficult to fully recapitulate in these models. Borrowing from the field of psychiatric genetics, we reiterate the framework of 'endophenotypes' - biological or behavioral markers with cellular, molecular or genetic underpinnings - to reduce complex disorders into measurable behaviors that can be compared across organisms. Zebrafish are popular disease models due to the conserved genetic, physiological and anatomical pathways between zebrafish and humans. Adult zebrafish, which display more sophisticated behaviors and cognition, have long been used to model psychiatric disorders. However, larvae (up to 1 month old) are more numerous and also optically transparent, and hence are particularly suited for high-throughput screening and brain-wide neural circuit imaging. A number of behavioral assays have been developed to quantify neuropsychiatric phenomena in larval zebrafish. Here, we will review these assays and the current knowledge regarding the underlying mechanisms of their behavioral readouts. We will also discuss the existing evidence linking larval zebrafish behavior to specific human behavioral traits and how the endophenotype framework can be applied. Importantly, many of the endophenotypes we review do not solely define a diseased state but could manifest as a spectrum across the general population. As such, we make the case for larval zebrafish as a promising model for extending our understanding of population mental health, and for identifying novel therapeutics and interventions with broad impact.

Psychological Distress During Pregnancy and the Development of Pregnancy-Induced Hypertension: A Prospective Study

OBJECTIVE

Pregnancy-induced hypertension (PIH) is associated with serious complications in both the mother and the unborn child. We examined the possible association between trajectories of maternal psychological distress symptoms and PIH separately in primiparous and multiparous women.

METHODS

Pregnancy-specific negative affect (P-NA) and depressive symptoms were assessed prospectively at each trimester using the Tilburg Pregnancy Distress Scale pregnancy negative affect subscale (P-NA) and the Edinburgh Depression Scale (EDS). Data on PIH were collected from medical records. Growth mixture modeling analysis was used to identify trajectories of P-NA and EDS. The independent role of P-NA and EDS symptom trajectories on developing PIH was examined using multivariate logistic regression models.

RESULTS

One hundred (7.6%) women developed PIH and were compared with 1219 women without hypertension or other complications during pregnancy. Three P-NA trajectories were identified: low stable (reference group; 90%), decreasing (5.2%), and increasing (4.8%). The latter two classes showed persistently and significantly higher P-NA symptoms during pregnancy compared with the reference group. In multiparous women, high P-NA scores (belonging to classes 2 and 3) were related to PIH (odds ratio [OR] = 6.91, 95% confidence interval [CI] = 2.26-21.2), independent of body mass index (OR = 1.17, 95% CI = 1.06-1.27) and previous PIH (OR = 14.82, 95% CI = 6.01-32.7). No associations between P-NA and PIH were found in primiparous women. EDS trajectories were not related to PIH in both primiparous and multiparous women.

CONCLUSIONS

In multiparous women, persistently high levels of P-NA symptoms but not depressive symptoms were independently associated with development of PIH.

Metabolomic and inflammatory signatures of symptom dimensions in major depression

BACKGROUND

Major depressive disorder (MDD) is a highly heterogenous disease, both in terms of clinical profiles and pathobiological alterations. Recently, immunometabolic dysregulations were shown to be correlated with atypical, energy-related symptoms but less so with the Melancholic or Anxious distress symptom dimensions of depression in The Netherlands Study of Depression and Anxiety (NESDA) study. In this study, we aimed to replicate these immunometabolic associations and to characterize the metabolomic correlates of each of the three MDD dimensions.

METHODS

Using three clinical rating scales, Melancholic, and Anxious distress, and Immunometabolic (IMD) dimensions were characterized in 158 patients who participated in the Predictors of Remission to Individual and Combined Treatments (PReDICT) study and from whom plasma and serum samples were available. The NESDA-defined inflammatory index, a composite measure of interleukin-6 and C-reactive protein, was measured from pre-treatment plasma samples and a metabolomic profile was defined using serum samples analyzed on three metabolomics platforms targeting fatty acids and complex lipids, amino acids, acylcarnitines, and gut microbiome-derived metabolites among other metabolites of central metabolism.

RESULTS

The IMD clinical dimension and the inflammatory index were positively correlated (r = 0.19, p = 0.019) after controlling for age, sex, and body mass index, whereas the Melancholic and Anxious distress dimensions were not, replicating the previous NESDA findings. The three symptom dimensions had distinct metabolomic signatures using both univariate and set enrichment statistics. IMD severity correlated mainly with gut-derived metabolites and a few acylcarnitines and long chain saturated free fatty acids. Melancholia severity was significantly correlated with several phosphatidylcholines, primarily the ether-linked variety, lysophosphatidylcholines, as well as several amino acids. Anxious distress severity correlated with several medium and long chain free fatty acids, both saturated and polyunsaturated ones, sphingomyelins, as well as several amino acids and bile acids.

CONCLUSION

The IMD dimension of depression appears reliably associated with markers of inflammation. Metabolomics provides powerful tools to inform about depression heterogeneity and molecular mechanisms related to clinical dimensions in MDD, which include a link to gut microbiome and lipids implicated in membrane structure and function.

Hearing Therapy Improves Tinnitus-Related Distress in Mildly Distressed Patients with Chronic Tinnitus and Mild-to-Moderate Hearing Loss: A Randomized-Controlled Cross-Over Design

BACKGROUND

The psychological effects of hearing aids and auditory training are underinvestigated.

OBJECTIVE

To assess the short- and long-term effects of an industry-developed auditory training on tinnitus-related distress, perceived stress, and psychological epiphenomena in patients with chronic tinnitus and mild-to-moderate hearing loss.

METHOD

One-hundred-seventy-seven gender-stratified patients were randomized to an immediate [IIG] or delayed [DIG] intervention group. Following binaural hearing aid fitting, participants completed a CD-enhanced 14-days self-study program. Applying a randomized-controlled cross-over design, psychological measures were obtained at four times: pre-treatment/wait [IIG: t₁; DIG: wait], post-treatment/pre-treatment [IIG: t₂; DIG: t₁], follow-up/post-treatment [IIG: t₃; DIG: t₂], and follow-up [DIG: t₃]. Between- and within-group analyses investigated treatment-related effects and their stability at a 70-day follow-up.

RESULTS

Overall, distress symptom severity was mild. Unlike the DIG, the IIG showed significant improvements in tinnitus-related distress. Some psychological epiphenomena, notably anxiety, slightly improved in both groups. Within-group analyses demonstrated the stability of the tinnitus-distress-related effects, alongside uncontrolled improvements of perceived stress and mood-related symptoms at follow-up.

CONCLUSIONS

The investigated hearing therapy lastingly improves tinnitus-related distress in mildly distressed patients with chronic tinnitus and mild-to-moderate hearing loss. Beneficial psychological knock-on effects deserve further investigation.

Refining our understanding of depressive states and state transitions in response to cognitive behavioural therapy using latent Markov modelling

BACKGROUND

It is increasingly recognized that existing diagnostic approaches do not capture the underlying heterogeneity and complexity of psychiatric disorders such as depression. This study uses a data-driven approach to define fluid depressive states and explore how patients transition between these states in response to cognitive behavioural therapy (CBT).

METHODS

Item-level Patient Health Questionnaire (PHQ-9) data were collected from 9891 patients with a diagnosis of depression, at each CBT treatment session. Latent Markov modelling was used on these data to define depressive states and explore transition probabilities between states. Clinical outcomes and patient demographics were compared between patients starting at different depressive states.

RESULTS

A model with seven depressive states emerged as the best compromise between optimal fit and interpretability. States loading preferentially on cognitive/affective v. somatic symptoms of depression were identified. Analysis of transition probabilities revealed that patients in cognitive/affective states do not typically transition towards somatic states and vice-versa. Post-hoc analyses also showed that patients who start in a somatic depressive state are less likely to engage with or improve with therapy. These patients are also more likely to be female, suffer from a comorbid long-term physical condition and be taking psychotropic medication.

CONCLUSIONS

This study presents a novel approach for depression sub-typing, defining fluid depressive states and exploring transitions between states in response to CBT. Understanding how different symptom profiles respond to therapy will inform the development and delivery of stratified treatment protocols, improving clinical outcomes and cost-effectiveness of psychological therapies for patients with depression.

Incident Major Depressive Disorder Predicted by Three Measures of Insulin Resistance: A Dutch Cohort Study

OBJECTIVE

Major depressive disorder is the leading cause of disability worldwide. Yet, there remain significant challenges in predicting new cases of major depression and devising strategies to prevent the disorder. An important first step in this process is identifying risk factors for the incidence of major depression. There is accumulating biological evidence linking insulin resistance, another highly prevalent condition, and depressive disorders. The objectives of this study were to examine whether three surrogate measures of insulin resistance (high triglyceride-HDL [high-density lipoprotein] ratio; prediabetes, as indicated by fasting plasma glucose level; and high central adiposity, as measured by waist circumference) at the time of study enrollment were associated with an increased rate of incident major depressive disorder over a 9-year follow-up period and to assess whether the new onset of these surrogate measures during the first 2 years after study enrollment was predictive of incident major depressive disorder during the subsequent follow-up period.

METHODS

The Netherlands Study of Depression and Anxiety (NESDA) is a multisite longitudinal study of the course and consequences of depressive and anxiety disorders in adults. The study population comprised 601 NESDA participants (18-65 years old) without a lifetime history of depression or anxiety disorders. The study's outcome was incident major depressive disorder, defined using DSM-IV criteria. Exposure measures included triglyceride-HDL ratio, fasting plasma glucose level, and waist circumference.

RESULTS

Fourteen percent of the sample developed major depressive disorder during follow-up. Cox proportional hazards models indicated that higher triglyceride-HDL ratio was positively associated with an increased risk for incident major depression (hazard ratio=1.89, 95% CI=1.15, 3.11), as were higher fasting plasma glucose levels (hazard ratio=1.37, 95% CI=1.05, 1.77) and higher waist circumference (hazard ratio=1.11 95% CI=1.01, 1.21). The development of prediabetes in the 2-year period after study enrollment was positively associated with incident major depressive disorder (hazard ratio=2.66, 95% CI=1.13, 6.27). The development of high triglyceride-HDL ratio and high central adiposity (cut-point ≥100 cm) in the same period was not associated with incident major depression.

CONCLUSIONS

Three surrogate measures of insulin resistance positively predicted incident major depressive disorder in a 9-year follow-up period among adults with no history of depression or anxiety disorder. In addition, the development of prediabetes between enrollment and the 2-year study visit was positively associated with incident major depressive disorder. These findings may have utility for evaluating the risk for the development of major depression among patients with insulin resistance or metabolic pathology.

Is symptom connectivity really the most important issue in depression? Depression as a dynamic system of interconnected symptoms revisited

According to the network theory strong associations between symptoms drive the disease process. We compared those with and without diagnosed depressive disorders (DD+/DD-) and analysed the effects of differences in (a) network connectivity, (b) symptom thresholds, and (c) autoregressive loops (i.e. how strongly specific symptoms predict themselves) on the potential activation of symptoms over time using simulations developed by Cramer and others (2016). The parameters for the simulation (symptom connectivity and symptom threshold) were obtained from Ising models and cross-lagged panel network analyses. Data were from the nationally representative samples (Health 2000-2011 Study) of 4190 participants measured in 2011 (cross-sectional analyses) and 3201 participants measured in 2000 and 2011 (longitudinal analyses). DD diagnosis was based on the Composite International Diagnostic Interview and depressive symptoms were self-reported using the 13-item version of the Beck Depression Inventory (BDI). Differences in symptom connectivity between participants with and without DD were not observed, but the mean probability (threshold) of symptom existence in the DD + group was higher than in the DD-group (0.41 vs. 0.12). Simulation showed that there are more active symptoms in the DD + group after 10 000 time points (means 1.2 vs. 4.6) than in the DD-group. This difference largely disappeared when we used longitudinal networks, including autoregressive loops, in the connectivity matrix. Our results suggest that the differences in symptom thresholds and autoregressive loops may be more important features than symptom connectivity in differentiating people with and without DD.

Health-Related Quality of Life, Depressive Symptoms, Anxiety, and Somatization Symptoms in Male and Female Patients with Chronic Tinnitus

Objective: To investigate the joint impact of tinnitus-related distress (TRD), anxiety, depressive symptoms, and other somatization symptoms on health-related quality of life (HRQoL) in female vs. male patients with chronic tinnitus. Method: Three-hundred-and-fifty-two patients with chronic tinnitus completed audiological testing and a psychological assessment battery that comprised—among other measures—German versions of the Tinnitus Questionnaire, Hospital Anxiety and Depression Scale, Somatic Symptom Scale-8, and Health-Related Quality of Life scale. Descriptive analyses examined associations as well as within- and between-gender differences of the measured variables. Gender-specific serial indirect effects analyses aimed to explain the impact of TRD on HRQoL through psychological processes, notably anxiety, depressive symptoms, and somatization symptoms. Results: Both female and male patients yielded lower mental than physical HRQoL and negative associations between the measured psychological variables and HRQoL. Compared to male patients, female patients reported higher levels of tinnitus-related- and wider psychological distress, other somatization symptoms (e.g., headaches), and impairments in mental and physical HRQoL. For each gender, depressive symptoms, anxiety, and somatization symptoms fully mediated the effect of TRD on mental and physical HRQoL. A double-dissociation revealed an interaction of somatization symptoms and depression on the TRD-HRQoL association in women, and of somatization symptoms and anxiety in men. Conclusions: In patients with chronic tinnitus, psychological constructs account for reported impairments in both mental and physical HRQoL. To improve patients’ HRQoL, treatment conceptualizations should consider gender-specific psychological expressions of low mood or anxiety.

Urinary Metabolite Signatures for Predicting Elderly Stroke Survivors with Depression

BACKGROUND

Post-stroke depression (PSD) is a major complication in stroke survivors, especially in elderly stroke survivors. But there are still no objective methods to diagnose depression in elderly stroke survivors. Thus, this study was conducted to identify potential biomarkers for diagnosing elderly PSD subjects.

METHODS

Elderly (60 years or older) stroke survivors with depression were assigned into the PSD group, and elderly stroke survivors without depression and elderly healthy controls (HCs) were assigned into the non-depressed group. Urinary metabolite signatures obtained from gas chromatography-mass spectrometry (GC-MS)-based metabolomic platform were collected. Both univariate and multivariate statistical analysis were used to find the differential urinary metabolites between the two groups.

RESULTS

The 78 elderly HCs, 122 elderly stroke survivors without depression and 124 elderly PSD subjects were included. A set of 13 differential urinary metabolites responsible for distinguishing PSD subjects from non-depressed subjects were found. The Phenylalanine, tyrosine and tryptophan biosynthesis, Phenylalanine metabolism and Galactose metabolism were found to be significantly changed in elderly PSD subjects. The phenylalanine was significantly negatively correlated with age and depressive symptoms. Meanwhile, a biomarker panel consisting of 3-hydroxyphenylacetic acid, tyrosine, phenylalanine, sucrose, palmitic acid, glyceric acid, azelaic acid and α-aminobutyric acid was identified.

CONCLUSION

These results provided candidate molecules for developing objective methods to diagnose depression in elderly stroke survivors, suggested that taking supplements of phenylalanine might be an effective method to prevent depression in elderly stroke survivors, and would be helpful for future revealing the pathophysiological mechanism of PSD.

Somatic and anxiety symptoms of depression are associated with disability in late life depression

Objectives: To assess the relationships of somatic and anxiety symptoms of depression with functional disability in a sample of older adults with late life depression.Method: Data were analyzed from 78 older adults aged 65-88 with current major depression. Somatic and anxiety symptoms from the 24-item Hamilton Depression Rating Scale (HDRS) were summed to create variables measuring severity of these symptoms. Other symptoms of depression were also assessed using the remaining items of the HDRS. Current physical health burden was assessed using the Functional Comorbidity Index (FCI). Disability was measured with the Late Life Function and Disability Instrument (LLFDI) total limitation score. A linear regression analysis was performed to assess the association of somatic and anxiety symptoms with disability independent of other factors.Results: The model accounted for 26.6% of variance in disability, (F(6,51) = 3.1, p = .01). Somatic (B = -1.9, p = .004) and anxiety (B = -3.7, p = .04) symptoms of depression were significantly associated with disability. Other depressive symptoms and physical illness burden were not associated with disability.Discussion: In older adults with major depression, somatic and anxiety symptoms of depression are associated with disability. Identification and treatment to remission of these symptoms may improve functional outcomes among older depressed adults.

Depression Subtypes in Individuals With or at Risk for Symptomatic Knee Osteoarthritis

OBJECTIVE

The present study was undertaken to identify depression subtypes in individuals with or at risk for symptomatic knee osteoarthritis (OA) and to evaluate differences in pain and disability trajectories between groups.

METHODS

Participants (n = 4,486) were enrolled in the Osteoarthritis Initiative. Latent class analysis was applied to the 20-item Center for Epidemiologic Studies Depression Scale measured at baseline to identify groups with similar patterns of depressive symptoms, and subtypes were assigned using posterior probability estimates. The relationships between depression subtypes and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain and disability subscales were modeled over 4 years and stratified by baseline knee OA status (symptomatic [n = 1,626] or at risk [n = 2,860]).

RESULTS

Four subtypes were identified: asymptomatic (80.6%), catatonic (5.3%), anhedonic (10.6%), and melancholic (3.5%). Catatonic and anhedonic subtypes were differentiated by symptoms corresponding to psychomotor agitation and the inability to experience pleasure, respectively. The melancholic subtype expressed symptoms related to reduced energy and movement, anhedonia, and other somatic symptoms. Detectable mean differences in pain and disability compared to the asymptomatic group were observed for the anhedonic (1.5-2.3 WOMAC units) and melancholic (4.8-6.6 WOMAC units) subtypes, and associations were generally larger in individuals with symptomatic knee OA relative to those at risk.

CONCLUSION

Among individuals with or at risk for symptomatic knee OA, there is evidence of depression subtypes characterized by distinct clusters of depressive symptoms that have differential effects on reports of pain and disability over time. Our findings thus imply that depression interventions could be optimized by targeting the specific symptomology that these subtypes exhibit.

Identification of Potential Metabolite Markers for Middle-Aged Patients with Post-Stroke Depression Using Urine Metabolomics

BACKGROUND

Post-stroke depression (PSD) is one of the most common complications in stroke survivors. But, there are still no objective methods to diagnose PSD. This study aims to identify potential biomarkers for diagnosing PSD in middle-aged stroke survivors.

METHODS

Middle-aged subjects aged 30 to 59 years (92 PSD patients and 89 stroke survivors without depression) were included in this study. Urinary metabolites were detected by gas chromatography-mass spectrometry (GC-MS). Differential urinary metabolites and potential biomarkers were screened by applying statistical analysis.

RESULTS

The different urinary metabolic phenotypes between PSD patients and stroke survivors without depression were identified. A total of 12 differential urinary metabolites were accurately identified by using orthogonal partial least-squares-discriminant analysis. After analyzing those 12 differential urinary metabolites by step-wise logistic regression analysis, only seven metabolites (palmitic acid, hydroxylamine, myristic acid, glyceric acid, lactic acid, tyrosine and azelaic acid) were finally selected as potential biomarkers for diagnosing PSD in middle-aged stroke survivors. A panel consisting of these potential biomarkers could effectively diagnose middle-aged PSD patients.

CONCLUSION

Urinary metabolic profiles were different between middle-aged PSD patients and stroke survivors without depression. Our results would be helpful in future for developing an objective method to diagnose PSD in middle-aged stroke survivors.

Network structure of depression symptomology in participants with and without depressive disorder: the population-based Health 2000-2011 study

PURPOSE

Putative causal relations among depressive symptoms in forms of network structures have been of recent interest, with prior studies suggesting that high connectivity of the symptom network may drive the disease process. We examined in detail the network structure of depressive symptoms among participants with and without depressive disorders (DD; consisting of major depressive disorder (MDD) and dysthymia) at two time points.

METHODS

Participants were from the nationally representative Health 2000 and Health 2011 surveys. In 2000 and 2011, there were 5998 healthy participants (DD-) and 595 participants with DD diagnosis (DD+). Depressive symptoms were measured using the 13-item version of the Beck Depression Inventory (BDI). Fused Graphical Lasso was used to estimate network structures, and mixed graphical models were used to assess network connectivity and symptom centrality. Network community structure was examined using the walktrap-algorithm and minimum spanning trees (MST). Symptom centrality was evaluated with expected influence and participation coefficients.

RESULTS

Overall connectivity did not differ between networks from participants with and without DD, but more simple community structure was observed among those with DD compared to those without DD. Exploratory analyses revealed small differences between the samples in the order of one centrality estimate participation coefficient.

CONCLUSIONS

Community structure, but not overall connectivity of the symptom network, may be different for people with DD compared to people without DD. This difference may be of importance when estimating the overall connectivity differences between groups with and without mental disorders.

Biomarker screening for antenatal depression in women who underwent caesarean section: a matched observational study with plasma Lipidomics

BACKGROUND

Antenatal depression is a prevalent mental disorder in women who have undergone caesarean section, and it often presages adverse postoperative outcomes. Because of the lack of a laboratory-based diagnostic strategy, antenatal depression is mainly determined by a psychologist's subjective judgment based on a structured clinical interview for established diagnostic criteria. However, the diagnostic accuracy rate for depression by non-psychiatrists is relatively low. Thus, this study aimed to use lipidomics to identify potential biomarkers related to antenatal depression in women who have undergone caesarean section.

METHODS

The study was designed as a matched prospective observational study. Singleton pregnant women scheduled to receive elective caesarean section, were screened for eligibility. Women diagnosed with major antenatal depression were matched with non-antenatal depression controls in terms of age (±1 year) and BMI (±1 kg/m²), and blood samples of the included matched pairs were collected. Subsequently, lipidomics of the plasma samples were performed using Ultra Performance Liquid Chromatography-mass spectrometry analysis to explore the differentially expressed lipids in women with or without antenatal depression.

RESULTS

In total, 484 pregnant women were screened; 66 subjects were recruited, including 33 subjects with major antenatal depression and 33 matched controls without antenatal depression. Thirty-five differentially expressed lipid metabolites were identified (P < 0.05). The area under the receiver operating characteristic curve of these lipid metabolites was 0.7 or larger; the area under curve for cholesterol sulfate was 0.823 (95% CI: 0.716-0.930), and that of PC (18:2 (2E, 4E)/0:0) was 0.778 (95%CI: 0.662-0.895). In the conditional logistic stepwise regression analysis, cholesterol sulfate (P = 0.009) and PC (18:2 (2E, 4E)/0:0) (P = 0.035) were also identified as effective predictive risk factors for antenatal depression.

CONCLUSIONS

Women who had undergone caesarean section and experienced antenatal depression presented a significantly differentially expressed profile of plasma lipidomics compared to those who did not experience antenatal depression. Cholesterol sulfate and PC (18:2 (2E, 4E)/0:0) may be effective and specific lipidic biomarkers for the prediction of antenatal depression.

TRIAL REGISTRATION

China Clinical Trial Registration Center registration number: ChiCTR1800016230 ; date of registration: 21/05/2018.

Impact of transcranial magnetic stimulation on the symptom profile of major depressive episode

OBJECTIVES

This study aimed to explore the effects of a four-week course of transcranial magnetic stimulation (TMS) on the following symptoms of major depressive episode (MDE): mood, work activities, health concerns, guilt, anxiety and retardation.

METHOD

Patients underwent 20 daily sessions of 10 Hz TMS (two sets of 10 daily treatments separated by two days of rest). The six-item Hamilton Depression Rating Scale (HAMD-6) was administered before and after treatment. Remission was operationalised as a HAMD-6 score of <4. Descriptive statistics and t-tests were used to compare pre/post scores on HAMD-6 subscales, and logistic regression was used to understand symptoms that predicted remission/non-remission.

RESULTS

There were 104 participants (79 female; 76%), with a mean age of 44.6 years (SD=15.7 years). There was a significant improvement in the whole sample and in remitters (n=70) on all subscales. However, those who failed to remit did not achieve significant reductions in 'health concerns' and 'retardation'. There were no difference in age and sex between remitters and non-remitters. Also, there were no significant differences between the remitters and non-remitters on the pretreatment depression symptom profiles. No predictors of response were identified, as expected.

CONCLUSIONS

TMS has the ability to reduce all listed MDE symptoms. No pretreatment MDE symptom profile was identified which might carry prognostic value.

Different trajectories of depressive symptoms during pregnancy

BACKGROUND

Up to 10-15% of women experience high levels of depressive symptoms during pregnancy. Since these levels of symptoms can vary greatly over time, the current study investigated the existence of possible longitudinal trajectories of depressive symptoms during pregnancy, and aimed to identify factors associated with these trajectories.

METHODS

Depressive symptoms were assessed prospectively at each trimester in 1832 women, using the Edinburgh (Postnatal) Depression Scale (E(P)DS). Growth mixture modeling was used to identify trajectories of depressive symptoms during pregnancy.

RESULTS

Three trajectories of depressive symptoms (E(P)DS scores) were identified: low stable (class 1, reference group, 83%), decreasing (class 2, 7%), and increasing (class 3, 10%). Classes 2 and 3 had significantly higher mean E(P)DS scores (7-13 throughout pregnancy) compared to the reference group (stable; E(P)DS <4). Factors associated with trajectories 2 and 3 included previous depressive episodes, life events during pregnancy, and unplanned pregnancy. Notably, the only factor distinguishing classes 2 and 3 was the perception of partner involvement experienced by women during their pregnancies. Class 2 (with decreasing E(P)DS scores) reported high partner involvement, while class 3 (with increasing E(P)DS scores) reported poor partner involvement throughout pregnancy.

LIMITATIONS

Depressive symptoms were assessed by self-report rather than a diagnostic interview. The participants were more often both highly educated and of Caucasian ethnicity compared to the general Dutch population.

CONCLUSIONS

Poor partner involvement was associated with increasing depressive symptoms during pregnancy. Health professionals should focus on partner involvement during pregnancy in order to identify women who are potentially vulnerable for perinatal depression.

Ablation of glucocorticoid receptor in the hindbrain of the mouse provides a novel model to investigate stress disorders

The hypothalamic-pituitary-adrenal (HPA) axis regulates responses to internal and external stressors. Many patients diagnosed with conditions such as depression or anxiety also have hyperactivity of the HPA axis. Hyper-stimulation of the HPA axis results in sustained elevated levels of glucocorticoids which impair neuronal function and can ultimately result in a psychiatric disorder. Studies investigating Glucocorticoid Receptor (GR/NR3C1) in the brain have primarily focused on the forebrain, however in recent years, the hindbrain has become a region of interest for research into the development of anxiety and depression, though the role of GR signalling in the hindbrain remains poorly characterised. To determine the role of glucocorticoid signalling in the hindbrain we have developed a novel mouse model that specifically ablates hindbrain GR to ascertain its role in behaviour, HPA-axis regulation and adrenal structure. Our study highlights that ablation of GR in the hindbrain results in excessive barbering, obsessive compulsive digging and lack of cage exploration. These mice also develop kyphosis, elevated circulating corticosterone and severe adrenal cortex disruption. Together, this data demonstrates a role for hindbrain GR signalling in regulating stress-related behaviour and identifies a novel mouse model to allow further investigation into the pathways impacting stress and anxiety.

Association networks in a matched case-control design - Co-occurrence patterns of preexisting chronic medical conditions in patients with major depression versus their matched controls

OBJECTIVE

We present a method for comparing association networks in a matched case-control design, which provides a high-level comparison of co-occurrence patterns of features after adjusting for confounding factors. We demonstrate this approach by examining the differential distribution of chronic medical conditions in patients with major depressive disorder (MDD) compared to the distribution of these conditions in their matched controls.

MATERIALS AND METHODS

Newly diagnosed MDD patients were matched to controls based on their demographic characteristics, socioeconomic status, place of residence, and healthcare service utilization in the Korean National Health Insurance Service's National Sample Cohort. Differences in the networks of chronic medical conditions in newly diagnosed MDD cases treated with antidepressants, and their matched controls, were prioritized with a permutation test accounting for the false discovery rate. Sensitivity analyses for the associations between prioritized pairs of chronic medical conditions and new MDD diagnosis were performed with regression modeling.

RESULTS

By comparing the association networks of chronic medical conditions in newly diagnosed depression patients and their matched controls, five pairs of such conditions were prioritized among 105 possible pairs after controlling the false discovery rate at 5%. In sensitivity analyses using regression modeling, four out of the five prioritized pairs were statistically significant for the interaction terms.

CONCLUSION

Association networks in a matched case-control design can provide a high-level comparison of comorbid features after adjusting for confounding factors, thereby supplementing traditional clinical study approaches. We demonstrate the differential co-occurrence pattern of chronic medical conditions in patients with MDD and prioritize the chronic conditions that have statistically significant interactions in regression models for depression.

Urinary biomarker panel for diagnosing patients with depression and anxiety disorders

Available data indicate that patients with depression and anxiety disorders are likely to be at greater risk for suicide. Therefore, it is important to correctly diagnose patients with depression and anxiety disorders. However, there are still no empirical laboratory methods to objectively diagnose these patients. In this study, the multiple metabolomics platforms were used to profile the urine samples from 32 healthy controls and 32 patients with depression and anxiety disorders for identifying differential metabolites and potential biomarkers. Then, 16 healthy controls and 16 patients with depression and anxiety disorders were used to independently validate the diagnostic performance of the identified biomarkers. Finally, a panel consisting of four biomarkers-N-methylnicotinamide, aminomalonic acid, azelaic acid and hippuric acid-was identified. This panel was capable of distinguishing patients with depression and anxiety disorders from healthy controls with an area under the receiver operating characteristic curve of 0.977 in the training set and 0.934 in the testing set. Meanwhile, we found that these identified differential metabolites were mainly involved in three metabolic pathways and five molecular and cellular functions. Our results could lay the groundwork for future developing a urine-based diagnostic method for patients with depression and anxiety disorders.

New insights into the correlation structure of DSM-IV depression symptoms in the general population v. subsamples of depressed individuals

AIMS

Previous research failed to uncover a replicable dimensional structure underlying the symptoms of depression. We aimed to examine two neglected methodological issues in this research: (a) adjusting symptom correlations for overall depression severity; and (b) analysing general population samples v. subsamples of currently depressed individuals.

METHODS

Using population-based cross-sectional and longitudinal data from two nations (Switzerland, 5883 young men; USA, 2174 young men and 2244 young women) we assessed the dimensions of the nine DSM-IV depression symptoms in young adults. In each general-population sample and each subsample of currently depressed participants, we conducted a standardised process of three analytical steps, based on exploratory and confirmatory factor and bifactor analysis, to reveal any replicable dimensional structure underlying symptom correlations while controlling for overall depression severity.

RESULTS

We found no evidence of a replicable dimensional structure across samples when adjusting symptom correlations for overall depression severity. In the general-population samples, symptoms correlated strongly and a single dimension of depression severity was revealed. Among depressed participants, symptom correlations were surprisingly weak and no replicable dimensions were identified, regardless of severity-adjustment.

CONCLUSIONS

First, caution is warranted when considering studies assessing dimensions of depression because general population-based studies and studies of depressed individuals generate different data that can lead to different conclusions. This problem likely generalises to other models based on the symptoms' inter-relationships such as network models. Second, whereas the overall severity aligns individuals on a continuum of disorder intensity that allows non-affected individuals to be distinguished from affected individuals, the clinical evaluation and treatment of depressed individuals should focus directly on each individual's symptom profile.

Annual Research Review: Suicide among youth - epidemiology, (potential) etiology, and treatment

BACKGROUND

Suicide is a leading cause of death and a complex clinical outcome. Here, we summarize the current state of research pertaining to suicidal thoughts and behaviors in youth. We review their definitions/measurement and phenomenology, epidemiology, potential etiological mechanisms, and psychological treatment and prevention efforts.

RESULTS

We identify key patterns and gaps in knowledge that should guide future work. Regarding epidemiology, the prevalence of suicidal thoughts and behaviors among youth varies across countries and sociodemographic populations. Despite this, studies are rarely conducted cross-nationally and do not uniformly account for high-risk populations. Regarding etiology, the majority of risk factors have been identified within the realm of environmental and psychological factors (notably negative affect-related processes), and most frequently using self-report measures. Little research has spanned across additional units of analyses including behavior, physiology, molecules, cells, and genes. Finally, there has been growing evidence in support of select psychotherapeutic treatment and prevention strategies, and preliminary evidence for technology-based interventions.

CONCLUSIONS

There is much work to be done to better understand suicidal thoughts and behaviors among youth. We strongly encourage future research to: (1) continue improving the conceptualization and operationalization of suicidal thoughts and behaviors; (2) improve etiological understanding by focusing on individual (preferably malleable) mechanisms; (3) improve etiological understanding also by integrating findings across multiple units of analyses and developing short-term prediction models; (4) demonstrate greater developmental sensitivity overall; and (5) account for diverse high-risk populations via sampling and reporting of sample characteristics. These serve as initial steps to improve the scientific approach, knowledge base, and ultimately prevention of suicidal thoughts and behaviors among youth.

Applying network analysis to psychological comorbidity and health behavior: Depression, PTSD, and sexual risk in sexual minority men with trauma histories

OBJECTIVE

High rates of depression and posttraumatic stress disorder (PTSD) contribute to sexual risk, particularly in men who have sex with men (MSM) who have experienced childhood sexual abuse. The comorbidity between depression and PTSD and mechanisms by which they contribute to sexual risk in MSM remain unclear. This study sought to demonstrate the feasibility and utility of a network approach to (a) characterize symptom interconnections between depression and PTSD in MSM, (b) identify specific symptoms related to sexual risk behavior, and (c) compare symptom networks across groups at different levels of risk.

METHOD

Cross-sectional baseline data were collected from 296 HIV-negative urban MSM as part of a multisite randomized intervention trial. Symptoms of depression and PTSD were self-reported along with sexual risk behavior. Analyses were performed in R using regularized partial correlation network modeling.

RESULTS

Network analyses revealed complex associations between depression and PTSD symptoms and in relation to sexual risk behavior. While symptoms clustered within their respective disorders, depression and PTSD were connected at key symptom nodes (e.g., sleep, concentration). Specific symptoms (e.g., avoiding thoughts and feelings) were linked to sexual risk behavior. Network comparisons across risk groups suggested avoidant processes could be more readily activated in higher-risk individuals, whereas hyperarousal symptoms may be more salient and protective for lower-risk individuals.

CONCLUSIONS

This study is one of the earliest network analyses of depression and PTSD, and first to extend this inquiry to health behavior. Symptom-level investigations may clarify mechanisms underlying psychological comorbidity and behavioral risk in MSM and refine targets for intervention/prevention. (PsycINFO Database Record

Roles of Inflammation and Depression in the Development of Gestational Diabetes

Inflammation, the body's response to harmful external agents, has long been found to be associated with depressive symptoms. The relationship between inflammation and depression is well established in the general population of people with depression, but is less so among perinatal women. Depression in the perinatal period is a common disorder, however available data do not indicate that there is a specific inflammatory picture associated with perinatal depression. We suggest that perinatal depression may be a heterogeneous construct, and that inflammation may be relevant to it in the context of other inflammatory morbidities of pregnancy. In this review we explore the available support for the hypothesis that inflammation associated with depression can represent a precipitating insult for the development of gestational diabetes, a known inflammatory morbidity of pregnancy.

A Risk Typology for Depression in African Americans

Studies report that African Americans are likely to experience severe, chronic, and disabling depressive symptoms leading to poor depression outcomes. There are several psychosocial and sociocultural factors that individually contribute to depression in African Americans (i.e. chronic stress, negative life events, negative social interaction, social support, and religiosity). However, African Americans are a heterogeneous population and the identification of subpopulations with distinct constellations of multiple co-occurring risk and protective factors may offer greater explanatory potential into depression burden for African Americans. This study used data from the American's Changing Lives Survey to identify risk types for depression in a national sample of African American adults and older adults (n = 1174). Latent class analysis results revealed a risk typology that included 3 distinct risk patterns for African Americans. Individuals in the High Protective/Low Risk Type (41%) had high levels of religiosity and social support. Those in the Moderate Protection/Low Risk Type (44%) had moderate levels of religiosity and social support. The Low Protection/Low Risk Type (15%) had low organizational and non-organizational religiosity and relatively low social support. All three types were marked by relatively low risk factors (i.e. negative events, financial stress, and negative social interaction). Multinomial logistic regression results suggested associations between demographic characteristics (i.e. gender, age, education, marital status), depressive symptoms, and risk type. Study results have implications for the development of depression interventions that are targeted to a specific risk type and tailored to the demographic profile of individuals likely to experience poor depression outcomes.

Dehydroepiandrosterone Sulfate Level Varies Nonlinearly with Symptom Severity in Major Depressive Disorder

Objective

The pathophysiology of major depressive disorder (MDD) is still not well understood. Conflicting results for surrogate.

biomarkers in MDD have been reported, which might be a consequence of the heterogeneity of MDD patients. Therefore, we aim to investigate how the severity of depression and various symptom domains are related to the levels of dehydroepiandrosterone sulfate (DHEA-s) in MDD patients.

Methods

We recruited 117 subjects from a general practice. Depressive symptoms were assessed using the Beck Depression Inventory (BDI). Depressive symptoms were divided into three subdomains according to BDI items; somatic symptoms, guilt and failure, and mood and inhibition.

Results

In subjects with very-mild-to-moderate depression, the DHEA-s level increased as BDI score did. However, the DHEA-s levels in the subjects with severe depression were significantly lower than in subjects with moderate depression (p=0.003). DHEA-s level was correlated with the BDI subscore for guilt and failure in very-mild-to-moderate depression (r=0.365, p=0.006).

Conclusion

The DHEA-s level appears to be indicative of MDD severity with respect to depressive symptoms, especially regarding guilt and failure. Our findings suggest that the upregulation of DHEA-s may be a part of a compensatory process in very-mild-to-moderate depression, and the failure of this compensation mechanism may underlie the development of severe depression.

Pre-ejection period reactivity to reward is associated with anhedonic symptoms of depression among adolescents

Pre-ejection period (PEP) reactivity to reward has been posited as a specific index of behavioral approach and incentive motivation, suggesting it might be uniquely associated with the affective and motivational deficits of anhedonia. This study evaluated PEP reactivity to a reward task as a predictor of depressive symptoms among adolescents, examining global depressive symptoms as well as specific anhedonic and nonanhedonic symptoms clusters. Participants included 76 adolescents, ages 11-15 years (52% female). This study found marginal support for an association between PEP reactivity to reward and concurrent anhedonia symptoms, but no association with nonanhedonic or the global scale. Findings are discussed in terms of potential associations between peripheral psychophysiological measures and dopaminergic functioning and also the utility of this measure for future research on anhedonia.

Changes in depression among older adults in China: A latent transition analysis

BACKGROUND

Depression in late life is an important public health problem in developing countries. It is timely to investigate stability and transition patterns of depressive symptom subtypes.

METHODS

Longitudinal data were used from the China Health and Retirement Longitudinal Study (CHARLS). A total of 853 women and 930 men aged 60-96 years were recruited. Latent class and latent transition analysis (LCA/LTA) were used to identify meaningful subgroups, transitions between those classes across time, and baseline demographic features that help to predict and design tailored interventions.

RESULTS

Three depression subgroups were identified: Class 1 was labeled "Mild Depression"; Class 2 was labeled "Severe Depression" and class 3 was labeled "Lack of Positive Affect". A predominant tendency for stability appeared rather than change, meanwhile individual in Mild Depression and Severe Depression latent status both had a high probability to convert to the Lack of Positive Affect latent status. Social activities played a significant role in buffering the effect of depression, while individuals with chronic diseases, having difficulty with ADLs and smoking might be at-risk groups.

LIMITATIONS

The limitations of the present study were inherent limitation in the LTA model and some small proportion of transitions.

CONCLUSIONS

This study demonstrated a transition pattern in older adult depression within a person-centered approach. Differential treatment effects were found across baseline depression class, suggesting the benefit for tailored intervention programs to improve depression outcomes among older adults.

A Novel Urinary Metabolite Signature for Non-invasive Post-stroke Depression Diagnosis

Post-stroke depression (PSD) is the most common psychiatric complication in stroke survivors that has been associated with increased physical disability, distress, poor rehabilitation, and suicidal ideation. However, there are still no biomarkers available to support objective laboratory testing for this disorder. Here, a GC-MS-based urinary metabolomics approach was used to characterize the urinary metabolic profiling of PSD (stroke) subjects and non-PSD (health controls) subjects in order to identify and validate urinary metabolite biomarkers for PSD. Six metabolites, azelaic acid, glyceric acid, pseudouridine, 5-hydroxyhexanoic acid, tyrosine, and phenylalanine, were defined as biomarkers. A combined panel of these six urinary metabolites could effectively discriminate between PSD subjects and non-PSD subjects, achieving an area under the receiver-operating characteristic curve (AUC) of 0.961 in a training set (n = 72 PSD subjects and n = 146 non-PSD subjects). Moreover, this urinary biomarker panel was capable of discriminating blinded test samples (n = 58 PSD patients and n = 109 non-PSD subjects) with an AUC of 0.954. These findings suggest that a urine-based laboratory test using these biomarkers may be useful in the diagnosis of PSD.

Blunted reward processing in remitted melancholic depression

Blunted reward response appears to be a trait-like marker of vulnerability for Major Depressive Disorder (MDD). As such, it should be present in remitted individuals; however, depression is a heterogeneous syndrome. Reward-related impairments may be more pronounced in individuals with melancholic depression. The present study examined neural responses to rewards in remitted melancholic depression (rMD; N = 29), remitted non-melancholic depression (rNMD; N = 56), and healthy controls (HC; N = 81). Event-related potentials to monetary gain and loss were recorded during a simple gambling paradigm. rMD was characterized by a blunted response to rewards relative to both the HC and the rNMD groups, who did not differ from one another. Moreover, the rMD and rNMD groups did not differ in course or severity of their past illnesses, or current depressive symptoms or functioning. Results suggest that blunted response to rewards may be a viable vulnerability marker for melancholic depression.

Substance Use and Depression Symptomatology: Measurement Invariance of the Beck Depression Inventory (BDI-II) among Non-Users and Frequent-Users of Alcohol, Nicotine and Cannabis

Depression is a highly heterogeneous condition, and identifying how symptoms present in various groups may greatly increase our understanding of its etiology. Importantly, Major Depressive Disorder is strongly linked with Substance Use Disorders, which may ameliorate or exacerbate specific depression symptoms. It is therefore quite plausible that depression may present with different symptom profiles depending on an individual’s substance use status. Given these observations, it is important to examine the underlying construct of depression in groups of substance users compared to non-users. In this study we use a non-clinical sample to examine the measurement structure of the Beck Depression Inventory (BDI-II) in non-users and frequent-users of various substances. Specifically, measurement invariance was examined across those who do vs. do not use alcohol, nicotine, and cannabis. Results indicate strict factorial invariance across non-users and frequent-users of alcohol and cannabis, and metric invariance across non-users and frequent-users of nicotine. This implies that the factor structure of the BDI-II is similar across all substance use groups

Life-space mobility and dimensions of depressive symptoms among community-dwelling older adults

OBJECTIVES

To examine the association between life-space mobility and different dimensions of depressive symptoms among older community-dwelling people.

METHODS

Cross-sectional analyses of baseline data of the 'Life-Space Mobility in Old Age' cohort study were carried out. The participants were community-dwelling women and men aged 75-90 years (N = 848). Data were gathered via structured interviews in participants' home. Life-space mobility (the University of Alabama at Birmingham (UAB) Life-Space Assessment - questionnaire) and depressive symptoms (Centre for Epidemiological Studies Depression Scale, CES-D) were assessed. Other factors examined included sociodemographic factors, difficulties walking 500 m, number of chronic diseases and the sense of autonomy in participation outdoors (subscale of Impact on Participation and Autonomy questionnaire).

RESULTS

Poorer life-space mobility was associated with higher prevalence of different dimensions of depressive symptoms. The associations were partially mediated through walking difficulties, health and the sense of autonomy in participation outdoor activities.

CONCLUSION

Poorer life-space mobility interrelates with higher probability for depressive symptoms, thus compromising older adults' mental wellbeing. A focus on older adults' life-space mobility may assist early identification of persons, who have elevated risk for depressive symptoms. The association between life-space mobility and depressive symptoms should be studied further utilizing longitudinal study designs to examine temporality and potential causality.

Mental State Decoding in Adolescent Boys with Major Depressive Disorder versus Sex-Matched Healthy Controls

BACKGROUND

Several adult depression studies have investigated mental state decoding, the basis for theory of mind, using the Reading the Mind in the Eyes Test. Findings have been mixed, but a comprehensive study found a greater severity of depression to be associated with poorer mental state decoding. Importantly, there has yet to be a similar study of adolescent depression. Converging evidence suggests that atypical mental state decoding may have particularly profound effects for psychosocial functioning among depressed adolescent boys.

SAMPLING AND METHODS

Adolescent boys with major depressive disorder (MDD, n = 33) and sex-matched healthy controls (HCs, n = 84) completed structured clinical interviews, self-report measures of psychopathology and the Child Eyes Test (CET).

RESULTS

The MDD group performed significantly better than HCs on the CET overall (p = 0.002), underscored by greater accuracy for negatively valenced items (p = 0.003). Group differences on items depicting positive (p = 0.129) and neutral mental states (p = 0.081) were nonsignificant.

CONCLUSIONS

Enhanced mental state decoding among depressed adolescent boys may play a role in the maintenance of and vulnerability to adolescent depression. Findings and implications are discussed. Limitations of this study include a reliance on self-report data for HC boys, as well as a lack of 'pure' depression among the boys with MDD.

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

BACKGROUND

In recent years, researchers have used various techniques to elucidate the heterogeneity in depressive symptoms. This study seeks to resolve the extent to which variations in depression reflect qualitative differences between symptom categories and/or quantitative differences in severity.

METHODS

Data were used from the Netherlands Mental Health Survey and Incidence Study-2, a nationally representative face-to-face survey of the adult general population. In a subsample of respondents with a lifetime key symptom of depression at baseline and who participated in the first two waves (n=1388), symptom profiles at baseline were based on symptoms reported during their worst lifetime depressive episode. Depressive symptoms and DSM-IV diagnoses were assessed with the Composite International Diagnostic Interview 3.0. Three latent variable techniques (latent class analysis, factor analysis, factor mixture modelling) were used to identify the best subtyping model.

RESULTS

A latent class analysis, adjusted for local dependence between weight change and appetite change, described the data best and resulted in four distinct depressive subtypes: severe depression with anxiety (28.0%), moderate depression with anxiety (29.3%), moderate depression without anxiety (23.6%) and mild depression (19.0%). These classes showed corresponding clinical correlates at baseline and corresponding course and outcome indicators at follow-up (i.e., class severity was linked to lifetime mental disorders at baseline, and service use for mental health problems and current disability at follow-up).

LIMITATIONS

Although the sample was representative of the population on most parameters, the findings are not generalisable to the most severely affected depressed patients.

CONCLUSIONS

Depression could best be described in terms of both qualitative differences between symptom categories and quantitative differences in severity. In particular anxiety was a distinguishing feature within moderate depression. This study stresses the central position anxiety occupies in the concept of depression.

An Objective Screening Method for Major Depressive Disorder Using Logistic Regression Analysis of Heart Rate Variability Data Obtained in a Mental Task Paradigm

BACKGROUND AND OBJECTIVES

Heart rate variability (HRV) has been intensively studied as a promising biological marker of major depressive disorder (MDD). Our previous study confirmed that autonomic activity and reactivity in depression revealed by HRV during rest and mental task (MT) conditions can be used as diagnostic measures and in clinical evaluation. In this study, logistic regression analysis (LRA) was utilized for the classification and prediction of MDD based on HRV data obtained in an MT paradigm.

METHODS

Power spectral analysis of HRV on R-R intervals before, during, and after an MT (random number generation) was performed in 44 drug-naïve patients with MDD and 47 healthy control subjects at Department of Psychiatry in Shizuoka Saiseikai General Hospital. Logit scores of LRA determined by HRV indices and heart rates discriminated patients with MDD from healthy subjects. The high frequency (HF) component of HRV and the ratio of the low frequency (LF) component to the HF component (LF/HF) correspond to parasympathetic and sympathovagal balance, respectively.

RESULTS

The LRA achieved a sensitivity and specificity of 80.0 and 79.0%, respectively, at an optimum cutoff logit score (0.28). Misclassifications occurred only when the logit score was close to the cutoff score. Logit scores also correlated significantly with subjective self-rating depression scale scores (p < 0.05).

CONCLUSION

HRV indices recorded during a MT may be an objective tool for screening patients with MDD in psychiatric practice. The proposed method appears promising for not only objective and rapid MDD screening but also evaluation of its severity.

Decomposing the heterogeneity of depression at the person-, symptom-, and time-level: latent variable models versus multimode principal component analysis

BACKGROUND

Heterogeneity of psychopathological concepts such as depression hampers progress in research and clinical practice. Latent Variable Models (LVMs) have been widely used to reduce this problem by identification of more homogeneous factors or subgroups. However, heterogeneity exists at multiple levels (persons, symptoms, time) and LVMs cannot capture all these levels and their interactions simultaneously, which leads to incomplete models. Our objective is to briefly review the most widely used LVMs in depression research, illustrating their use and incompatibility in real data, and to consider an alternative, statistical approach, namely multimode principal component analysis (MPCA).

METHODS

We applied LVMs to data from 147 patients, who filled out the Quick Inventory of Depressive Symptomatology (QIDS) at 9 time points. Compatibility of the results and suitability of the LVMs to capture the heterogeneity of the data were evaluated. Alternatively, MPCA was used to simultaneously decompose depression on the person-, symptom- and time-level and to investigate the interactions between these levels.

RESULTS

QIDS-data could be decomposed on the person-level (2 classes), symptom-level (2 factors) and time-level (2 trajectory-classes). However, these results could not be integrated into a single model. Instead, MPCA allowed for decomposition of the data at the person- (3 components), symptom- (2 components) and time-level (2 components) and for the investigation of these components' interactions.

CONCLUSIONS

Traditional LVMs have limited use when trying to define an integrated model of depression heterogeneity at the person, symptom and time level. More integrative statistical techniques such as MPCA can be used to address these relatively complex data patterns and could be used in future attempts to identify empirically-based subtypes/phenotypes of depression.

Subtypes of depression in cancer patients: an empirically driven approach

Purpose

This study aimed to (1) identify subgroups of cancer patients with distinct subtypes of depression before the start of psychological care, (2) examine whether socio-demographic and medical characteristics distinguished these subtypes, and (3) examine whether people with distinct subtypes reported differential courses of depression during psychological care.

Method

This naturalistic, longitudinal study included cancer patients who sought psychological care at specialized psycho-oncology institutions in the Netherlands. Data were collected before psychological care (T1) and three (T2) and nine (T3) months thereafter. Latent class analysis was performed to identify depression subtypes in 243 patients at T1.

Results

Before starting psychological care, three depressive subtypes were identified, differing in severity and type of symptoms. Class 1 (47 %) with mild depression reported mostly concentration and sleep problems and fatigue. Class 2 (41 %), with slightly higher levels of depression, reported similar concentration and sleep problems and fatigue as class 1, and additionally depressed mood. Class 3 (12 %), with severe depression, reported mainly a depressed mood and, to a lesser extent but still elevated, fatigue and concentration problems. None of socio-demographic and medical characteristics significantly distinguished these subtypes. These subtypes significantly predicted the course of depression over time, with class 1 reporting moderate improvements, class 2 large improvements, and class 3 the largest improvements.

Conclusions

Results indicate the presence of three subtypes of depression in cancer patients before starting psychological care. Our findings suggest that psychological interventions could be tailored to respond to the specific subtype of depression experienced by each individual.

Latent profile analyses of posttraumatic stress disorder, depression and generalized anxiety disorder symptoms in trauma-exposed soldiers

Posttraumatic stress disorder (PTSD) is comorbid with major depressive disorder (MDD; Kessler et al., 1995) and generalized anxiety disorder (GAD; Brown et al., 2001). We aimed to (1) assess discrete patterns of post-trauma PTSD-depression-GAD symptoms using latent profile analyses (LPAs), and (2) assess covariates (gender, income, education, age) in defining the best fitting class solution. The PTSD Checklist (assessing PTSD symptoms), GAD-7 scale (assessing GAD symptoms), and Patient Health Questionnaire-9 (assessing depression) were administered to 1266 trauma-exposed Ohio National Guard soldiers. Results indicated three discrete subgroups based on symptom patterns with mild (class 1), moderate (class 2) and severe (class 3) levels of symptomatology. Classes differed in symptom severity rather than symptom type. Income and education significantly predicted class 1 versus class 3 membership, and class 2 versus class 3. In conclusion, there is heterogeneity regarding severity of PTSD-depression-GAD symptomatology among trauma-exposed soldiers, with income and education predictive of class membership.

Urinary metabonomics for diagnosis of depression in hepatitis B virus-infected patients

BACKGROUND

Depression is concomitantly presented in Hepatitis B Virus (HBV)-infected patients and decreases these patients' quality of life. However, there are no laboratory-based methods to objectively diagnose this disorder.

OBJECTIVES

The aim of this study was to investigate the alteration of urinary metabolites in depressed HBV-infected patients.

PATIENTS AND METHODS

In this study, 81 depressed HBV-infected patients, 68 non-depressed HBV-infected patients and 64 Healthy Controls (HC) were recruited. A nuclear magnetic resonance (NMR)-based urinary metabonomic method was used to characterize the urinary metabolic profiling of depressed and non-depressed subjects.

RESULTS

Seventeen differential urinary metabolites responsible for discriminating depressed HBV-infected patients from non-depressed HBV-infected patients and HC were identified. Among these metabolites, pyruvate, isobutyrate, N-methylnicotinamide, α-hydroxybutyrate, acetoacetate and malonate were identified as potential biomarkers for diagnosing depression in HBV-infected patients. A combined panel of these potential biomarkers could effectively discriminate depressed HBV-infected patients from non-depressed HBV-infected patients and HC, with an average accuracy of 89.6% in the training set and a predictive accuracy of 86.4% in the test set.

CONCLUSIONS

These findings suggest that NMR-based urinary metabonomics approach might be a useful tool for the clinical diagnosis of depression in HBV-infected patients and the six potential biomarkers could be helpful for developing an objective diagnostic method. Limited by the number of recruited subjects, future studies are required to validate our conclusions.

The prospective association between obesity and major depression in the general population: does single or recurrent episode matter?

BACKGROUND

Obesity and major depressive disorder (MDD) are important public health problems. MDD is a heterogeneous disorder and the direction of its association with obesity remains unclear. Evidence grows that recurrent MDD (MDD-R) differs in etiology and prognosis from single episode MDD (MDD-S), which could affect associations with obesity. However, evidence on this differential effect is lacking. The aim of this study was to examine the direction of the association between obesity and MDD, single or recurrent episode.

METHODS

A longitudinal study was performed in a cohort of 1094 participants of the PREVEND study, on whom data were collected at baseline and at an average 2-year follow-up. MDD-S and MDD-R were assessed by the Composite International Diagnostic Interview (CIDI 2.1). Obesity was defined as Body Mass Index ≥ 30 kg/m(2). Binary logistic regression analyses were conducted to examine whether obesity predicts MDD-S/MDD-R or vice versa, adjusted for potential confounders.

RESULTS

Prospective analyses showed that BMI at baseline was associated with the onset of MDD-R (Odds ratio, OR = 1.32; 95% confidence interval, 95%CI: 1.11; 1.57) during 2-year follow-up, but not with the onset of MDD-S (OR = 0.98; 95%CI: 0.89; 1.07). Obesity at baseline was not associated with the onset of MDD-S during follow-up (OR = 0.75; 95%CI: 0.25; 2.30), but associated with the onset of MDD-R during follow-up (OR = 11.63; 95%CI: 1.05; 128.60). Neither MDD-S nor MDD-R were associated with the development of obesity during 2-year follow-up (OR = 1.67, 95%CI: 0.64; 4.29 and OR = 2.32, 95%CI: 0.82; 6.58, respectively).

CONCLUSIONS

Our findings add to the available evidence that obesity might specifically be associated with the onset of multiple episodes of major depression (MDD-R). Although the reverse association was not found, MDD-R tends to be also associated with subsequent development of obesity, but larger studies are needed to fully assess this issue. The heterogeneity of MDD should be considered when examining the effect of obesity on MDD.