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

A smartphone- and wearable-based biomarker for the estimation of unipolar depression severity

Drug development for mood disorders can greatly benefit from the development of robust, reliable, and objective biomarkers. The incorporation of smartphones and wearable devices in clinical trials provide a unique opportunity to monitor behavior in a non-invasive manner. The objective of this study is to identify the correlations between remotely monitored self-reported assessments and objectively measured activities with depression severity assessments often applied in clinical trials. 30 unipolar depressed patients and 29 age- and gender-matched healthy controls were enrolled in this study. Each participant's daily physiological, physical, and social activity were monitored using a smartphone-based application (CHDR MORE™) for 3 weeks continuously. Self-reported depression anxiety stress scale-21 (DASS-21) and positive and negative affect schedule (PANAS) were administered via smartphone weekly and daily respectively. The structured interview guide for the Hamilton depression scale and inventory of depressive symptomatology-clinical rated (SIGHD-IDSC) was administered in-clinic weekly. Nested cross-validated linear mixed-effects models were used to identify the correlation between the CHDR MORE™ features with the weekly in-clinic SIGHD-IDSC scores. The SIGHD-IDSC regression model demonstrated an explained variance (R2) of 0.80, and a Root Mean Square Error (RMSE) of ± 15 points. The SIGHD-IDSC total scores were positively correlated with the DASS and mean steps-per-minute, and negatively correlated with the travel duration. Unobtrusive, remotely monitored behavior and self-reported outcomes are correlated with depression severity. While these features cannot replace the SIGHD-IDSC for estimating depression severity, it can serve as a complementary approach for assessing depression and drug effects outside the clinic.

Profiles of depressive symptoms in Peru: An 8-year analysis in population-based surveys

Background Profiles of depressive symptoms have been described due to heterogeneity in symptomatology and presentation. In our study, we estimate depressive symptom profiles and relate these symptom profiles to risk factors in the Peruvian population. Methods We carried out an observational study based on the Peruvian Demographic and Health Survey (2014-2022). Men and women aged 15 years and older living in urban and rural areas in all regions of Peru were included. The Patient Health Questionnaire-9 was used to define depressive symptom profiles. We estimated latent class models to define the profiles and performed a Poisson regression analysis to determine the associated factors. Results A total of 259,655 participants were included. The three-class model was found to be the most appropriate, and the classes were defined according to the severity of depressive symptoms (moderate-severe symptoms, mild symptoms, and without depressive symptoms). Also, it was found that the three classes identified have not changed during the years of evaluations, presenting very similar prevalence over the years. In addition, women are more likely than men to belong to a class with more severe depressive symptoms; and the older the age, the higher the probability of belonging to a class with greater severity of depressive symptoms. Conclusions Our study found that at the population level in Peru, depressive symptoms are grouped into three classes according to the intensity of the symptomatology present (no symptoms, mild symptoms and moderate-severe symptoms).

Threat Neurocircuitry Predicts the Development of Anxiety and Depression Symptoms in a Longitudinal Study

BACKGROUND

Owing to high heterogeneity and comorbidity, the shared and unique neural mechanisms underlying the development of anxiety and major depressive disorders remain unclear. Using a dimensional model describing shared versus unique symptoms associated with anxiety and depression, this study investigated how longitudinal changes in symptom dimensions relate to threat neurocircuitry.

METHODS

Participants were 18- to 19-year-olds (N = 279, 186 females) who completed self-report measures of anxiety and depression at baseline and at 10, 20, and 30 months. Linear slopes of symptom dimensions of general distress, fear, and anhedonia-apprehension were estimated through a trilevel factorial model. In addition, functional magnetic resonance imaging scans were obtained while participants performed Pavlovian fear conditioning tasks at baseline and 30 months, including three phases of fear acquisition, extinction, and extinction recall. Neural responses in regions of interest related to threat neural circuitry (e.g., amygdala, ventromedial prefrontal cortex, and subgenual anterior cingulate cortex) were extracted.

RESULTS

Linear mixed models used to estimate relationships between changes of symptom dimensions and neural responses revealed two major findings: 1) greater neural responses to threatening stimuli during fear acquisition at baseline were associated with a greater increase in fear symptoms during the 30-month prospective period; and 2) elevated neural responses to the extinguished stimulus during extinction recall at 30 months were negatively associated with changes in general distress, suggesting that greater increases in general distress are associated with larger deficits in extinction memory.

CONCLUSIONS

These findings improve our understanding of pathophysiological pathways underlying the development of anxiety and depression, while separating symptom dimensions that are shared versus unique between the two disorders.

The role and mechanism of NLRP3 inflammasome-mediated astrocyte activation in dehydrocorydaline against CUMS-induced depression

Background: Depression is a common and potentially life-threatening mental illness, and currently, there is a lack of effective treatment. It has been reported that dehydrocorydaline (DHC) can inhibit monoamine transporter uptake in depressed CUMS mice, but more possible mechanisms of action remain to be further studied. Methods: C57BL/6 mice were exposed to chronic unpredictable mild stress (CUMS) for five consecutive weeks. The mice were administrated with dehydrocorydaline or fluoxetine (FLU) for four consecutive weeks. Behavioral tests including sucrose preference test (SPT), tail suspension test (TST), and forced swimming test (FST) were applied. In parallel, hematoxylin and eosin (H&E) staining and Nissl staining were used to explore the effect of DHC on pathological changes in the hippocampus. The concentrations of depression-related factors (5-HT and DA) and inflammatory factors (TNF-α, IL-6, and IL-1β) in the hippocampus and serum were assessed by ELISA assay. NLRP3 inflammasome pathway-related proteins (NLRP3, IL-18, IL-1 IL-1α, and caspase-1) were detected by western blot. The activation of microglia and astrocytes was subjected to immunofluorescent staining. Additionally, microglia were treated with DHC (100 mg/L) for 24 h following incubation with 100 ng/ml LPS for 12 h. ov-NC or ov-NLRP3 plasmid was transfected into microglia 6 h before LPS induction for exploring the effect of NLRP3 overexpression on DHC-inhibited microglia activation. Then, conditioned media of microglia were collected from each group, followed by intervention of astrocytes for 24 h to explore the effect of NLRP3 overexpression of microglia on astrocyte activation. Results: In vivo administration of DHC was found to ameliorate depressive-like behaviors and attenuate neuron damage of CUMS mice. DHC increased neurotransmitter concentration, reduced the proinflammatory factor levels, attenuated NLRP3 inflammasome activation, and decreased A1 astrocyte and microglia activation in the hippocampus of CUMS mice. Furthermore, in vivo results showed that activated microglia induced activation of A1 astrocytes but not A2 astrocytes. Conclusion: Taken together, we provided evidence that DHC exhibited antidepressive effects on CUMS mice possibly via NLRP3 inflammasome-mediated astrocyte activation.

Antidepressant-like effects of Rehmannioside A on rats induced by chronic unpredictable mild stress through inhibition of endoplasmic reticulum stress and apoptosis of hippocampus

Depression is one of the most prevalent psychiatric mood diseases worldwide, whose therapy is in urgent need of development. Although the neuroprotective effects of Rehmannioside A (Rea) have been demonstrated, its anti-depressive effect remains unclear. Here, a depression model was induced with chronic unpredictable mild stress (CUMS) in rats. The behavioral trails, including sucrose preference test, forced swim test and open field test were used to determine the success of the CUMS-induced model. The effect of Rea on the neuronal protection, apoptosis and endoplasmic reticulum stress (ERS) was evaluated by HE, NISSL, IF and TUNEL staining, and western blot assays. Mechanically, the MAPK signaling pathway-related proteins expressions were examined by western blot. The results showed that CUMS stimulation evoked a prominent reduction of rat body weight, sucrose preference, and numbers of crossing, rearing and grooming with the enhanced immobility times. Besides, CUMS exposure induced the nuclear shrinkage and damage, as well as the decreased ISSL⁺ numbers. Moreover, CUMS stimulation increased the relative protein expressions of Bax and Cleaved caspase-3 and the percent of TUNEL positive cells, and decreased the relative protein expressions of Bcl-2. Furthermore, CUMS exposure also increased the relative protein expression of GRP-78, XBP-1, ATF-6, ATF-4 and CHOP. However, the CUMS-induced changes of all these indicators were reversed with Rea introduction in a dose-dependent fashion. Mechanically, Rea supply observably antagonized the CUMS-induced the relative protein levels of p-p38/p-38, p-ERK1/2/ERK1/2 and p-JNK/JNK. Therefore, Rea attenuated depression through suppressing ERS and apoptosis in hippocampus of CUMS-induced rats involved in MAPK signaling.

Association Between Dietary Patterns and Plasma Lipid Biomarker and Female Breast Cancer Risk: Comparison of Latent Class Analysis (LCA) and Factor Analysis (FA)

Background: Diet research focuses on the characteristics of "dietary patterns" regardless of the statistical methods used to derive them. However, the solutions to these methods are both conceptually and statistically different. Methods: We compared factor analysis (FA) and latent class analysis (LCA) methods to identify the dietary patterns of participants in the Chinese Wuxi Exposure and Breast Cancer Study, a population-based case-control study that included 818 patients and 935 healthy controls. We examined the association between dietary patterns and plasma lipid markers and the breast cancer risk. Results: Factor analysis grouped correlated food items into five factors, while LCA classified the subjects into four mutually exclusive classes. For FA, we found that the Prudent-factor was associated with a lower risk of breast cancer [4th vs. 1st quartile: odds ratio (OR) for 0.70, 95% CI = 0.52, 0.95], whereas the Picky-factor was associated with a higher risk (4th vs. 1st quartile: OR for 1.35, 95% CI = 1.00, 1.81). For LCA, using the Prudent-class as the reference, the Picky-class has a positive association with the risk of breast cancer (OR for 1.42, 95% CI = 1.06, 1.90). The multivariate-adjusted model containing all of the factors was better than that containing all of the classes in predicting HDL cholesterol (p = 0.04), triacylglycerols (p = 0.03), blood glucose (p = 0.04), apolipoprotein A1 (p = 0.02), and high-sensitivity C-reactive protein (p = 0.02), but was weaker than that in predicting the breast cancer risk (p = 0.03). Conclusion: Factor analysis is useful for understanding which foods are consumed in combination and for studying the associations with biomarkers, while LCA is useful for classifying individuals into mutually exclusive subgroups and compares the disease risk between the groups.

Subgrouping time-dependent prescribing patterns of first-onset major depressive episodes by psychotropics dissection

BACKGROUND

Subgrouping patients with major depressive disorder is a promising solution for the issue of heterogeneity. However, the link between available subtypes and distinct pathological mechanisms is weak and yields disappointing results in clinical application.

AIM

To develop a novel approach for classification of patients with time-dependent prescription patterns at first onset in real-world settings.

METHODS

Drug-naive patients experiencing their first major depressive episode (n = 105) participated in this study. Psychotropic agents prescribed in the first 24 mo following disease onset were recorded monthly and categorized as antidepressants, augmentation agents, and hypnosedatives. Monthly cumulative doses of agents in each category were converted into relevant equivalents. Four parameters were used to summarize the time-dependent prescription patterns for each psychotropic load: Stability, amount, frequency, and the time trend of monthly prescriptions. A K-means cluster analysis was used to derive subgroups of participants based on these input parameters of psychotropic agents across 24 mo. Clinical validity of the resulting data-driven clusters was compared using relevant severity indicators.

RESULTS

Four distinct clusters were derived from K-means analysis, which matches experts’ consent: "Short-term antidepressants use", "long-term antidepressants use", "long-term antidepressants and sedatives use", and "long-term antidepressants, sedatives, and augmentation use". At the first 2 years of disease course, the four clusters differed on the number of antidepressants used at adequate dosage and duration, frequency of outpatient service use, and number of psychiatric admissions. After the first 2 years following disease onset, depression severity was differed in the four subgroups.

CONCLUSION

Our findings suggested a new approach to optimize the subgrouping of patients with major depressive disorder, which may assist future etiological and treatment response studies.

Individual and common patterns in the order of symptom improvement during outpatient treatment for major depression

BACKGROUND

Research so far provided few clues on the order in which depressive symptoms typically remit during treatment. This study examined which depressive symptoms improve first, and whether symptoms changed before, simultaneous with, or after the core symptoms of depression (i.e., sad mood, loss of pleasure, and loss of interest).

METHODS

Participants were 176 patients with Major Depressive Disorder (MDD) receiving outpatient treatment (a combination of pharmacotherapy and psychological interventions) for depression. Participants filled out the Inventory of Depressive Symptomatology - Self Report (IDS-SR) for 16 to 20 consecutive weeks. For each symptom, the timing of onset of a persistent improvement was determined for each single-subject separately.

RESULTS

Which symptoms improved first differed markedly across patients. The core depression symptoms improved 1.5 to 2 times more often before (48% - 60%) than after (19% -28%) depressive cognitions ('view of myself' and 'view of the future'), anxiety symptoms ('feeling irritable' and 'feeling anxious / tense') and vegetative symptoms ('loss of energy', 'slowed down', and 'physical energy'). Only improvements in suicidal thoughts were more likely to occur before (46% - 48%) than after (29%) improvements in the depression core symptoms.

LIMITATIONS

Not all 'core depression-non-core symptom' combinations could be tested because some symptoms did not improve in a sufficient number of patients.

CONCLUSIONS

Which improvements mark the start of symptom remission differed between patients. Improvements in the core depression symptoms 'sad mood', 'loss of interest', and 'loss of pleasure' were more likely to occur before than after improvements in non-core symptoms.

Animal models for the study of depressive disorder

Depressive disorder is one of the most widespread forms of psychiatric pathology, worldwide. According to a report by the World Health Organization, the number of people with depression, globally, is increasing dramatically with each year. Previous studies have demonstrated that various factors, including genetics and environmental stress, contribute to the risk of depression. As such, it is crucial to develop a detailed understanding of the pathogenesis of depressive disorder and animal studies are essential for identifying the mechanisms and genetic disorders underlying depression. Recently, many researchers have reported on the pathology of depression via various models of depressive disorder. Given that different animal models of depression show differences in terms of patterns of depressive behavior and pathology, the comparison between depressive animal models is necessary for progress in the field of the depression study. However, the various animal models of depression have not been fully compared or evaluated until now. In this paper, we reviewed the pathophysiology of the depressive disorder and its current animal models with the analysis of their transcriptomic profiles. We provide insights for selecting different animal models for the study of depression.

Clinical characteristics of late-life depression predicting mortality

OBJECTIVE

Depression has been associated with increased mortality rates, and modifying mechanisms have not yet been elucidated. We examined whether specific subtypes or characteristics of late-life depression predict mortality.

METHODS

A cohort study including 378 depressed older patients according to DSM-IV criteria and 132 never depressed comparisons. The predictive value of depression subtypes and characteristics on the six-year mortality rate, as well as their interaction with somatic disease burden and antidepressant drug use, were studied by Cox proportional hazard analysis adjusted for demographic and lifestyle characteristics.

RESULTS

Depressed persons had a higher mortality risk than non-depressed comparisons (HR = 2.95 [95% CI: 1.41-6.16], p = .004), which lost significance after adjustment for age, sex, education, smoking, alcohol, physical activity, number of prescribed medications and somatic comorbidity. Regarding depression subtypes and characteristics, only minor depression was associated with a higher mortality risk when adjusted for confounders (HR = 6.59 [95% CI: 1.79-24.2], p = .005).

CONCLUSIONS

Increased mortality rates of depressed older persons seem best explained by unhealthy lifestyle characteristics and multiple drug prescriptions. The high mortality rate in minor depression, independent of these factors, might point to another, yet unknown, pathway towards mortality for this depression subtype. An explanation might be that minor depression in later life reflects depressive symptoms due to underlying aging-related processes, such as inflammation-based sickness behavior, frailty, and mild cognitive impairment, which have all been associated with increased mortality.

Risk Factors for Adult Depression: Adverse Childhood Experiences and Personality Functioning

Background: Depressive disorder is one of the main health problems worldwide. Many risk factors have been associated with this pathology. However, while the association between risks factors and adult depression is well established, the mechanisms behind its impact remains poorly understood. A possible, yet untested explanation is the mediating impact of levels of personality functioning, i.e., impairments with regard to self and interpersonal.

Method: Around 162 patients were assessed at the beginning of their therapy, with regard to risk factors, such as sociodemographic, physical, hereditary (Information Form), and adverse childhood experiences (ACE; CTQ). Depressive symptoms (Beck Depression Inventory, BDI) and personality functioning (OPD-SQ) were also measured. Associations between the related variables as well as other possible covariates were examined by means of zero-order correlations and bootstrapping-based mediation analysis.

Results: Of all the risk factors taken into account, level of education and physical illness were associated with depression. On the other hand, the most significant predictor of depressive symptomatology was ACE, and this relationship was mediated by personality functioning. This indicates that patients presenting adverse childhood experiences are more likely to develop deficiencies in personality functioning, which in turn increases their likelihood of developing depressive symptomatology.

Conclusion: These results reaffirm the importance of incorporating risk and vulnerability factors such as personality functioning in understanding depression.

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.

Perceptions of traditional and modern types of depression: A cross-cultural vignette survey comparing Japanese and American undergraduate students

AIM

Depression is a heterogeneous disorder that has various subtypes. In Japan, however, a prevailing misunderstanding is that the term utsu-byo (clinical depression) indicates only the melancholic type. Consequently, a subtype called 'modern-type depression' (MTD), which has contrasting features to those of melancholic or traditional-type depression (TTD), is severely stigmatized in Japan these days. The present study conducted a cross-cultural comparison of perceptions of TTD and MTD between Japan and the USA to examine how the Japanese collectivistic culture contributes to negative biases toward MTD.

METHODS

Undergraduate students in Japan (N = 303) and the Midwestern USA (N = 272) completed the survey. They read two vignettes that described the conditions of fictional individuals with either TTD or MTD, and then reported their perceptions of each vignette.

RESULTS

Mixed analyses of variance revealed significant interactions between nation (Japan or the USA) and vignette (TTD or MTD) on most perception items. These interactions and subsequent analyses with Bonferroni corrections mainly indicate the following: (i) Japanese are more likely to suppose that conditions of MTD are milder compared with TTD; and (ii) Japanese are more likely to hold stronger aversive attitudes and weaker willingness to provide support toward people with MTD than toward those with TTD.

CONCLUSION

These results indicate that people with MTD are more likely to be accepted in the US independent culture than in the Japanese collectivistic culture. Discussion highlights that cultural diversity education potentially reduces stigma of MTD in Japan.

Biomarker-based subtyping of depression and anxiety disorders using Latent Class Analysis. A NESDA study

BACKGROUND

Etiological research of depression and anxiety disorders has been hampered by diagnostic heterogeneity. In order to address this, researchers have tried to identify more homogeneous patient subgroups. This work has predominantly focused on explaining interpersonal heterogeneity based on clinical features (i.e. symptom profiles). However, to explain interpersonal variations in underlying pathophysiological mechanisms, it might be more effective to take biological heterogeneity as the point of departure when trying to identify subgroups. Therefore, this study aimed to identify data-driven subgroups of patients based on biomarker profiles.

METHODS

Data of patients with a current depressive and/or anxiety disorder came from the Netherlands Study of Depression and Anxiety, a large, multi-site naturalistic cohort study (n = 1460). Thirty-six biomarkers (e.g. leptin, brain-derived neurotrophic factor, tryptophan) were measured, as well as sociodemographic and clinical characteristics. Latent class analysis of the discretized (lower 10%, middle, upper 10%) biomarkers were used to identify different patient clusters.

RESULTS

The analyses resulted in three classes, which were primarily characterized by different levels of metabolic health: 'lean' (21.6%), 'average' (62.2%) and 'overweight' (16.2%). Inspection of the classes' clinical features showed the highest levels of psychopathology, severity and medication use in the overweight class.

CONCLUSIONS

The identified classes were strongly tied to general (metabolic) health, and did not reflect any natural cutoffs along the lines of the traditional diagnostic classifications. Our analyses suggested that especially poor metabolic health could be seen as a distal marker for depression and anxiety, suggesting a relationship between the 'overweight' subtype and internalizing psychopathology.

Identification of depression subtypes and relevant brain regions using a data-driven approach

It is well known that depressive disorder is heterogeneous, yet little is known about its neurophysiological subtypes. In the present study, we identified neurophysiological subtypes of depression related to specific neural substrates. We performed cluster analysis for 134 subjects (67 depressive subjects and 67 controls) using a high-dimensional dataset consisting of resting state functional connectivity measured by functional MRI, clinical questionnaire scores, and various biomarkers. Applying a newly developed, multiple co-clustering method to this dataset, we identified three subtypes of depression that are characterized by functional connectivity between the right Angular Gyrus (AG) and other brain areas in default mode networks, and Child Abuse Trauma Scale (CATS) scores. These subtypes are also related to Selective Serotonin-Reuptake Inhibitor (SSRI) treatment outcomes, which implies that we may be able to predict effectiveness of treatment based on AG-related functional connectivity and CATS.

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.

Association Between Self-Esteem and Depressive Symptoms Is Stronger Among Black than White Older Adults

BACKGROUND

Although poor self-esteem is a core component of depression, we still do not know if racial and ethnic groups differ in the magnitude of this link. This study compared Black and White older adults on the association between self-esteem and depressive symptoms.

METHODS

With a cross-sectional design, this study enrolled 1493 older individuals (age 66 or more) from the 2001 Religion, Aging, and Health Survey, a nationally representative study in the United States. Participants were either Blacks (n = 734) or Whites (n = 759). Depressive symptoms and self-esteem were measured using brief measures of the Center for Epidemiological Studies-Depression scale (CES-D) and the Rosenberg Self-Esteem Scale, respectively. Demographics, socioeconomics, and self-rated health (SRH) were covariates and self-identified race was the moderator. Linear regression models were used for data analysis.

RESULTS

Low self-esteem was associated with more depressive symptoms (B = 0.17, 95 % CI 0.15-0.28), above and beyond all covariates. We found a significant and positive interaction between race (Black) and poor self-esteem on depressive symptoms (B = 0.34, 95 % CI 0.17-0.36), suggesting a stronger association between self-esteem and depressive symptoms among Blacks compared to Whites. Although low self-esteem is associated with higher depressive symptoms in both Whites and Blacks (p < 0.05 for both races), the standardized coefficient was 0.25 (95 % CI = 0.20-0.43) for Blacks and 0.16 (95 % CI = 0.09-0.29) for Whites.

CONCLUSIONS

Low self-esteem and high depressive symptoms are more closely associated among Blacks than Whites. It is not clear whether depression leaves a larger scar on self-esteem for Blacks, or Blacks are more vulnerable to the effect of low self-esteem on depression.