Casting wider nets for anxiety and depression: disability-driven cross-diagnostic subtypes in a large cohort

Role of Inflammation in Depressive and Anxiety Disorders, Affect, and Cognition: Genetic and Non-Genetic Findings in the Lifelines Cohort Study

Background

Low-grade systemic inflammation is implicated in the pathogenesis of various neuropsychiatric conditions affecting mood and cognition. While much of the evidence concerns depression, large-scale population studies of anxiety, affect, and cognitive function are scarce. Importantly, causality remains unclear. We used complementary non-genetic, genetic risk score (GRS), and Mendelian randomization (MR) analyses to examine whether inflammatory markers are associated with affect, depressive and anxiety disorders, and cognitive performance in the Lifelines Cohort; and whether associations are likely to be causal.

Methods

Using data from up to 55,098 (59% female) individuals from the Dutch Lifelines cohort, we tested the cross-sectional and longitudinal associations of C-reactive protein (CRP) with (i) depressive and anxiety disorders; (ii) positive and negative affect scores, and (iii) five cognitive measures assessing attention, psychomotor speed, episodic memory, and executive functioning (figural fluency and working memory). Additionally, we examined the association between inflammatory marker GRSs (CRP, interleukin-6 [IL-6], IL-6 receptor [IL-6R and soluble IL-6R (sIL-6R)], glycoprotein acetyls [GlycA]) on these same outcomes (Nmax=57,946), followed by MR analysis examining evidence of causality of CRP on outcomes (Nmax=23,268). In genetic analyses, all GRSs and outcomes were z-transformed.

Results

In non-genetic analyses, higher CRP was associated with diagnosis of any depressive disorder, lower positive and higher negative affect scores, and worse performance on tests of figural fluency, attention, and psychomotor speed after adjusting for potential confounders, although the magnitude of these associations was small. In genetic analyses, CRPGRS was associated with any anxiety disorder (β=0.002, p=0.037, N=57,047) whereas GlycAGRS was associated with major depressive disorder (β=0.001, p=0.036; N=57,047). Both CRPGRS (β=0.006, p=0.035, N=57,946) and GlycAGRS (β=0.006, p=0.049; N=57,946) were associated with higher negative affect score. Inflammatory marker GRSs were not associated with cognitive performance, except sIL-6RGRS which was associated with poorer memory performance (β=-0.009, p=0.018, N=36,783). Further examination of the CRP-anxiety association using MR provided some weak evidence of causality (β=0.12; p=0.054).

Conclusions

Genetic and non-genetic analyses provide consistent evidence for an association between CRP and negative affect. Genetic analyses suggest that IL-6 signaling could be relevant for memory, and that the association between CRP and anxiety disorders could be causal. These results suggest that dysregulated immune physiology may impact a broad range of trans-diagnostic affective symptoms. However, given the small effect sizes and multiple tests conducted, future studies are required to investigate whether effects are moderated by sub-groups and whether these findings replicate in other cohorts.

Profiles of Intolerance of Uncertainty Among 108,540 Adolescents: Associations with Sociodemographic Variables and Mental Health

Intolerance of uncertainty (IU) is widely considered a transdiagnostic risk and maintaining factor for psychiatric disorders. However, little is known about the overall nature and profile of IU among adolescents. This study aims to investigate the profiles of IU among Chinese adolescents and explore their associations with sociodemographic characteristics and mental health problems. A sample of 108,540 adolescents provided data on IU, sociodemographic characteristics, and mental health via an online platform. Latent profile analysis revealed three profiles: Low IU, Medium IU, and High IU. Girls, older adolescents, and those with specific sociodemographics were more likely to belong to the "High IU" profile. Furthermore, the "High IU" profile was associated with the highest risk of several mental health problems. These findings provided valuable information for early prevention and intervention strategies targeting IU and highlighted the importance of IU-based interventions for mental health among adolescents.

Psychological treatment of depression with other comorbid mental disorders: systematic review and meta-analysis

Most people with a mental disorder meet criteria for multiple disorders. We conducted a systematic review and meta-analysis of randomized trials comparing psychotherapies for people with depression and comorbid other mental disorders with non-active control conditions. We identified studies through an existing database of randomized trials on psychotherapies for depression. Thirty-five trials (3,157 patients) met inclusion criteria. Twenty-seven of the 41 interventions in the 35 trials (66%) were based on CBT. The overall effect on depression was large (g = 0.65; 95% CI: 0.40 ~ 0.90), with high heterogeneity (I² = 78%; 95% CI: 70 ~ 83). The ten studies in comorbid anxiety showed large effects on depression (g = 0.90; 95% CI: 0.30 ~ 1.51) and anxiety (g = 1.01; 95% CI: 0.28 ~ 1.74). For comorbid insomnia (11 comparisons) a large and significant effect on depression (g = 0.99; 95% CI: 0.16 ~ 1.82) and insomnia (g = 1.38; 95% CI: 0.38 ~ 2.38) were found. For comorbid substance use problems (12 comparisons) effects on depression (g = 0.25; 95% CI: 0.06 ~ 0.43) and on substance use problems (g = 0.25; 95% CI: 0.01 ~ 0.50) were significant. Most effects were no longer significant after adjustment for publication bias and when limited to studies with low risk of bias. Therapies are probably effective in the treatment of depression with comorbid anxiety, insomnia, and substance use problems.

Profiles of depressive symptoms and influential factors among people living with HIV in China

BACKGROUND

Depressive symptoms are highly prevalent among people living with HIV (PLWH). We leveraged Latent Profile Analysis (LPA) to identify profiles of depressive symptoms among PLWHs. We also investigated differences in psychological factors of interest, demographic characteristics, and HIV-related factors across patients' profiles.

METHODS

A cross-sectional study was conducted at one hospital and two designated prison facilities in Hunan province, China. A total sample of 533 PLWHs (320 recruited from the hospital, 213 recruited from prisons) completed the survey. Depressive symptoms were assessed using the 9-item Patient Health Questionnaire (PHQ-9). Family function, resilience, childhood trauma, demographic characteristics, and HIV-related factors were also evaluated. We conducted LPA and multinomial logistic regression analyses to: 1) identify distinct profiles for depressive symptoms; 2) identify demographic characteristics, and HIV-related, and psychological factors predicting PLWHs' likelihood to express a specific profile.

RESULTS

We identified three distinct profiles of depressive symptoms among PLWHs: severe symptoms (11.8%), moderate symptoms (40.5%), and low/no symptoms (47.7%). Moderate/ severe family dysfunction, low resilience, experiencing emotional abuse and neglect were more likely to fall in the "severe symptoms" rather than the "low/no symptoms" profile. In addition, severe family dysfunction, low resilience, and experiencing emotional neglect indicated a higher likelihood of being classified in the "moderate symptoms" profile, compared to the "low/no symptoms" profile.

CONCLUSION

Identifying profiles of depressive symptoms among PLWHs using the PHQ-9 items allows for understanding of the distinct paths of development of depressive symptoms and for developing tailored prevention and intervention programs for PLWHs.

Distinct patterns of problematic smartphone use and related factors in Chinese college students

BACKGROUND

This study aimed to categorize different subgroups of problematic smartphone use in Chinese college students. Differences in gender and psychosocial characteristics of the categorized groups were also examined.

METHODS

A total of 1123 participants completed the Mobile Phone Addiction Index Scale, the Center for Epidemiologic Studies Depression Scale, the Perceived Social Support Scale, and the Perceived Stress Scale. Using latent profile analysis, we identified different subgroups of problematic smartphone use in college students. Multivariate logistic regression analysis was implemented to examine the relationship between latent classes and demographic and psychosocial covariates.

RESULTS

The four following latent classes were identified: a low-risk group, a moderate-risk with no evasiveness group, a moderate-risk with evasiveness group, and high-risk group that accounted for 11%, 24.1%, 35.5%, and 29.4% of the total sample, respectively. Further analysis revealed that female participants were more likely to be in the moderate-risk with evasiveness and high-risk groups, and individuals with depressed mood were more likely to be in the moderate-risk and high-risk groups.

CONCLUSIONS

Classifying college students according to the features of problematic smartphone use is potentially useful for understanding risk factors and developing targeted prevention and intervention programs.

The structure of the symptoms of major depression: Factor analysis of a lifetime worst episode of depressive symptoms in a large general population sample

BACKGROUND

A range of depressive symptoms may occur during an episode of major depression (MD). Do these symptoms describe a single disorder liability or different symptom dimensions? This study investigates the structure and clinical relevance of an expanded set of depressive symptoms in a large general population sample.

METHODS

We studied 43,431 subjects from the Dutch Lifelines Cohort Study who participated in an online survey assessing the 9 symptom criteria of MD (DSM-IV-TR) and additional depressive symptoms during their worst lifetime episode of depressive symptoms lasting two weeks or more. Exploratory factor analyses were performed on expanded sets of 9, 14, and 24 depressive symptoms. The clinical relevance of the identified symptom dimensions was analyzed in confirmatory factor analyses including ten external validators.

RESULTS

A single dimension adequately accounted for the covariation among the 9 DSM-criteria, but multiple dimensions were needed to describe the 14 and 24 depressive symptoms. Five dimensions described the structure underlying the 24 depressive symptoms. Three cognitive affective symptom dimensions were mainly associated with risk factors for MD. Two somatic dimensions -appetite/weight problems and sleep problems-were mainly associated with BMI and age, respectively.

LIMITATIONS

Respondents of our online survey tended to be more often female, older, and more highly educated than non-respondents.

CONCLUSIONS

Different symptom dimensions described the structure of depressive symptoms during a lifetime worst episode in a general population sample. These symptom dimensions resembled those reported in a large clinical sample of Han-Chinese women with recurrent MD, suggesting robustness of the syndrome of MD.

Investigating data-driven biological subtypes of psychiatric disorders using specification-curve analysis

BACKGROUND

Cluster analyses have become popular tools for data-driven classification in biological psychiatric research. However, these analyses are known to be sensitive to the chosen methods and/or modelling options, which may hamper generalizability and replicability of findings. To gain more insight into this problem, we used Specification-Curve Analysis (SCA) to investigate the influence of methodological variation on biomarker-based cluster-analysis results.

METHODS

Proteomics data (31 biomarkers) were used from patients (n = 688) and healthy controls (n = 426) in the Netherlands Study of Depression and Anxiety. In SCAs, consistency of results was evaluated across 1200 k-means and hierarchical clustering analyses, each with a unique combination of the clustering algorithm, fit-index, and distance metric. Next, SCAs were run in simulated datasets with varying cluster numbers and noise/outlier levels to evaluate the effect of data properties on SCA outcomes.

RESULTS

The real data SCA showed no robust patterns of biological clustering in either the MDD or a combined MDD/healthy dataset. The simulation results showed that the correct number of clusters could be identified quite consistently across the 1200 model specifications, but that correct cluster identification became harder when the number of clusters and noise levels increased.

CONCLUSION

SCA can provide useful insights into the presence of clusters in biomarker data. However, SCA is likely to show inconsistent results in real-world biomarker datasets that are complex and contain considerable levels of noise. Here, the number and nature of the observed clusters may depend strongly on the chosen model-specification, precluding conclusions about the existence of biological clusters among psychiatric patients.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Investigating whether a combination of higher CRP and depression is differentially associated with worse executive functioning in a cohort of 43,896 adults

Many depressed individuals experience difficulties in executive functioning that contribute substantially to functional impairment. It is unknown whether a subtype of depression characterized by chronic inflammation is differentially associated with worse executive functioning. This study examined whether the combination of depression and higher C reactive protein (CRP) is differentially associated with worse executive functioning and whether this association is stronger in older adults. This cross-sectional study analyzed data collected from a population-representative sample of 43,896 adults aged 44.13 years (SD = 13.52) who participated in the baseline assessment of the Lifelines cohort study. Multivariate regression models tested whether depressed individuals (established via structured interview) exhibiting higher levels of inflammation (indexed via high-sensitivity CRP assay following an overnight fast) performed worse on a behavioral test of executive functioning. Depression (B = -3.66, 95% CI: -4.82, -2.49, p < .001) and higher log-transformed CRP (B = -0.67, 95% CI: -0.87,-0.47, p < .001) were associated with worse executive functioning, after adjustment for age, sex, educational attainment, body mass index, smoking status, exposure to stressful life events and chronic stressors, sedentary behavior, and number of chronic medical conditions. Depressed individuals with higher log-transformed CRP exhibited differentially poorer executive functioning (B = -1.09, 95% CI: -2.07,-0.11, p < .001). This association did not differ based on age (B = 0.01, 95% CI: -0.08, 0.10, p = .82). Executive functioning is poorer in depressed individuals with higher CRP, even in early adulthood. Interventions that reduce inflammation may improve cognitive functioning in depression.

Additive, Curvilinear, and Interactive Relations of Anxiety and Depression With Indicators of Psychosocial Functioning

This study examines additive, curvilinear, and interactive relations of anxiety and depression with several subjective indicators of intrapersonal (i.e., hope, self-compassion, shame) and interpersonal (i.e., social connectedness, quality of social relationships) functioning in a sample of adults ( N =  547, Mage = 43.37 ± 12.02, female = 56.88%) seeking treatment for psychological difficulties. Results of complementary analyses were largely consistent with the hypothesis that increasing levels of anxiety and depression would correspond with worse psychosocial functioning, although nonlinear relations indicated that the effect of depression progressively attenuated at higher levels of symptom severity. Whereas the findings generally supported additive effects of anxiety and depression, the hypothesis that there would be synergistic effects of anxiety and depression was not supported. Supplementary group comparisons revealed that the functional implications of subsyndromal combinations of anxiety and depression may be comparable to those associated with symptoms that meet more traditional standards (i.e., syndromal or dimensional definitions) of comorbid anxiety–depression. The findings offer further insight into the complex relations of anxiety and depression with psychosocial functioning and emphasize the importance of detecting and offering appropriate treatments for anxiety and depression symptoms that coexist at subsyndromal levels.

A study on classification features of depressive symptoms in adolescents

Although extensive literature has addressed depression among adolescents, few studies have emphasized the classification features of depressive symptoms in adolescents. To gain insight into the hierarchy and heterogeneity of depression in adolescents based on symptoms, 5086 adolescents completed the Chinese version of the Center for Epidemiological Studies Depression Scale (CES-D). Using Latent Class Analysis (LCA), we identified different subgroups of adolescents based on depressive symptoms. Multivariate logistic regression analysis was implemented to examine the relations between latent classes and demographic covariates. Four latent classes of individuals with depressive symptoms displaying a pattern of hierarchical organization were identified. The four classes were ordered by the degree of severity, ranging from the students reporting the highest number of depressive symptoms to the lowest number: "probable clinical depression", "subthreshold depression", "mild depression" and "low depression", accounting for 8.2%, 19.2%, 41.8% and 30.8% of total sample respectively. Further analyses revealed that compared to the "mild depression" class, the rest of three classes differed significantly across age groups and only child (vs. sibling) status. In conclusion, classifying the groups of adolescents based on features of depressive symptoms is potentially useful for understanding risk factors and developing tailored prevention and intervention programs for this age group.

Subtyping psychological distress in the population: a semi-parametric network approach

AIMS

The mechanisms underlying both depressive and anxiety disorders remain poorly understood. One of the reasons for this is the lack of a valid, evidence-based system to classify persons into specific subtypes based on their depressive and/or anxiety symptomatology. In order to do this without a priori assumptions, non-parametric statistical methods seem the optimal choice. Moreover, to define subtypes according to their symptom profiles and inter-relations between symptoms, network models may be very useful. This study aimed to evaluate the potential usefulness of this approach.

METHODS

A large community sample from the Canadian general population (N = 254 443) was divided into data-driven clusters using non-parametric k-means clustering. Participants were clustered according to their (co)variation around the grand mean on each item of the Kessler Psychological Distress Scale (K10). Next, to evaluate cluster differences, semi-parametric network models were fitted in each cluster and node centrality indices and network density measures were compared.

RESULTS

A five-cluster model was obtained from the cluster analyses. Network density varied across clusters, and was highest for the cluster of people with the lowest K10 severity ratings. In three cluster networks, depressive symptoms (e.g. feeling depressed, restless, hopeless) had the highest centrality. In the remaining two clusters, symptom networks were characterised by a higher prominence of somatic symptoms (e.g. restlessness, nervousness).

CONCLUSION

Finding data-driven subtypes based on psychological distress using non-parametric methods can be a fruitful approach, yielding clusters of persons that differ in illness severity as well as in the structure and strengths of inter-symptom relationships.

Chronic diseases and social risk factors in relation to specific symptoms of depression: Evidence from the U.S. national health and nutrition examination surveys

BACKGROUND

Depression is a heterogeneous mental disorder with multiple symptoms, but only few studies have examined whether associations of risk factors with depression are symptom-specific. We examined whether chronic diseases and social risk factors (poverty, divorce, and perceived lack of emotional support) are differently associated with somatic and cognitive/affective symptoms of depression.

METHODS

Cross-sectional analyses were based on individual-level data from the 31,191 participants of six cross-sectional U.S. National Health and Nutrition Examination Surveys (NHANES) carried out between 2005 and 2016. Depressive symptoms were assessed using the 9-item Patient Health Questionnaire. Information on chronic diseases and social risk factors was self-reported by participants.

RESULTS

After adjustment for sex, age, race/ethnicity, and all the of other symptoms besides the outcome symptom, higher number of chronic diseases was independently related to fatigue, psychomotor retardation/agitation, and sleep problems in a dose-response pattern (range of odds ratios: 1.21 to 2.59). Except for concentration problems, social risk factors were associated with almost all of the cognitive/affective symptoms (range of odds ratios: 1.02 to 2.09) but only sporadically with somatic symptoms.

LIMITATIONS

All measures were self-reported by the participants, which may have introduced bias to the associations. Cross-sectional data did not allow us to study temporal dynamics.

CONCLUSIONS

Specific symptoms of depression may be useful in characterizing the heterogeneous etiology of depression with respect to somatic versus social risk factors.

Anxiety disorders and CRP in a population cohort study with 54,326 participants: The LifeLines study

OBJECTIVES

Growing evidence indicates that inflammatory processes may play a role in the pathogenesis of anxiety disorders. Nevertheless, much remains to be learned about the involvement of inflammation, including C-reactive protein (CRP), in specific anxiety disorders. This study examines the relation between anxiety disorders and CRP.

METHODS

Associations of serum CRP with anxiety disorders were determined in a large population study (n = 54,326 participants, mean age = 47 years; 59% female), the LifeLines cohort. Depressive and anxiety disorders (generalized anxiety disorder, social anxiety phobia, panic disorder with or without agoraphobia and agoraphobia without panic disorder) were assessed using the Mini-International Neuropsychiatric Interview.

RESULTS

Anxiety disorders, with the exception of social anxiety disorder, were significantly associated with increased CRP. After adjusting for demographics, life style factors, health factors, medication use, depression, and psychological stressors, CRP remained significantly associated with panic disorder with agoraphobia (β = 0.01, P = .013). Moreover, CRP levels were significantly higher in people with panic disorder with agoraphobia compared to other anxiety disorders, independent of all covariates (F = 3.00, df = 4, P = .021).

CONCLUSIONS

Panic disorder with agoraphobia is associated with increased CRP, although the effect size of this association is small. This indicates that neuroinflammatory mechanisms may play a potential role in its pathophysiology.

Symptom severity and disability in psychiatric disorders: The U.S. Collaborative Psychiatric Epidemiology Survey

BACKGROUND

While most psychiatric diagnoses are based on simple counts of symptoms, some symptoms may be sign of a more severe mental syndrome than others. This calls for validated estimates of the relative severity specific symptoms imply within a disorder. We focused on four diagnostic disorders: Manic Episode (ME), Major Depressive Episode (MDE), Post-traumatic Stress Disorder (PTSD) and Generalized Anxiety Disorder (GAD). Symptom-specific severity parameters were estimated, and validated by examining their association with levels of self-reported disability in daily activities over and above the number of symptoms.

METHODS

Data from the cohort study of the U.S. Collaborative Psychiatric Epidemiology Surveys (CPES) was used, which comprises the National Comorbidity Survey Replication, National Survey of American Life, and the National Latino and Asian American Study. The four analytic datasets included respondents who endorsed disorder-specific pre-screening symptoms according to the World Mental Health Survey Initiative's version of the Composite International Diagnostic Interview. Disability was measured using the WHO Disability Assessment Schedule. Item Response Theory and Tobit models were implemented.

RESULTS

For ME, PTSD, and GAD (not MDE) symptom severity based on psychometric Item Response Theory predicted disability outcomes after adjusting for symptom count. For PTSD, symptom count was not associated with disability.

LIMITATIONS

The analytic sample for each psychiatric disorder was based on a pre-selection stemming from index criteria (e.g. sadness or pleasure loss for MDE), which implies that our results are only generalizable to those individuals at risk rather than for the entire population. Additionally, we acknowledge that the use of unidimensional models is only one of the several options to model psychopathological constructs.

CONCLUSIONS

The same number of symptoms may be related to different levels of disability, depending on the specific symptoms from which the person suffers. Diagnostic procedures and treatment decisions may benefit from such additional information without extra costs.

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

BACKGROUND

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

METHODS

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

RESULTS

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

LIMITATIONS

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

CONCLUSIONS

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