The Role of Relationship Status in Major Depressive Disorder - Results of the European Group for the Study of Resistant Depression

The clinical perspective on late-onset depression in European real-world treatment settings

The clinical phenotype of the so-called late-onset depression (LOD) affecting up to 30% of older adults and yielding heterogeneous manifestations concerning symptoms, severity and course has not been fully elucidated yet. This European, cross-sectional, non-interventional, naturalistic multicenter study systematically investigated socio-demographic and clinical correlates of early-onset depression (EOD) and LOD (age of onset ≥ 50 years) in 1410 adult in- and outpatients of both sexes receiving adequate psychopharmacotherapy. In a total of 1329 patients (94.3%) with known age of disease onset, LOD was identified in 23.2% and was associated with unemployment, an ongoing relationship, single major depressive episodes, lower current suicidal risk and higher occurrence of comorbid hypertension. In contrast, EOD was related to higher rates of comorbid migraine and additional psychotherapy. Although the applied study design does not allow to draw any causal conclusions, the present results reflect broad clinical settings and emphasize easily obtainable features which might be characteristic for EOD and LOD. A thoughtful consideration of age of onset might, hence, contribute to optimized diagnostic and therapeutic processes in terms of the globally intended precision medicine, ideally enabling early and adequate treatment allocations and implementation of respective prevention programs.

Evaluating a Comprehensive Model of Euthymia

INTRODUCTION

In research and treatment of mood disorders, "euthymia" traditionally denotes the absence of clinically significant mood disturbance. A newer, expanded definition of euthymia also includes positive affect and psychological well-being.

OBJECTIVE

We aimed to test this comprehensive model of euthymia and estimate the coherence and predictive power of each factor in the model.

METHODS

Community-dwelling adults (N = 601), including both mental health outpatients and non-patients at high risk for personality pathology, completed a battery of interviews and questionnaires at time 1. Most (n = 497) were reassessed on average 8 months later (time 2). We modeled euthymia using standard mood, personality, and psychosocial functioning assessments rather than measures designed specifically for euthymia.

RESULTS

The hypothesized model of euthymia was supported by confirmatory factor analysis: specific measures loaded on three lower order factors (mood disturbance, positive affect, and psychological well-being) that reflected general euthymia at time 1. Each factor (general euthymia plus lower order factors) demonstrated moderately strong concurrent (time 1) and predictive (time 1-2) correlations with outcomes, including employment status, income, mental health treatment consumption, and disability. Compared to positive affect and psychological well-being, mood disturbance had stronger incremental (i.e., nonoverlapping) relations with these outcomes.

CONCLUSIONS

Support for a comprehensive model of euthymia reinforces efforts to improve assessment and treatment of mood and other disorders. Beyond dampening of psychological distress, euthymia-informed treatment goals encompass full recovery, including enjoyment and meaning in life.

Sociodemographic and cultural determinants of mood disorders

PURPOSE OF REVIEW

The present review aims to examine, summarize and update information on the sociodemographic and cultural determinants of mood disorders.

RECENT FINDINGS

Known sociodemographic and cultural determinants continue to be good predictors of the risk of developing a mood disorder over the lifetime. Polygenic risk scores do not appear to offer any advantages over these determinants at present. There is also new and emerging understanding of the role of lifestyle and environmental factors in mediating vulnerability to mood disorder. The influence of ethnicity and migration, on the other hand, is far more complex than initially envisaged.

SUMMARY

Recent evidence on sociodemographic determinants of mood disorders confirms associations derived from existing literature. There is also new and emerging evidence on how quality of sleep, diet and the environment influence risk of mood disorders. Culture and ethnicity, depending on context, may contribute to both vulnerability and resilience. Socioeconomic deprivation may be the final common pathway through which several sociodemographic and cultural determinants of mood disorders act.

Pregabalin augmentation of antidepressants in major depression - results from a European multicenter study

BACKGROUND

We aimed to investigate the prescription pattern of pregabalin augmentation of antidepressants in major depressive disorder (MDD) and to explore variables associated with add-on pregabalin treatment.

METHODS

1410 MDD patients participated in this naturalistic European multicenter study with retrospective assessment of treatment response. Analyses of covariance, chi-squared tests, and binary logistic regressions were accomplished to determine differences in socio-demographic and clinical characteristics between MDD patients with and without pregabalin augmentation.

RESULTS

Add-on pregabalin was established in 102 (7.23%) MDD patients. Compared to those without receiving pregabalin, pregabalin-treated patients were characterized by a significantly higher likelihood for older age (mean: 54.74 ± 13.08 vs 49.93 ± 14.13 years), unemployment (78.43% vs 51.23%), melancholic features (83.33% vs 58.94%), inpatient treatment (72.55% vs 31.65%), previous psychiatric hospitalizations (13.52 ± 24.82 vs 4.96 ± 19.93 weeks), any somatic comorbidity (68.63% vs 44.57%), comorbid hypertension (37.25% vs 17.51%), more severe depressive symptom severity at the onset of the current episode (mean MADRS: 37.55 ± 9.00 vs 33.79 ± 7.52), receiving augmentation/combination treatment strategies in general (mean number of psychotropic drugs: 3.64 ± 0.92 vs 2.07 ± 1.17), and with antidepressants (50.00% vs 27.91%) and antipsychotics (46.08% vs 24.08%) in particular.

LIMITATIONS

Due to its observational cross-sectional study design, our patient sample might not be fully representative for MDD patients in primary care settings.

CONCLUSIONS

Our findings suggest that add-on pregabalin is particularly administered in more severe/difficult-to-treat MDD conditions, whereas no association between the prescription of adjunctive pregabalin and comorbid anxiety symptoms could be determined.

Prediction of Probable Major Depressive Disorder in the Taiwan Biobank: An Integrated Machine Learning and Genome-Wide Analysis Approach

In light of recent advancements in machine learning, personalized medicine using predictive algorithms serves as an essential paradigmatic methodology. Our goal was to explore an integrated machine learning and genome-wide analysis approach which targets the prediction of probable major depressive disorder (MDD) using 9828 individuals in the Taiwan Biobank. In our analysis, we reported a genome-wide significant association with probable MDD that has not been previously identified: FBN1 on chromosome 15. Furthermore, we pinpointed 17 single nucleotide polymorphisms (SNPs) which show evidence of both associations with probable MDD and potential roles as expression quantitative trait loci (eQTLs). To predict the status of probable MDD, we established prediction models with random undersampling and synthetic minority oversampling using 17 eQTL SNPs and eight clinical variables. We utilized five state-of-the-art models: logistic ridge regression, support vector machine, C4.5 decision tree, LogitBoost, and random forests. Our data revealed that random forests had the highest performance (area under curve = 0.8905 ± 0.0088; repeated 10-fold cross-validation) among the predictive algorithms to infer complex correlations between biomarkers and probable MDD. Our study suggests that an integrated machine learning and genome-wide analysis approach may offer an advantageous method to establish bioinformatics tools for discriminating MDD patients from healthy controls.