Depression is one of the most prevalent mental health conditions in the world. However, the heterogeneity of depression has presented obstacles for research concerning disease mechanisms, treatment indication, and personalization. The current study used network analysis to analyze and compare profiles of depressive symptoms present in community samples, considering the relationship between symptoms.

Cross-sectional measures of depression using the Patient Health Questionnaire – 9 items (PHQ-9) were collected from community samples using data from participants scoring above a clinical threshold of ≥10 points (N = 2,023; 73.9% female; mean age 49.87, SD = 17.40). Data analysis followed three steps. First, a profiling algorithm was implemented to identify all possible symptom profiles by dichotomizing each PHQ-9 item. Second, the most prevalent symptom profiles were identified in the sample. Third, network analysis for the most prevalent symptom profiles was carried out to identify the centrality and covariance of symptoms.

Of 382 theoretically possible depression profiles, only 167 were present in the sample. Furthermore, 55.6% of the symptom profiles present in the sample were represented by only eight profiles. Network analysis showed that the network and symptoms’ relationship varied across the profiles.

Findings indicate that the vast number of theoretical possible ways to meet the criteria for major depressive disorder (MDD) is significantly reduced in empirical samples and that the most common profiles of symptoms have different networks and connectivity patterns. Scientific and clinical consequences of these findings are discussed in the context of the limitations of this study.

Alcohol consumption, smoking and mood disorders are leading contributors to the global burden of disease and are highly comorbid. Yet, their interrelationships have remained elusive. The aim of this study was to examine the multi-cross-sectional and longitudinal associations between (change in) smoking and alcohol use and (change in) number of depressive symptoms.

In this prospective, longitudinal study, 6646 adults from the general population were included with follow-up measurements after 3 and 6 years. Linear mixed-effects models were used to test multi-cross-sectional and longitudinal associations, with smoking behaviour, alcohol use and genetic risk scores for smoking and alcohol use as independent variables and depressive symptoms as dependent variables.

In the multi-cross-sectional analysis, smoking status and number of cigarettes per day were positively associated with depressive symptoms (p < 0.001). Moderate drinking was associated with less symptoms of depression compared to non-use (p = 0.011). Longitudinally, decreases in the numbers of cigarettes per day and alcoholic drinks per week as well as alcohol cessation were associated with a reduction of depressive symptoms (p = 0.001–0.028). Results of genetic risk score analyses aligned with these findings.

While cross-sectionally smoking and moderate alcohol use show opposing associations with depressive symptoms, decreases in smoking behaviour as well as alcohol consumption are associated with improvements in depressive symptoms over time. Although we cannot infer causality, these results open avenues to further investigate interventions targeting smoking and alcohol behaviours in people suffering from depressive symptoms.

Appetite and weight changes are commonly occurring symptoms of depressive illness. The occurrence of these symptoms may not only be related to depressive mood but may also be related to body weight.

To examine the relationship between symptoms of depression and body weight.

Symptoms of depression were assessed by the Montgomery-Asberg depression rating scale (MADRS) in 1694 patients seeking medical help and fulfilling DSM-IV criteria for a major depressive episode. The level of anxiety was evaluated by Covi’s anxiety scale. Body weight was expressed as body-mass index (BMI, kg/m2) and treated both categorically and continuously.

The total MADRS score was not statistically different across the four BMI categories (underweight: 32.3 ± 0.6, normal weight: 30.9 ± 0.2, grade 1: 30.6 ± 0.3, and 2 overweight: 30.6 ± 0.6, P = 0.053 (NS)). In women with BMI ≤ 18.5 kg/m2 MADRS was significantly higher than that in other BMI categories (underweight: 32.4 ± 0.6, normal weight: 30.6 ± 0.2, grade 1: 30.6 ± 0.4, and 2 overweight: 30.6 ± 0.6: P = 0.036). Increasing BMI was related to a linear decrease in symptoms “Reduced appetite” (P < 0.0001) and “Pessimistic thoughts” (P < 0.003). The presence of melancholic or atypical features was not associated with lower or higher BMI, respectively.

In patients with major depression higher body weight is likely to be associated with less reduction in appetite and less pessimistic thoughts.

Subclinical Narcissism (SN) is part of the Dark Triad (DT), which includes also Subclinical Psychopathy (SP) and Machiavellianism. SN comprises facets retained from the clinical syndrome, such as grandiosity and dominance. Previous cross-sectional and longitudinal research indicates that SN may increase Mental Toughness (MT) resulting in various positive outcomes, including lower levels of psychopathy.

The researchers conducted three studies (N = 364, 244 and 144 for Study 1, 2 and 3 respectively) to test if the path model from SN to higher MT predicted lower symptoms of depression (DS). An extension to the model considered Openness to Experience (OE) as a possible mediator. Participants completed self-report measures of SN, MT, OE and DS. In Study 3, participants responded to an additional measure of SN to allow differentiation between grandiose and vulnerable aspects.

SN exerted a negative indirect effect on DS through MT across studies; and a negative indirect effect on DS through MT and OE in Study 2. In Study 3, Grandiose SN increased MT contributing to lower DS. Vulnerable SN demonstrated the reverse pattern. MT subfactors of Control and Confidence had a mediating effect across studies.

The current findings support the model that SN to MT predicts positive outcomes in various domains, including lower levels of psychiatric symptoms. Exploring the link between SN with prosocial traits can be particularly helpful when seeking to identify and promote SN’s adaptive tendencies against symptoms of psychopathology.