BackgroundDepression is a highly recurrent disorder, with more than 50% of those affected experiencing a subsequent episode. Although there is relatively little stability in symptoms across... Show moreBackgroundDepression is a highly recurrent disorder, with more than 50% of those affected experiencing a subsequent episode. Although there is relatively little stability in symptoms across episodes, some evidence indicates that suicidal ideation may be an exception. However, these findings warrant replication, especially over longer periods and across multiple episodes.AimsTo assess the relative stability of suicidal ideation in comparison with other non-core depressive symptoms across episodes.MethodWe examined 490 individuals with current major depressive disorder (MDD) at baseline and at least one subsequent episode during 9-year follow-up within the Netherlands Study of Depression and Anxiety (NESDA). The Inventory of Depressive Symptomatology (IDS) was used to assess DSM-5 non-core MDD symptoms (fatigue, appetite/weight change, sleep disturbance, psychomotor disturbance, concentration difficulties, worthlessness/guilt, suicidal ideation) at baseline and 2-, 4-, 6- and 9-year follow-up. We examined consistency in symptom presentation (i.e. whether the symptom met the diagnostic threshold, based on a binary categorisation of the IDS) using kappa (κ) and percentage agreement, and stability in symptom severity using Spearman correlation, based on the continuous IDS scores.ResultsOut of all non-core depressive symptoms, insomnia appeared the most stable across episodes (r = 0.55–0.69, κ = 0.31–0.47) and weight decrease the least stable (r = 0.03–0.33, κ = 0.06–0.19). For suicidal ideation, correlations across episodes ranged from r = 0.36 to r = 0.55 and consistency ranged from κ = 0.28 to κ = 0.49.ConclusionsSuicidal ideation is moderately stable in recurrent depression over 9 years. Contrary to prior reports, however, it does not exhibit substantially more stability than most other non-core symptoms of depression. Show less
Roefs, A.; Fried, E.I.; Kindt, M.; Martijn, C.; Elzinga, B.; Evers, A.W.M.; ... ; Jansen, A. 2022
The core ideas of a 10-year research program 'New Science of Mental Disorders' are outlined. This research program moves away from the disorder-based 'one-model-fits-all' approach to treating... Show moreThe core ideas of a 10-year research program 'New Science of Mental Disorders' are outlined. This research program moves away from the disorder-based 'one-model-fits-all' approach to treating mental disorders, and adopts the network approach to psychopathology as its foundation of research. Its core assumption is that dynamically interacting symptoms constitute the disorder. Our goal is to further develop the network approach by studying (1) dynamic networks of symptoms and other variables (i.e., elements) in a large number of individuals with a wide range of mental disorders from a transdiagnostic perspective (network-based diagnosis; mapping), including both Ecological Momentary Assessment (EMA) and digital phenotyping, (2) the transdiagnostic mechanisms reflecting potential causal relations among elements of the networks by performing experimental (pre-)clinical studies (zooming), and (3) the effectiveness of personalised network-informed interventions (tar-geting). Challenges to overcome in this research program are discussed, which relate to data collection (e.g., selection of EMA variables) and data analyses (e.g., power considerations), the development and application of network-informed diagnoses and network-informed interventions (e.g., what characteristic(s) of the network to target in interventions), and the implementation in clinical practice (e.g., train therapists in the use of networks in therapy). Show less
Hebbrecht, K.; Stuivenga, M.; Birkenhager, T.; Morrens, M.; Fried, E.I.; Sabbe, B.; Giltay, E.J. 2020
BackgroundMajor depressive disorder (MDD) shows large heterogeneity of symptoms between patients, but within patients, particular symptom clusters may show similar trajectories. While symptom... Show moreBackgroundMajor depressive disorder (MDD) shows large heterogeneity of symptoms between patients, but within patients, particular symptom clusters may show similar trajectories. While symptom clusters and networks have mostly been studied using cross-sectional designs, temporal dynamics of symptoms within patients may yield information that facilitates personalized medicine. Here, we aim to cluster depressive symptom dynamics through dynamic time warping (DTW) analysis.MethodsThe 17-item Hamilton Rating Scale for Depression (HRSD-17) was administered every 2weeks for a median of 11weeks in 255 depressed inpatients. The DTW analysis modeled the temporal dynamics of each pair of individual HRSD-17 items within each patient (i.e., 69,360 calculated "DTW distances"). Subsequently, hierarchical clustering and network models were estimated based on similarities in symptom dynamics both within each patient and at the group level.ResultsThe sample had a mean age of 51 (SD 15.4), and 64.7% were female. Clusters and networks based on symptom dynamics markedly differed across patients. At the group level, five dynamic symptom clusters emerged, which differed from a previously published cross-sectional network. Patients who showed treatment response or remission had the shortest average DTW distance, indicating denser networks with more synchronous symptom trajectories.ConclusionsSymptom dynamics over time can be clustered and visualized using DTW. DTW represents a promising new approach for studying symptom dynamics with the potential to facilitate personalized psychiatric care. Show less
Multiple studies show an association between inflammatory markers and major depressive disorder (MDD). People with chronic low-grade inflammation may be at an increased risk of MDD, often in the... Show moreMultiple studies show an association between inflammatory markers and major depressive disorder (MDD). People with chronic low-grade inflammation may be at an increased risk of MDD, often in the form of sickness behaviors. We hypothesized that inflammation is predictive of the severity and the course of a subset of MDD symptoms, especially symptoms that overlap with sickness behavior, such as anhedonia, anorexia, low concentration, low energy, loss of libido, psychomotor slowness, irritability, and malaise. We tested the association between basal and lipopolysaccharide (LPS)-induced inflammatory markers with individual MDD symptoms (measured using the Inventory of Depressive Symptomatology Self-Report) over a period of up to 9 years using multivariate-adjusted mixed models in 1147-2872 Netherlands Study of Depression and Anxiety (NESDA) participants. At baseline, participants were on average 42.2 years old, 66.5% were women and 53.9% had a current mood or anxiety disorder. We found that basal and LPS-stimulated inflammatory markers were more strongly associated with sickness behavior symptoms at up to 9-year follow-up compared with non-sickness behavior symptoms of depression. However, we also found significant associations with some symptoms that are not typical of sickness behavior (e.g., sympathetic arousal among others). Inflammation was not related to depression as a unified syndrome but rather to the presence and the course of specific MDD symptoms, of which the majority were related to sickness behavior. Anti-inflammatory strategies should be tested in the subgroup of MDD patients who report depressive symptoms related to sickness behavior. Show less
