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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.
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.
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.
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.
It has been claimed that functional somatic syndromes share a common etiology. This prospective population-based study assessed whether the same variables predict new onsets of irritable bowel syndrome (IBS), chronic fatigue syndrome (CFS) and fibromyalgia (FM).
The study included 152 180 adults in the Dutch Lifelines study who reported the presence/absence of relevant syndromes at baseline and follow-up. They were screened at baseline for physical and psychological disorders, socio-demographic, psycho-social and behavioral variables. At follow-up (mean 2.4 years) new onsets of each syndrome were identified by self-report. We performed separate analyses for the three syndromes including participants free of the relevant syndrome or its key symptom at baseline. LASSO logistic regressions were applied to identify which of the 102 baseline variables predicted new onsets of each syndrome.
There were 1595 (1.2%), 296 (0.2%) and 692 (0.5%) new onsets of IBS, CFS, and FM, respectively. LASSO logistic regression selected 26, 7 and 19 predictors for IBS, CFS and FM, respectively. Four predictors were shared by all three syndromes, four predicted IBS and FM and two predicted IBS and CFS but 28 predictors were specific to a single syndrome. CFS was more distinct from IBS and FM, which predicted each other.
Syndrome-specific predictors were more common than shared ones and these predictors might form a better starting point to unravel the heterogeneous etiologies of these syndromes than the current approach based on symptom patterns. The close relationship between IBS and FM is striking and requires further research.
In mental health research, functional recovery is increasingly valued as an important outcome in addition to symptomatic remission.
Course types of functional limitations among depressed older patients and its relation with symptomatic remission were explored in a naturalistic cohort study (Netherlands Study of Depression in Older persons). 378 depressed older patients (≥60 years) and 132 non-depressed persons were included. Depressive disorders were assessed with Composite International Diagnostic Interview at baseline and two-year follow-up. Functional limitations were assessed every 6 months with the World Health Organization Disability Assessment II.
Depressed patients had more functional limitations compared to their non-depressed counterparts. Growth Mixture Modeling among depressed patients identified two trajectories of functional limitations, both starting at a high disability level. The largest subgroup (81.2%) was characterized by a course of high disability levels over time. The smaller subgroup (18.8%) had an improving course (functional recovery). After two years, the main predictor of functional recovery was the remission of depression. Among symptomatic remitted patients, female sex, higher level of education, higher gait speed, and less severe depression were associated with no functional recovery. Non-remitted patients without functional recovery were characterized by the presence of more chronic somatic diseases, a lower sense of mastery, and a higher level of anxiety.
1 in 5 depressed older patients have a course with functional recovery. Combining functional and symptomatic recovery points to a subgroup of older patients that might profit from more rigorous psychiatric treatment targeted at psychiatric comorbidity and a group of frail depressed older patients that might profit from integrated geriatric rehabilitation.
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.
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.
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.
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.
The patterns of comorbidity among mental disorders have led researchers to model the underlying structure of psychopathology. While studies have suggested a structure including internalizing and externalizing disorders, less is known with regard to the cross-national stability of this model. Moreover, little data are available on the placement of eating disorders, bipolar disorder and psychotic experiences (PEs) in this structure.
We evaluated the structure of mental disorders with data from the World Health Organization Composite International Diagnostic Interview, including 15 lifetime mental disorders and six PEs. Respondents (n = 5478–15 499) were included from 10 high-, middle- and lower middle-income countries across the world aged 18 years or older. Confirmatory factor analyses (CFAs) were used to evaluate and compare the fit of different factor structures to the lifetime disorder data. Measurement invariance was evaluated with multigroup CFA (MG-CFA).
A second-order model with internalizing and externalizing factors and fear and distress subfactors best described the structure of common mental disorders. MG-CFA showed that this model was stable across countries. Of the uncommon disorders, bipolar disorder and eating disorder were best grouped with the internalizing factor, and PEs with a separate factor.
These results indicate that cross-national patterns of lifetime common mental-disorder comorbidity can be explained with a second-order underlying structure that is stable across countries and can be extended to also cover less common mental disorders.
Depressive patients can present with complex and different symptom patterns in clinical care. Of these, some may report patterns that are inconsistent with typical patterns of depressive symptoms. This study aimed to evaluate the validity of person-fit statistics to identify inconsistent symptom reports and to assess the clinical usefulness of providing clinicians with person-fit score feedback during depression assessment.
Inconsistent symptom reports on the Inventory of Depressive Symptomatology Self-Report (IDS-SR) were investigated quantitatively with person-fit statistics for both intake and follow-up measurements in the Groningen University Center of Psychiatry (n = 2036). Subsequently, to investigate the causes and clinical usefulness of on-the-fly person-fit alerts, qualitative follow-up assessments were conducted with three psychiatrists about 20 of their patients that were randomly selected.
Inconsistent symptom reports at intake (12.3%) were predominantly characterized by reporting of severe symptoms (e.g. psychomotor slowing) without mild symptoms (e.g. irritability). Person-fit scores at intake and follow-up were positively correlated (r = 0.45). Qualitative interviews with psychiatrists resulted in an explanation for the inconsistent response behavior (e.g. complex comorbidity, somatic complaints, and neurological abnormalities) for 19 of 20 patients. Psychiatrists indicated that if provided directly after the assessment, a person-fit alert would have led to new insights in 60%, and be reason for discussion with the patient in 75% of the cases.
Providing clinicians with automated feedback when inconsistent symptom reports occur is informative and can be used to support clinical decision-making.
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