To send content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about sending content to .
To send content items to your Kindle, first ensure email@example.com
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about sending to your Kindle.
Note you can select to send to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
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.
Email your librarian or administrator to recommend adding this to your organisation's collection.