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Inflammation and metabolic dysregulation are age-related physiological changes and are associated with depressive disorder. We tried to identify subgroups of depressed older patients based on their metabolic-inflammatory profile and examined the course of depression for these subgroups.
This clinical cohort study was conducted in a sample of 364 depressed older (⩾60 years) patients according to DSM-IV criteria. Severity of depressive symptoms was monitored every 6 months and a formal diagnostic interview repeated at 2-year follow-up. Latent class analyses based on baseline metabolic and inflammatory biomarkers were performed. Adjusted for confounders, we compared remission of depression at 2-year follow-up between the metabolic-inflammatory subgroups with logistic regression and the course of depression severity over 2-years by linear mixed models.
We identified a ‘healthy’ subgroup (n = 181, 49.7%) and five subgroups characterized by different profiles of metabolic-inflammatory dysregulation. Compared to the healthy subgroup, patients in the subgroup with mild ‘metabolic and inflammatory dysregulation’ (n = 137, 37.6%) had higher depressive symptom scores, a lower rate of improvement in the first year, and were less likely to be remitted after 2-years [OR 0.49 (95% CI 0.26–0.91)]. The four smaller subgroups characterized by a more specific immune-inflammatory dysregulation profile did not differ from the two main subgroups regarding the course of depression.
Nearly half of the patients with late-life depressions suffer from metabolic-inflammatory dysregulation, which is also associated with more severe depression and a worse prognosis. Future studies should examine whether these depressed older patients benefit from a metabolic-inflammatory targeted treatment.
The diagnosis of personality disorder (PD) in individuals with an intellectual disability (ID) is clinically relevant, but there is little research on the subject. Because the impact of PD on both patients and carers is substantial, it is important to increase the understanding of how ID and PD co-occur.
The aim of the present study was to examine the prevalence of all axis II DSM-IV-TR PD diagnoses in a well- defined sample of ID out-patients in two specialized secondary care ou-patient treatment centers, in the Netherlands. Special attention was paid to borderline ID (70≤Total IQ < 85), prevalence of co-morbid axis I disorders and associations of PD with several demographic variables.
The present study was a cross-sectional medical chart review. On January first, 2011, 599 patients were registered at the two centers. Diagnostic information was available for 576 patients. Diagnoses were based on Došen's integrative approach and formulated according to the DM-ID.
194 patients (33.7%) were diagnosed with a PD. PD NOS (19.1%) was most common, followed by borderline PD (8.7%). Patients with borderline ID were most likely to be diagnosed with a PD (43.7%). 85.1% of patients diagnosed with a PD had at least 1 co-morbid axis I diagnosis. PTSD was the most common co-morbid disorder (23.7%).
Diagnosed according to strict criteria, PDs often occur in ID out-patients, mostly together with axis I disorders. Importantly, PDs and PTSD often co-occur in this group, indicating special care needs and signifying an interesting direction for further research.
The mechanisms underlying both depressive and anxiety disorders remain poorly understood. One of the reasons for this is the lack of a valid, evidence-based system to classify persons into specific subtypes based on their depressive and/or anxiety symptomatology. In order to do this without a priori assumptions, non-parametric statistical methods seem the optimal choice. Moreover, to define subtypes according to their symptom profiles and inter-relations between symptoms, network models may be very useful. This study aimed to evaluate the potential usefulness of this approach.
A large community sample from the Canadian general population (N = 254 443) was divided into data-driven clusters using non-parametric k-means clustering. Participants were clustered according to their (co)variation around the grand mean on each item of the Kessler Psychological Distress Scale (K10). Next, to evaluate cluster differences, semi-parametric network models were fitted in each cluster and node centrality indices and network density measures were compared.
A five-cluster model was obtained from the cluster analyses. Network density varied across clusters, and was highest for the cluster of people with the lowest K10 severity ratings. In three cluster networks, depressive symptoms (e.g. feeling depressed, restless, hopeless) had the highest centrality. In the remaining two clusters, symptom networks were characterised by a higher prominence of somatic symptoms (e.g. restlessness, nervousness).
Finding data-driven subtypes based on psychological distress using non-parametric methods can be a fruitful approach, yielding clusters of persons that differ in illness severity as well as in the structure and strengths of inter-symptom relationships.
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.
A substantial proportion of persons with mental disorders seek treatment from complementary and alternative medicine (CAM) professionals. However, data on how CAM contacts vary across countries, mental disorders and their severity, and health care settings is largely lacking. The aim was therefore to investigate the prevalence of contacts with CAM providers in a large cross-national sample of persons with 12-month mental disorders.
In the World Mental Health Surveys, the Composite International Diagnostic Interview was administered to determine the presence of past 12 month mental disorders in 138 801 participants aged 18–100 derived from representative general population samples. Participants were recruited between 2001 and 2012. Rates of self-reported CAM contacts for each of the 28 surveys across 25 countries and 12 mental disorder groups were calculated for all persons with past 12-month mental disorders. Mental disorders were grouped into mood disorders, anxiety disorders or behavioural disorders, and further divided by severity levels. Satisfaction with conventional care was also compared with CAM contact satisfaction.
An estimated 3.6% (standard error 0.2%) of persons with a past 12-month mental disorder reported a CAM contact, which was two times higher in high-income countries (4.6%; standard error 0.3%) than in low- and middle-income countries (2.3%; standard error 0.2%). CAM contacts were largely comparable for different disorder types, but particularly high in persons receiving conventional care (8.6–17.8%). CAM contacts increased with increasing mental disorder severity. Among persons receiving specialist mental health care, CAM contacts were reported by 14.0% for severe mood disorders, 16.2% for severe anxiety disorders and 22.5% for severe behavioural disorders. Satisfaction with care was comparable with respect to CAM contacts (78.3%) and conventional care (75.6%) in persons that received both.
CAM contacts are common in persons with severe mental disorders, in high-income countries, and in persons receiving conventional care. Our findings support the notion of CAM as largely complementary but are in contrast to suggestions that this concerns person with only mild, transient complaints. There was no indication that persons were less satisfied by CAM visits than by receiving conventional care. We encourage health care professionals in conventional settings to openly discuss the care patients are receiving, whether conventional or not, and their reasons for doing so.
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.
To effectively shape mental healthcare policy in modern-day China, up-to-date epidemiological data on mental disorders is needed. The objective was to estimate the prevalence, age-of-onset (AOO) and sociodemographic correlates of mental disorders in a representative household sample of the general population (age ⩾ 18) in the Tianjin Municipality in China.
Data came from the Tianjin Mental health Survey (TJMHS), which was conducted between July 2011 and March 2012 using a two-phase design. 11 748 individuals were screened with an expanded version of the General Health Questionnaire and 4438 subjects were selected for a diagnostic interview by a psychiatrist, using the Structured Clinical Interview for the Diagnostic and Statistical Manual – fourth edition (SCID).
The lifetime and 1-month prevalence of any mental disorder were 23.6% and 12.8%, respectively. Mood disorders (lifetime: 9.3%; 1-month: 3.9%), anxiety disorders (lifetime: 4.5% 1-month: 3.1%) and substance-use disorders (lifetime: 8.8%; 1-month: 3.5%) were most prevalent. The median AOO ranged from 25 years [interquartile range (IQR): 23–32] for substance-use disorders to 36 years (IQR: 24–50) for mood disorders. Not being married, non-immigrant status (i.e. local ‘Hukou’), being a farmer, having <6 years of education and male gender were associated with a higher lifetime prevalence of any mental disorder.
Results from the current survey indicate that mental disorders are steadily reported more commonly in rapidly-developing urban China. Several interesting sociodemographic correlates were observed (e.g. male gender and non-immigrant status) that warrant further investigation and could be used to profile persons in need of preventive intervention.
Although specific phobia is highly prevalent, associated with impairment, and an important risk factor for the development of other mental disorders, cross-national epidemiological data are scarce, especially from low- and middle-income countries. This paper presents epidemiological data from 22 low-, lower-middle-, upper-middle- and high-income countries.
Data came from 25 representative population-based surveys conducted in 22 countries (2001–2011) as part of the World Health Organization World Mental Health Surveys initiative (n = 124 902). The presence of specific phobia as defined by the Diagnostic and Statistical Manual of Mental Disorders, fourth edition was evaluated using the World Health Organization Composite International Diagnostic Interview.
The cross-national lifetime and 12-month prevalence rates of specific phobia were, respectively, 7.4% and 5.5%, being higher in females (9.8 and 7.7%) than in males (4.9% and 3.3%) and higher in high- and higher-middle-income countries than in low-/lower-middle-income countries. The median age of onset was young (8 years). Of the 12-month patients, 18.7% reported severe role impairment (13.3–21.9% across income groups) and 23.1% reported any treatment (9.6–30.1% across income groups). Lifetime co-morbidity was observed in 60.5% of those with lifetime specific phobia, with the onset of specific phobia preceding the other disorder in most cases (72.6%). Interestingly, rates of impairment, treatment use and co-morbidity increased with the number of fear subtypes.
Specific phobia is common and associated with impairment in a considerable percentage of cases. Importantly, specific phobia often precedes the onset of other mental disorders, making it a possible early-life indicator of psychopathology vulnerability.
In search of empirical classifications of depression and anxiety, most subtyping studies focus solely on symptoms and do so within a single disorder. This study aimed to identify and validate cross-diagnostic subtypes by simultaneously considering symptoms of depression and anxiety, and disability measures.
A large cohort of adults (Lifelines, n = 73 403) had a full assessment of 16 symptoms of mood and anxiety disorders, and measurement of physical, social and occupational disability. The best-fitting subtyping model was identified by comparing different hybrid mixture models with and without disability covariates on fit criteria in an independent test sample. The best model's classes were compared across a range of external variables.
The best-fitting Mixed Measurement Item Response Theory model with disability covariates identified five classes. Accounting for disability improved differentiation between people reporting isolated non-specific symptoms [‘Somatic’ (13.0%), and ‘Worried’ (14.0%)] and psychopathological symptoms [‘Subclinical’ (8.8%), and ‘Clinical’ (3.3%)]. Classes showed distinct associations with clinically relevant external variables [e.g. somatization: odds ratio (OR) 8.1–12.3, and chronic stress: OR 3.7–4.4]. The Subclinical class reported symptomatology at subthreshold levels while experiencing disability. No pure depression or anxiety, but only mixed classes were found.
An empirical classification model, incorporating both symptoms and disability identified clearly distinct cross-diagnostic subtypes, indicating that diagnostic nets should be cast wider than current phenomenology-based categorical systems.
Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful.
We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalised) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments.
Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention v. control) or differential treatment outcomes (i.e., intervention A v. intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalised treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials.
Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.
It has been suggested that the structure of psychopathology is best described as a complex network of components that interact in dynamic ways. The goal of the present paper was to examine the concept of psychopathology from a network perspective, combining complementary top-down and bottom-up approaches using momentary assessment techniques.
A pooled Experience Sampling Method (ESM) dataset of three groups (individuals with a diagnosis of depression, psychotic disorder or no diagnosis) was used (pooled N = 599). The top-down approach explored the network structure of mental states across different diagnostic categories. For this purpose, networks of five momentary mental states (‘cheerful’, ‘content’, ‘down’, ‘insecure’ and ‘suspicious’) were compared between the three groups. The complementary bottom-up approach used principal component analysis to explore whether empirically derived network structures yield meaningful higher order clusters.
Individuals with a clinical diagnosis had more strongly connected moment-to-moment network structures, especially the depressed group. This group also showed more interconnections specifically between positive and negative mental states than the psychotic group. In the bottom-up approach, all possible connections between mental states were clustered into seven main components that together captured the main characteristics of the network dynamics.
Our combination of (i) comparing network structure of mental states across three diagnostically different groups and (ii) searching for trans-diagnostic network components across all pooled individuals showed that these two approaches yield different, complementary perspectives in the field of psychopathology. The network paradigm therefore may be useful to map transdiagnostic processes.
Although variation in the long-term course of major depressive disorder (MDD) is not strongly predicted by existing symptom subtype distinctions, recent research suggests that prediction can be improved by using machine learning methods. However, it is not known whether these distinctions can be refined by added information about co-morbid conditions. The current report presents results on this question.
Data came from 8261 respondents with lifetime DSM-IV MDD in the World Health Organization (WHO) World Mental Health (WMH) Surveys. Outcomes included four retrospectively reported measures of persistence/severity of course (years in episode; years in chronic episodes; hospitalization for MDD; disability due to MDD). Machine learning methods (regression tree analysis; lasso, ridge and elastic net penalized regression) followed by k-means cluster analysis were used to augment previously detected subtypes with information about prior co-morbidity to predict these outcomes.
Predicted values were strongly correlated across outcomes. Cluster analysis of predicted values found three clusters with consistently high, intermediate or low values. The high-risk cluster (32.4% of cases) accounted for 56.6–72.9% of high persistence, high chronicity, hospitalization and disability. This high-risk cluster had both higher sensitivity and likelihood ratio positive (LR+; relative proportions of cases in the high-risk cluster versus other clusters having the adverse outcomes) than in a parallel analysis that excluded measures of co-morbidity as predictors.
Although the results using the retrospective data reported here suggest that useful MDD subtyping distinctions can be made with machine learning and clustering across multiple indicators of illness persistence/severity, replication with prospective data is needed to confirm this preliminary conclusion.
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