Association of CYP2D6 and CYP2C19 metabolizer status with switching and discontinuing antidepressant drugs: an exploratory study.

BACKGROUND: Tailoring antidepressant drugs (AD) to patients' genetic drug-metabolism profile is promising. However, literature regarding associations of ADs' treatment effect and/or side effects with drug metabolizing genes CYP2D6 and CYP2C19 has yielded inconsistent results. Therefore, our aim was to longitudinally investigate associations between CYP2D6 (poor, intermediate, and normal) and CYP2C19 (poor, intermediate, normal, and ultrarapid) metabolizer-status, and switching/discontinuing of ADs. Next, we investigated whether the number of perceived side effects differed between metabolizer statuses. METHODS: Data came from the multi-site naturalistic longitudinal cohort Netherlands Study of Depression and Anxiety (NESDA). We selected depression- and/or anxiety patients, who used AD at some point in the course of the 9 years follow-up period (n = 928). Medication use was followed to assess patterns of AD switching/discontinuation over time. CYP2D6 and CYP2C19 alleles were derived using genome-wide data of the NESDA samples and haplotype data from the PharmGKB database. Logistic regression analyses were conducted to investigate the association of metabolizer status with switching/discontinuing ADs. Mann-Whitney U-tests were conducted to compare the number of patient-perceived side effects between metabolizer statuses. RESULTS: No significant associations were observed of CYP metabolizer status with switching/discontinuing ADs, nor with the number of perceived side effects. CONCLUSIONS: We found no evidence for associations between CYP metabolizer statuses and switching/discontinuing AD, nor with side effects of ADs, suggesting that metabolizer status only plays a limited role in switching/discontinuing ADs. Additional studies with larger numbers of PM and UM patients are needed to further determine the potential added value of pharmacogenetics to guide pharmacotherapy.

A blended module (STAIRS) to promote functional and personal recovery in patients with a major depressive disorder in remission: study protocol of a concurrent mixed methods randomized controlled trial.

BACKGROUND: Despite the availability of a wide variety of evidence-based treatments for major depressive disorder (MDD), many patients still experience impairments in their lives after remission. Programs are needed that effectively support patients in coping with these impairments. The program Storytelling and Training to Advance Individual Recovery Skills (STAIRS) was developed to address this need and combines the use of peer contact, expert-by-experience guidance, family support and professional blended care. The aim of the planned study is (1) to assess the efficacy of the STAIRS program in patients with remitted MDD, (2) to investigate patients' subjective experiences with STAIRS, and (3) to evaluate the program's cost-effectiveness. METHODS: A concurrent mixed-methods randomized controlled trial design will be used. Patients aged between 18 and 65 years with remitted MDD (N = 140) will be randomized to either a group receiving care as usual (CAU) + the STAIRS-program or a control group receiving CAU + some basic psychoeducation. Quantitative efficacy data on functional and personal recovery and associated aspects will be collected using self-report questionnaires at the start of the intervention, immediately following the intervention, and at the six-month follow-up. Insights into patients' experiences on perceived effects and the way in which different program elements contribute to this effect, as well as the usability and acceptability of the program, will be gained by conducting qualitative interviews with patients from the experimental group, who are selected using maximum variation sampling. Finally, data on healthcare resource use, productivity loss and quality of life will be collected and analysed to assess the cost-effectiveness and cost-utility of the STAIRS-program. DISCUSSION: Well-designed recovery-oriented programs for patients suffering from MDD are scarce. If efficacy and cost-effectiveness are demonstrated with this study and patients experience the STAIRS program as usable and acceptable, this program can be a valuable addition to CAU. The qualitative interviews may give insights into what works for whom, which can be used to promote implementation. TRIAL REGISTRATION: This trial was registered at ClinicalTrials.gov on 1 July 2021, registration number NCT05440812.

Predictors of new onsets of irritable bowel syndrome, chronic fatigue syndrome and fibromyalgia: the lifelines study.

BACKGROUND: 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). METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Female sex and femininity independently associate with common somatic symptom trajectories.

BACKGROUND: Multiple predictors have been associated with persistent somatic symptoms. However, previous studies problematically defined the persistence of symptoms, conflated participants' sex and gender, and focused on patient populations. Therefore, we studied associations between predictors, especially sex and gender, and longitudinal patterns of somatic symptoms in the general adult population. We also assessed whether predictors for persisting symptoms differ between sexes. METHOD: To identify developmental trajectories of somatic symptoms, assessed by the SCL-90 SOM, we used latent class trajectory modeling in the Dutch Lifelines Cohort Study [N = 150 494; 58.6% female; median time to follow-up: 46.0 (min-max: 22.0-123.0) months]. To identify predictors of trajectories, we applied multiple logistic regression analyses. Predictors were measured by surveys at baseline and a composite gender index was previously developed. RESULTS: A five-class linear LCGA model fitted the data best: 93.7% of the population had a stable symptom trajectory, whereas 1.5% and 4.8% of the population had a consistently increasing or decreasing symptom trajectory, respectively. Female sex predicted severe, stable symptom severity (OR 1.74, 95% CI 1.36-2.22), but not increasing symptom severity (OR 1.15, 95% CI 0.99-1.40). Femininity was protective hereof (OR 0.60, 95% CI 0.44-0.82 and OR 0.66, 95% CI 0.51-0.85, respectively). Merely a few predictors of symptom severity, for instance hours of paid employment and physical functioning, differed in strength between sexes. Yet, effect sizes were small. CONCLUSION: Female sex and femininity predict symptom trajectories. No large sex differences in the strength of additional predictors were found, thus it may not be clinically useful to distinguish between predictors specific to male or female patients of persistent somatic symptoms.

Investigating data-driven biological subtypes of psychiatric disorders using specification-curve analysis.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Data-driven biological subtypes of depression: systematic review of biological approaches to depression subtyping.

Research into major depressive disorder (MDD) is complicated by population heterogeneity, which has motivated the search for more homogeneous subtypes through data-driven computational methods to identify patterns in data. In addition, data on biological differences could play an important role in identifying clinically useful subtypes. This systematic review aimed to summarize evidence for biological subtypes of MDD from data-driven studies. We undertook a systematic literature search of PubMed, PsycINFO, and Embase (December 2018). We included studies that identified (1) data-driven subtypes of MDD based on biological variables, or (2) data-driven subtypes based on clinical features (e.g., symptom patterns) and validated these with biological variables post-hoc. Twenty-nine publications including 24 separate analyses in 20 unique samples were identified, including a total of ~ 4000 subjects. Five out of six biochemical studies indicated that there might be depression subtypes with and without disturbed neurotransmitter levels, and one indicated there might be an inflammatory subtype. Seven symptom-based studies identified subtypes, which were mainly determined by severity and by weight gain vs. loss. Two studies compared subtypes based on medication response. These symptom-based subtypes were associated with differences in biomarker profiles and functional connectivity, but results have not sufficiently been replicated. Four out of five neuroimaging studies found evidence for groups with structural and connectivity differences, but results were inconsistent. The single genetic study found a subtype with a distinct pattern of SNPs, but this subtype has not been replicated in an independent test sample. One study combining all aforementioned types of data discovered a subtypes with different levels of functional connectivity, childhood abuse, and treatment response, but the sample size was small. Although the reviewed work provides many leads for future research, the methodological differences across studies and lack of replication preclude definitive conclusions about the existence of clinically useful and generalizable biological subtypes.

Validation of the Dutch version of the Multidimensional Adolescent Functioning Scale (MAFS).

BACKGROUND: The Multidimensional Adolescent Functioning Scale (MAFS) is a 23-item, self-report questionnaire assessing psychosocial functioning in adolescents aged 12-17 years. It captures three domains of functioning: 'general functioning', 'family-related functioning', and 'peer-related functioning'. The original English version has good psychometric properties. The aim of the current paper was to translate the MAFS to Dutch and to investigate the psychometric properties of this translation. METHODS: After translation, the Dutch MAFS was assessed in 397 adolescents aged 12-17 years, assessed at schools. Internal consistency, factor structure and correlations with other questionnaires assessing functioning, psychopathology and well-being were investigated. RESULTS: A hierarchical/bifactor model with a general factor that loads on all items (MAFS-general) and three group factors, loading respectively on the GF, FF and PF items, was found to describe the data best. Internal consistency of the MAFS total score (α = 0.87) was good and of the subscales (α = 0.74-0.80) acceptable. Comparable alphas were found in males and females. Correlations between MAFS subscales ranged from 0.33 to 0.43, indicating sufficient differentiation. The MAFS general factor score and group factor scores showed positive correlations with other measures of good functioning and well-being, and negative correlations with measures of psychopathology, supporting convergent and divergent validity. CONCLUSIONS: The Dutch translation of the MAFS has adequate psychometric properties to assess three domains of functioning in adolescents from the general population aged 12-17 years. The MAFS is freely accessible in the Appendix and easy to administer.

Mental health stigma and mental health knowledge in Chinese population: a cross-sectional study.

BACKGROUND: Little is known about the public stigma on mental illness and mental health knowledge (MHK) in China, public stigma and low MHK can negatively affect patients' health and increase the burden of mental disorders on society. This study aimed at investigating the rates of stigma and MHK, the correlates of stigma and MHK, and the association between MHK and stigma among a Chinese population. METHODS: The data is from the Tianjin Mental Health Survey (TJMHS), which involved a large and a representative sample of adult community residents in the Chinese municipality of Tianjin (n = 11,748). In a 12% random subsample (n = 1775) the Perceived Discrimination and Devaluation scale (PDD) and a Mental Health Knowledge Questionnaire (MHKQ) were administered. First, percentages of the responses to the individual items of the PDD and MHKQ were investigated. Second, sociodemographic correlates of PDD and MHK, and the association between stigma and MHK were investigated. RESULTS: We found that a sizable proportion of participants responded that others would hold a negative attitude towards (former) mental patients, especially with regard to engaging in closer personal relationships. Most people were not familiar about the causes, treatments and prevention of mental illness. Resident area, age, education level, Per capita family income and employment status were related to devaluation score and MHKQ score. MHK was negatively associated with public stigma. CONCLUSIONS: There is room for improvement with regard to levels of public stigma and MHK in China. Providing psychoeducation to improve public MHK could also contribute to reduction of public stigma.

Mobile Mental Health Apps in China: Systematic App Store Search.

BACKGROUND: Smartphones have become ubiquitous in China, offering a promising way to deliver mental health interventions; however, little is known about the current use and characteristics of smartphone apps for mental health. OBJECTIVE: The purpose of this study was to gain insight into mobile mental health apps available in China as of December 2018. METHODS: A systematic search was conducted to identify and evaluate the most downloaded apps from iOS and Android platforms. Apps were categorized according to their main purpose and downloaded to evaluate their content. Each app's affiliation, cost, target users, information security, and evidence-based nature were evaluated. RESULTS: Of the 172 unique apps that were identified, there were 37 apps (21.5%) for psychological counseling, 50 apps (29.1%) for assessment, 12 apps (7.0%) to relieve stress, 24 apps (14.0%) for psychoeducation, and 49 (28.4%) multipurpose apps (ie, a combination of counseling and assessment). Most apps were developed for adults in the general population (166/172, 96.5%), rather than for psychiatric patients. App-based counseling was mostly provided by psychologists, and of the assessed apps, only 40% (70/172) used evidence-based scales to assess mental health problems such as anxiety or depressed mood. Guided meditation was used as the main technique in stress-relieving apps. CONCLUSIONS: Many apps contained useful and evidence-based elements, such as good quality information, validated measurements, and useful meditation methods; however, for mobile apps to contribute significantly to mental health care in China, considerable challenges remain, including the need for more patient-focused apps that can actually take on the role of a health care provider. In addition, efficacy studies are needed.

Spontaneous discontinuation of distressing auditory verbal hallucinations in a school-based sample of adolescents: a longitudinal study.

Auditory verbal hallucinations (AVH) can be transiently present in both clinical and healthy adolescent populations. It is not yet fully understood why AVH discontinue in some adolescents and persist in others. The aim of this explorative study is to investigate predictors of spontaneous discontinuation of distressing AVH in a school-based sample of adolescents. 1841 adolescents (mean age 12.4 years, 58% female) completed self-report questionnaires at baseline. The current study included 123 adolescents (7%; 63% female) who reported at least mild distressing AVH at baseline and completed follow-up measurements. LASSO analyses were used to uncover predictors of spontaneous discontinuation of distressing AVH. During follow-up, 43 adolescents (35%) reported having experienced distressing AVH during the last 12 months, while 80 adolescents did not. Spontaneous discontinuation of distressing AVH was predicted by never having used cannabis, parents not being divorced in the past year, never having been scared by seeing a deceased body, less prosocial behaviour, school grade repetition, having the feeling that others have it in for you, having anxiety when meeting new people, having lived through events exactly as if they happened before and having the feeling as if parts of the body have changed. No associations between spontaneous discontinuation of distressing AVH and age or ethnicity were found. Distressing AVH in non-clinical adolescents are mostly transient. Discontinuation was predicted up to a certain extent. However, several predictors were difficult to interpret and do not provide leads for preventive measures, except for discouraging cannabis use.

Correction to: Spontaneous discontinuation of distressing auditory verbal hallucinations in a school­based sample of adolescents: a longitudinal study.

In the Original Publication Table 2 was incorrectly published. The correct table is given below.

Biomarker-based subtyping of depression and anxiety disorders using Latent Class Analysis. A NESDA study.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Help-seeking behaviors among Chinese people with mental disorders: a cross-sectional study.

BACKGROUND: Failure to seek treatment for mental health disorders is a serious public health concern. Unfortunately, there is little insight into help-seeking and its associated factors in China which has undergone rapid economic development in the past 30 years and has an increasing prevalence of mental disorder. Therefore, this study aimed to (1) investigate help-seeking rates in healthcare and non-healthcare settings and (2) investigate the correlates of help-seeking behavior in a large Chinese survey. METHODS: Data came from the Tianjin Mental Health Survey (TJMHS), a representative sample of adult community residents in the Chinese municipality of Tianjin (n = 11,748). Of these, 1759 individuals had ≥1 axis-I diagnosis according to the Diagnostic and Statistical manual- fourth edition (DSM-IV) and were administered a Help-Seeking Questionnaire. RESULTS: 15.7% of patients reported that they had ever sought help during their entire lifetime before the interview, with 4.5% seeking help in mental healthcare, 3.2% in other healthcare and 8.1% in non-healthcare settings (e.g., family, friends, and spiritual advisor). Among help-seekers, the first help was mostly sought in non-healthcare settings (58.4%), followed by healthcare (27.5%) and mental healthcare settings (24.5%). Female gender, younger age, having 7-9 years vs 0-6 years of education, a low income, a psychotic disorder and having ≥2 disorders were associated with increased help-seeking. Older age, being married and having a psychotic or organic disorder were associated with increased help-seeking in healthcare vs. non-healthcare settings. CONCLUSION: A small percentage of persons with mental disorders in the Tianjin region seek help and among those who do, variations in the types of help-seeking may be partially explained by demographic and clinical characteristics.

Person-fit feedback on inconsistent symptom reports in clinical depression care.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: Providing clinicians with automated feedback when inconsistent symptom reports occur is informative and can be used to support clinical decision-making.

The cross-national structure of mental disorders: results from the World Mental Health Surveys.

BACKGROUND: 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. METHODS: 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). RESULTS: 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. CONCLUSIONS: 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.

Using bundle embeddings to predict daily cortisol levels in human subjects.

BACKGROUND: Many biological variables sampled from human subjects show a diurnal pattern, which poses special demands on the techniques used to analyze such data. Furthermore, most biological variables belong to nonlinear dynamical systems, which may make linear statistical techniques less suitable to analyze their dynamics. The current study investigates the usefulness of two analysis techniques based on nonlinear lagged vector embeddings: sequentially weighted global linear maps (SMAP), and bundle embeddings. METHODS: Time series of urinary cortisol were collected in 10 participants, in the morning ('night' measurement) and the evening ('day' measurement), resulting in 126 consecutive measurements. These time series were used to create lagged vector embeddings, which were split into 'night' and 'day' bundle embeddings. In addition, embeddings were created based on time series that were corrected for the average time-of-day (TOD) values. SMAP was used to predict future values of cortisol in these embeddings. Global (linear) and local (non-linear) predictions were compared for each embedding. Bootstrapping was used to obtain confidence intervals for the model parameters and the prediction error. RESULTS: The best cortisol predictions were found for the night bundle embeddings, followed by the full embeddings and the time-of-day corrected embeddings. The poorest predictions were found for the day bundle embeddings. The night bundle embeddings, the full embeddings and the TOD-corrected embeddings all showed low dimensions, indicating the absence of dynamical processes spanning more than one day. The dimensions of the day bundles were higher, indicating the presence of processes spanning more than one day, or a higher amount of noise. In the full embeddings, local models gave the best predictions, whereas in the bundles the best predictions were obtained from global models, indicating potential nonlinearity in the former but not the latter. CONCLUSIONS: Using a bundling approach on time series of cortisol may reveal differences between the predictions of night and day cortisol that are difficult to find with conventional time-series methods. Combination of this approach with SMAP may especially be useful when analyzing time-series data with periodic components.

Seeing the signs: Using the course of residual depressive symptomatology to predict patterns of relapse and recurrence of major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) is characterized by high relapse/recurrence rates. Predicting individual patients' relapse/recurrence risk has proven hard, possibly due to course heterogeneity among patients. This study aimed to (1) identify homogeneous data-driven subgroups with different patterns of relapse/recurrence and (2) identify associated predictors. METHODS: For a year, we collected weekly depressive symptom ratings in 213 primary care MDD patients. Latent class growth analyses (LCGA), based on symptom-severity during the 24 weeks after no longer fulfilling criteria for the initial major depressive episode (MDE), were used to identify groups with different patterns of relapse/recurrence. Associations of baseline predictors with these groups were investigated, as were the groups' associations with 3- and 11-year follow-up depression outcomes. RESULTS: LCGA showed that heterogeneity in relapse/recurrence after no longer fulfilling criteria for the initial MDE was best described by four classes: "quick symptom decline" (14.0%), "slow symptom decline" (23.3%), "steady residual symptoms" (38.7%), and "high residual symptoms" (24.1%). The latter two classes showed lower self-esteem at baseline, and more recurrences and higher severity at 3-year follow-up than the first two classes. Moreover, the high residual symptom class scored higher on neuroticism and lower on extraversion and self-esteem at baseline. Interestingly, the steady residual symptoms and high residual symptoms classes still showed higher severity of depressive symptoms after 11 years. CONCLUSION: Some measures were associated with specific patterns of relapse/recurrence. Moreover, the data-driven relapse/recurrence groups were predictive of long-term outcomes, suggesting that patterns of residual symptoms could be of prognostic value in clinical practice.

The Prognostic Effect of Physical Health Complaints With New Cardiac Events and Mortality in Patients With a Myocardial Infarction.

BACKGROUND: Self-rated general health has been associated with worse outcome after a myocardial infarction (MI). Previously, however, concurrent depression or anxiety was not taken into account. OBJECTIVE: To evaluate the effect of physical health complaints post-MI on cardiac prognosis adjusting for cardiac disease severity, depression, and anxiety. METHODS: The somatic subscale of the Health Complaints Scale was administered to 424 patients with MI at 3 and 12 months post-MI. Types and trajectories of health complaints were identified with latent transition analysis. The prognostic effect of Health Complaints Scale sum-score at 3 months, and of types and trajectories of health complaints on combined end points (new cardiac events and mortality) was evaluated with Cox regression. Adjustments were made for age, sex, education level, living alone, history of MI, left ventricular ejection fraction, depressive symptoms, and generalized anxiety disorder. RESULTS: Overall, 189 (44.9%) patients with MI had a cardiac event or died during a mean follow-up of 5.7 (3.1) years. In the fully adjusted model, Health Complaints Scale sum-score predicted outcome (hazard ratio [HR] = 1.02 [95% CI: 1.00-1.05]). Latent transition analysis distinguished 5 groups at 3 and 12 months characterized by (1) no/minimal complaints, (2) cardiac complaints, (3) lack of energy, (4) sleep problems, and (5) mixed health complaints, resulting in 25 transition classes. Patients with cardiac and energy complaints at 3 months (HRcardiac = 1.55 [1.15-2.10] and HRenergy = 1.35[1.00-1.81]) and those with new or persistent cardiac, energy, and mixed complaints over time had a worse prognosis (HRcardiac = 1.55 [1.11-2.16], HRmixed = 1.71 [1.19-2.47], and HRenergy = 1.51 [1.09-2.08]). CONCLUSIONS: Physical health complaints are predictors of cardiac outcome independent from cardiac disease, depression, and anxiety. Type and trajectories of health complaints may have additional prognostic significance.

Using State Space Methods to Reveal Dynamical Associations Between Cortisol and Depression.

Despite extensive research, the link between etiological factors and depression remains poorly understood. This may in part be due to a focus on strictly linear definitions of causality, derived at the group level. However, etiological relations in depression are likely to be dynamical, nonlinear and potentially unquantifiable with traditional statistics. Therefore the aim of this study was to evaluate the use of the convergent cross-mapping (CCM) method in investigating possible nonlinear relationships between supposed etiological factors and depressive symptomatology. Time series data from six healthy individuals were used to model the relationship between 24-h urinary free cortisol and negative affect using CCM and dewdrop embeddings. CCM is a nonlinear measure of causality, based on state space reconstruction with lagged coordinate embeddings. The results showed that nonlinear dynamical relationships between cortisol and negative affect may be present within participants, as demonstrated by a positive cross-map convergence from negative affect to cortisol. However, analyses also showed that noise and influential points had considerable impact on the results. Convergent crossmapping can be used to reveal possible nonlinear dynamical relationships between etiological factors and psychopathology that may remain undetected with traditional linear causality measures.