Prevalence of internalizing disorders, symptoms, and traits across age using advanced nonlinear models.

BACKGROUND: Most epidemiological studies show a decrease of internalizing disorders at older ages, but it is unclear how the prevalence exactly changes with age, and whether there are different patterns for internalizing symptoms and traits, and for men and women. This study investigates the impact of age and sex on the point prevalence across different mood and anxiety disorders, internalizing symptoms, and neuroticism. METHODS: We used cross-sectional data on 146 315 subjects, aged 18-80 years, from the Lifelines Cohort Study, a Dutch general population sample. Between 2012 and 2016, five current internalizing disorders - major depression, dysthymia, generalized anxiety disorder, social phobia, and panic disorder - were assessed according to DSM-IV criteria. Depressive symptoms, anxiety symptoms, neuroticism, and negative affect (NA) were also measured. Generalized additive models were used to identify nonlinear patterns across age, and to investigate sex differences. RESULTS: The point prevalence of internalizing disorders generally increased between the ages of 18 and 30 years, stabilized between 30 and 50, and decreased after age 50. The patterns of internalizing symptoms and traits were different. NA and neuroticism gradually decreased after age 18. Women reported more internalizing disorders than men, but the relative difference remained stable across age (relative risk ~1.7). CONCLUSIONS: The point prevalence of internalizing disorders was typically highest between age 30 and 50, but there were differences between the disorders, which could indicate differences in etiology. The relative gap between the sexes remained similar across age, suggesting that changes in sex hormones around the menopause do not significantly influence women's risk of internalizing disorders.

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.

Gender Differences in Developing Biomarker-Based Major Depressive Disorder Diagnostics.

The identification of biomarkers associated with major depressive disorder (MDD) holds great promise to develop an objective laboratory test. However, current biomarkers lack discriminative power due to the complex biological background, and not much is known about the influence of potential modifiers such as gender. We first performed a cross-sectional study on the discriminative power of biomarkers for MDD by investigating gender differences in biomarker levels. Out of 28 biomarkers, 21 biomarkers were significantly different between genders. Second, a novel statistical approach was applied to investigate the effect of gender on MDD disease classification using a panel of biomarkers. Eleven biomarkers were identified in men and eight in women, three of which were active in both genders. Gender stratification caused a (non-significant) increase of Area Under Curve (AUC) for men (AUC = 0.806) and women (AUC = 0.807) compared to non-stratification (AUC = 0.739). In conclusion, we have shown that there are differences in biomarker levels between men and women which may impact accurate disease classification of MDD when gender is not taken into account.

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.

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.

Effectiveness of psychotherapy for treatment-resistant depression: a meta-analysis and meta-regression.

Despite substantial advances in treatment and management strategies for major depression, less than 50% of patients respond to first-line antidepressant treatment or psychotherapy. Given the growing number of controlled studies of psychotherapy for treatment-resistant depression (TRD) and the preference for psychotherapy of depressed subjects as a treatment option, we conducted a meta-analysis and meta-regression analysis to investigate the effectiveness of psychotherapy for TRD. Seven different psychotherapies were studied in 21 trials that included a total of 25 comparisons. In three comparisons of psychotherapy v. treatment as usual (TAU) we found no evidence to conclude that there is a significant benefit of psychotherapy as compared with TAU. In 22 comparisons of add-on psychotherapy plus TAU v. TAU only, we found a moderate general effect size of 0.42 (95% CI 0.29-0.54) in favor of psychotherapy plus TAU. The meta-regression provided evidence for a positive association between baseline severity as well as group v. individual therapy format with the treatment effect. There was no evidence for publication bias. Most frequent investigated treatments were cognitive behavior therapy, interpersonal psychotherapy, mindfulness-based cognitive therapy, and cognitive behavioral analysis system of psychotherapy. Our meta-analysis provides evidence that, in addition to pharmacological and neurostimulatory treatments, the inclusion of add-on of psychotherapy to TAU in guidelines for the treatment of TRD is justified and will provide better outcomes for this difficult-to-treat population.

Spin in RCTs of anxiety medication with a positive primary outcome: a comparison of concerns expressed by the US FDA and in the published literature.

OBJECTIVES: This study aimed to determine the presence of spin in papers on positive randomised clinical trials (RCTs) of antidepressant medication for anxiety disorders by comparing concerns expressed in the Food and Drug Administration (FDA) reviews with those expressed in the published paper. METHODS: For every positive anxiety medication trial with a matching publication (n=41), two independent reviewers identified the concerns raised in the US FDA reviews and those in the published literature. Spin was identified when concerns or limitations were expressed by the FDA (about the efficacy of the study drug) but not in the corresponding published paper. Concerns mentioned in the papers but not by the FDA were scored as 'non-FDA' concerns. FINDINGS: Only six out of 35 (17%) of the FDA concerns pertaining to drug efficacy were reported in the papers. Two papers mentioned a concern that fit the FDA categories, but was not mentioned in the corresponding FDA review. Eighty-seven non-FDA concerns were counted, which often reflected general concerns or concerns related to the study design. CONCLUSIONS: Results indicate the presence of substantial spin in the clinical trial literature on drugs for anxiety disorders. In papers describing RCTs on anxiety medication, the concerns raised by the authors differed from those raised by the FDA. Published papers mentioned a large number of generic concerns about RCTs, such as a lack of long-term research and limited generalisability, while they mentioned few concerns about drug efficacy. These results warrant the promotion of independent statistical review, reporting of patient-level data, more study of spin, and an increased expectation that authors report FDA concerns.

Bias in the reporting of harms in clinical trials of second-generation antidepressants for depression and anxiety: A meta-analysis.

Previous research has shown that reporting bias has inflated the apparent efficacy of antidepressants. We investigated whether apparent safety was also affected. We included 133 trials, involving 31,296 patients, of second-generation antidepressants for the treatment of major depressive disorder (MDD) or anxiety disorders, obtained from Food and Drug Administration (FDA) reviews. We extracted data on overall discontinuation, discontinuation due to adverse events, and serious adverse events (SAEs). Meta-analysis was used to compare discontinuation rates between FDA reviews and matching journal articles, while SAEs were compared qualitatively. The odds ratio for overall discontinuation, comparing drug to placebo, was 1.0 for both sources, while that for discontinuation due to adverse events was 2.4 for both sources. Seventy-seven of 97 (79%) journal articles provided incomplete information on SAEs; sixty-one (63%) articles made no mention of SAEs at all. Of 21 articles which could be compared to the FDA, only 6 (29%) had full reporting without discrepancies. Nine (43%) articles reported a discrepant number of SAEs. Descriptions were absent or discrepant in 6 (29%) additional articles, even for important SAEs such as suicide attempts. In conclusion, reporting bias has not affected average discontinuation rates over trials. However, SAE reporting is not only very poor, with over half of articles failing to discuss SAEs altogether, but discrepancies between the FDA and articles were common and often led to a more favorable drug-placebo comparison. These findings suggest that journal articles are an unreliable source of data on SAEs in antidepressant trials.