Objectives
To systematically compare and pool the prevalence of frailty, including prefrailty, reported in community‐dwelling older people overall and according to sex, age, and definition of frailty ...used.
Design
Systematic review of the literature using the key words elderly, aged, frailty, prevalence, and epidemiology.
Setting
Cross‐sectional data from community‐based cohorts.
Participants
Community‐dwelling adults aged 65 and older.
Measurements
In the studies that were found, frailty and prefrailty were measured according to physical phenotype and broad phenotype, the first defining frailty as a purely physical condition and the second also including psychosocial aspects.
Results
Reported prevalence in the community varies enormously (range 4.0–59.1%). The overall weighted prevalence of frailty was 10.7% (95% confidence interval (CI) = 10.5–10.9; 21 studies; 61,500 participants). The weighted prevalence was 9.9% for physical frailty (95% CI = 9.6–10.2; 15 studies; 44,894 participants) and 13.6% for the broad phenotype of frailty (95% CI = 13.2–14.0; 8 studies; 24,072 participants) (chi‐square (χ2) = 217.7, degrees of freedom (df)=1, P < .001). Prevalence increased with age (χ2 = 6067, df = 1, P < .001) and was higher in women (9.6%, 95% CI = 9.2–10.0%) than in men (5.2%, 95% CI = 4.9–5.5%; χ2 = 298.9 df = 1, P < .001).
Conclusion
Frailty is common in later life, but different operationalization of frailty status results in widely differing prevalence between studies. Improving the comparability of epidemiological and clinical studies constitutes an important step forward.
Although current classification systems have greatly contributed to the reliability of psychiatric diagnoses, they ignore the unique role of individual symptoms and, consequently, potentially ...important information is lost. The network approach, in contrast, assumes that psychopathology results from the causal interplay between psychiatric symptoms and focuses specifically on these symptoms and their complex associations. By using a sophisticated network analysis technique, this study constructed an empirically based network structure of 120 psychiatric symptoms of twelve major DSM-IV diagnoses using cross-sectional data of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC, second wave; N = 34,653). The resulting network demonstrated that symptoms within the same diagnosis showed differential associations and indicated that the strategy of summing symptoms, as in current classification systems, leads to loss of information. In addition, some symptoms showed strong connections with symptoms of other diagnoses, and these specific symptom pairs, which both concerned overlapping and non-overlapping symptoms, may help to explain the comorbidity across diagnoses. Taken together, our findings indicated that psychopathology is very complex and can be more adequately captured by sophisticated network models than current classification systems. The network approach is, therefore, promising in improving our understanding of psychopathology and moving our field forward.
According to current classification systems, patients with major depressive disorder (MDD) may have very different combinations of symptoms. This symptomatic diversity hinders the progress of ...research into the causal mechanisms and treatment allocation. Theoretically founded subtypes of depression such as atypical, psychotic, and melancholic depression have limited clinical applicability. Data-driven analyses of symptom dimensions or subtypes of depression are scarce. In this systematic review, we examine the evidence for the existence of data-driven symptomatic subtypes of depression.
We undertook a systematic literature search of MEDLINE, PsycINFO and Embase in May 2012. We included studies analyzing the depression criteria of the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) of adults with MDD in latent variable analyses.
In total, 1176 articles were retrieved, of which 20 satisfied the inclusion criteria. These reports described a total of 34 latent variable analyses: 6 confirmatory factor analyses, 6 exploratory factor analyses, 12 principal component analyses, and 10 latent class analyses. The latent class techniques distinguished 2 to 5 classes, which mainly reflected subgroups with different overall severity: 62 of 71 significant differences on symptom level were congruent with a latent class solution reflecting severity. The latent class techniques did not consistently identify specific symptom clusters. Latent factor techniques mostly found a factor explaining the variance in the symptoms depressed mood and interest loss (11 of 13 analyses), often complemented by psychomotor retardation or fatigue (8 of 11 analyses). However, differences in found factors and classes were substantial.
The studies performed to date do not provide conclusive evidence for the existence of depressive symptom dimensions or symptomatic subtypes. The wide diversity of identified factors and classes might result either from the absence of patterns to be found, or from the theoretical and modeling choices preceding analysis.
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.
Psilocybin is being studied for use in treatment-resistant depression.
In this phase 2 double-blind trial, we randomly assigned adults with treatment-resistant depression to receive a single dose of ...a proprietary, synthetic formulation of psilocybin at a dose of 25 mg, 10 mg, or 1 mg (control), along with psychological support. The primary end point was the change from baseline to week 3 in the total score on the Montgomery-Åsberg Depression Rating Scale (MADRS; range, 0 to 60, with higher scores indicating more severe depression). Secondary end points included response at week 3 (≥50% decrease from baseline in the MADRS total score), remission at week 3 (MADRS total score ≤10), and sustained response at 12 weeks (meeting response criteria at week 3 and all subsequent visits).
A total of 79 participants were in the 25-mg group, 75 in the 10-mg group, and 79 in the 1-mg group. The mean MADRS total score at baseline was 32 or 33 in each group. Least-squares mean changes from baseline to week 3 in the score were -12.0 for 25 mg, -7.9 for 10 mg, and -5.4 for 1 mg; the difference between the 25-mg group and 1-mg group was -6.6 (95% confidence interval CI, -10.2 to -2.9; P<0.001) and between the 10-mg group and 1-mg group was -2.5 (95% CI, -6.2 to 1.2; P = 0.18). In the 25-mg group, the incidences of response and remission at 3 weeks, but not sustained response at 12 weeks, were generally supportive of the primary results. Adverse events occurred in 179 of 233 participants (77%) and included headache, nausea, and dizziness. Suicidal ideation or behavior or self-injury occurred in all dose groups.
In this phase 2 trial involving participants with treatment-resistant depression, psilocybin at a single dose of 25 mg, but not 10 mg, reduced depression scores significantly more than a 1-mg dose over a period of 3 weeks but was associated with adverse effects. Larger and longer trials, including comparison with existing treatments, are required to determine the efficacy and safety of psilocybin for this disorder. (Funded by COMPASS Pathfinder; EudraCT number, 2017-003288-36; ClinicalTrials.gov number, NCT03775200.).
Network approaches to psychometric constructs, in which constructs are modeled in terms of interactions between their constituent factors, have rapidly gained popularity in psychology. Applications ...of such network approaches to various psychological constructs have recently moved from a descriptive stance, in which the goal is to estimate the network structure that pertains to a construct, to a more comparative stance, in which the goal is to compare network structures across populations. However, the statistical tools to do so are lacking. In this article, we present the network comparison test (NCT), which uses resampling-based permutation testing to compare network structures from two independent, cross-sectional data sets on invariance of (a) network structure, (b) edge (connection) strength, and (c) global strength. Performance of NCT is evaluated in simulations that show NCT to perform well in various circumstances for all three tests: The Type I error rate is close to the nominal significance level, and power proves sufficiently high if sample size and difference between networks are substantial. We illustrate NCT by comparing depression symptom networks of males and females. Possible extensions of NCT are discussed.
Translational AbstractThe network approach, in which psychological constructs are modeled in terms of interactions between their constituent factors, have rapidly gained popularity in psychology. Applications of such network approaches to various psychological constructs have recently moved from a descriptive stance, in which the goal is to estimate the network structure, to a more comparative stance, in which the goal is to compare network structures across groups. However, the statistical tools to do so are lacking. In this article, we present the network comparison test (NCT). NCT is a statistical test that compares two network structures on three types of characteristics. Performance of NCT is evaluated by means of a simulation study. Simulated data shows that NCT performs well in various circumstances for all three tests: when the groups are simulated to be similar, the error rate (i.e., NCT indicating that they are different, while the simulated networks are similar) is adequately low, and when the groups are simulated to be different, the ability to detect a difference is sufficiently high when the difference between simulated networks and the sample size are substantial. We illustrate NCT by comparing depression symptom networks of males and females. Possible extensions of NCT are discussed.
Introduction:
Small-scale clinical studies with psychedelic drugs have shown promising results for the treatment of several mental disorders. Before psychedelics become registered medicines, it is ...important to know the full range of adverse events (AEs) for making balanced treatment decisions.
Objective:
To systematically review the presence of AEs during and after administration of serotonergic psychedelics and 3,4-methyenedioxymethamphetamine (MDMA) in clinical studies.
Methods:
We systematically searched PubMed, PsycINFO, Embase, and ClinicalTrials.gov for clinical trials with psychedelics since 2000 describing the results of quantitative and qualitative studies.
Results:
We included 44 articles (34 quantitative + 10 qualitative), describing treatments with MDMA and serotonergic psychedelics (psilocybin, lysergic acid diethylamide, and ayahuasca) in 598 unique patients. In many studies, AEs were not systematically assessed. Despite this limitation, treatments seemed to be overall well tolerated. Nausea, headaches, and anxiety were commonly reported acute AEs across diagnoses and compounds. Late AEs included headaches (psilocybin, MDMA), fatigue, low mood, and anxiety (MDMA). One serious AE occurred during MDMA administration (increase in premature ventricular contractions requiring brief hospitalization); no other AEs required medical intervention. Qualitative studies suggested that psychologically challenging experiences may also be therapeutically beneficial. Except for ayahuasca, a large proportion of patients had prior experience with psychedelic drugs before entering studies.
Conclusions:
AEs are poorly defined in the context of psychedelic treatments and are probably underreported in the literature due to study design (lack of systematic assessment of AEs) and sample selection. Acute challenging experiences may be therapeutically meaningful, but a better understanding of AEs in the context of psychedelic treatments requires systematic and detailed reporting.
•IL-6 could hold promise as a marker for the prediction of TRD treatment response.•CRP/hsCRP could work as marker for the prediction of treatment response in TRD.•IL-1β, IL-8, IL-10, INF-γ and TNF-α ...were not predictive for TRD treatment response.
A substantial percentage of depressed patients do not respond satisfactorily to conventional antidepressant treatment. This treatment resistant depression (TRD) may be partly related to inflammatory processes in the central nervous system. Accordingly, peripheral inflammatory markers might serve to predict treatment response with novel but still experimental forms of antidepressant treatment.
A literature search on treatment of TRD and inflammatory markers was performed using the PubMed/Medline database on November 8th 2018, and 95 articles were retrieved initially, which were subsequently screened and selected only when the inclusion and exclusion criteria were met.
Ten studies were recruited. In five studies higher baseline interleukin-6 (IL-6) or C-reactive protein (CRP)/high-sensitivity-CRP (hsCRP) in blood predicted better response to medication with anti-inflammatory characteristics, such as ketamine and infliximab. One study found that higher IL-6 predicted worse response to antidepressant treatment in patients with TRD. No evidence was found for the predictive value of other inflammatory markers (e.g., Tumor Necrosis Factor-α, Interferon-γ).
The number of available studies was limited; included studies showed considerable methodological variation and used different definitions for TRD.
The inflammatory markers IL-6 and CRP/hsCRP could hold promise as markers for the prediction of treatment response in TRD. Clearly, this field of research is still far from mature but it could pave the way for novel and efficacious treatments for at least the inflammatory type of TRD with more well-designed studies and more convincing results.
In the past two decades, the study of mood disorder patients using experience sampling methods (ESM) and ecological momentary assessment (EMA) has yielded important findings. In patients with major ...depressive disorder (MDD), the dynamics of their everyday mood have been associated with various aspects of their lives. To some degree similar studies have been conducted in patients with bipolar disorder (BD). In this paper we present the results of a systematic review of all ESM/EMA studies in MDD and BD to date. We focus not only on the correlates of patients' everyday mood but also on the impact on treatment, residual symptoms in remitted patients, on findings in pediatric populations, on MDD/BD specificity, and on links with neuroscience. After reviewing these six topics, we highlight the benefits of ESM/EMA for researchers, clinicians, and patients, and offer suggestions for future studies.
► ESM/EMA is increasingly used to study mood disorder patients. ► Data provide insight into patients' everyday lives and the impact of treatment. ► Pediatric populations and bipolar disorder are understudied. ► Future studies might also combine ESM/EMA with neuroscience methods. ► ESM/EMA may benefit researchers and clinicians as well as patients.
Network analysis is entering fields where network structures are unknown, such as psychology and the educational sciences. A crucial step in the application of network models lies in the assessment ...of network structure. Current methods either have serious drawbacks or are only suitable for Gaussian data. In the present paper, we present a method for assessing network structures from binary data. Although models for binary data are infamous for their computational intractability, we present a computationally efficient model for estimating network structures. The approach, which is based on Ising models as used in physics, combines logistic regression with model selection based on a Goodness-of-Fit measure to identify relevant relationships between variables that define connections in a network. A validation study shows that this method succeeds in revealing the most relevant features of a network for realistic sample sizes. We apply our proposed method to estimate the network of depression and anxiety symptoms from symptom scores of 1108 subjects. Possible extensions of the model are discussed.
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