People with severe mental illness (SMI) -schizophrenia, bipolar disorder and major depressive disorder -appear at risk for cardiovascular disease (CVD), but a comprehensive meta-analysis is lacking. We conducted a large-scale meta-analysis assessing the prevalence and incidence of CVD; coronary heart disease; stroke, transient ischemic attack or cerebrovascular disease; congestive heart failure; peripheral vascular disease; and CVD-related death in SMI patients (N53,211,768) versus controls (N5113,383,368) (92 studies). The pooled CVD prevalence in SMI patients (mean age 50 years) was 9.9% (95% CI: 7.4-13.3). Adjusting for a median of seven confounders, patients had significantly higher odds of CVD versus controls in cross-sectional studies (odds ratio, OR51.53, 95% CI: 1.27-1.83; 11 studies), and higher odds of coronary heart disease (OR51.51, 95% CI: 1.47-1.55) and cerebrovascular disease (OR51.42, 95% CI: 1.21-1.66). People with major depressive disorder were at increased risk for coronary heart disease, while those with schizophrenia were at increased risk for coronary heart disease, cerebrovascular disease and congestive heart failure. Cumulative CVD incidence in SMI patients was 3.6% (95% CI: 2.7-5.3) during a median follow-up of 8.4 years (range 1.8-30.0). Adjusting for a median of six confounders, SMI patients had significantly higher CVD incidence than controls in longitudinal studies (hazard ratio, HR51.78, 95% CI: 1.60-1.98; 31 studies). The incidence was also higher for coronary heart disease (HR51.54, 95% CI: 1.30-1.82), cerebrovascular disease (HR51.64, 95% CI: 1.26-2.14), congestive heart failure (HR52.10, 95% CI: 1.64-2.70), and CVDrelated death (HR51.85, 95% CI: 1.53-2.24). People with major depressive disorder, bipolar disorder and schizophrenia were all at increased risk of CVD-related death versus controls. CVD incidence increased with antipsychotic use (p50.008), higher body mass index (p50.008) and higher baseline CVD prevalence (p50.03) in patients vs. controls. Moreover, CVD prevalence (p50.007), but not CVD incidence (p50.21), increased in more recently conducted studies. This large-scale meta-analysis confirms that SMI patients have significantly increased risk of CVD and CVD-related mortality, and that elevated body mass index, antipsychotic use, and CVD screening and management require urgent clinical attention.Key words: Cardiovascular disease, severe mental illness, schizophrenia, bipolar disorder, major depression, coronary heart disease, cerebrovascular disease, congestive heart failure, premature mortality (World Psychiatry 2017;16:163-180) People with severe mental illness (SMI) -including schizophrenia, bipolar disorder, major depressive disorder, and their related spectrum disorders -have a life expectancy shortened of 10-17.5 years compared to the general population 1,2 . While suicide explains some of this reduced life expectancy 3 , it is now established that physical diseases account for the overwhelming majority of premature mortality 4,5 . Among physical conditions, c...
The literature on non-genetic peripheral biomarkers for major mental disorders is broad, with conflicting results. An umbrella review of meta-analyses of non-genetic peripheral biomarkers for Alzheimer's disease, autism spectrum disorder, bipolar disorder (BD), major depressive disorder, and schizophrenia, including first-episode psychosis. We included meta-analyses that compared alterations in peripheral biomarkers between participants with mental disorders to controls (i.e., between-group meta-analyses) and that assessed biomarkers after treatment (i.e., withingroup meta-analyses). Evidence for association was hierarchically graded using a priori defined criteria against several biases. The Assessment of Multiple Systematic Reviews (AMSTAR) instrument was used to investigate study quality. 1161 references were screened. 110 met inclusion criteria, relating to 359 meta-analytic estimates and 733,316 measurements, on 162 different biomarkers. Only two estimates met a priori defined criteria for convincing evidence (elevated awakening cortisol levels in euthymic BD participants relative to controls and decreased pyridoxal levels in participants with schizophrenia relative to controls). Of 42 estimates which met criteria for highly suggestive evidence only five biomarker aberrations occurred in more than one disorder. Only 15 meta-analyses had a power >0.8 to detect a small effect size, and most (81.9%) meta-analyses had high heterogeneity. Although some associations met criteria for either convincing or highly suggestive evidence, overall the vast literature of peripheral biomarkers for major mental disorders is affected by bias and is underpowered. No convincing evidence supported the existence of a transdiagnostic biomarker. Adequately powered and methodologically sound future large collaborative studies are warranted.
Catatonia is an independent syndrome that co-occurs with several mental and medical conditions. We performed a systematic literature review in PubMed/Scopus until February 2017 and meta-analyzed studies reporting catatonia prevalence. Across 74 studies (cross-sectional = 32, longitudinal = 26, retrospective = 16) providing data collected from 1935 to 2017 across all continents, mean catatonia prevalence was 9.0% (k = 80, n = 110764; 95% CI = 6.9-11.7, I2 = 98%, publication bias P < .01), decreasing to 7.8% (k = 19, n = 7612, 95% CI = 7-8.7, I2 = 38.9%) in a subgroup with low heterogeneity. Catatonia prevalence was 23.9% (k = 8, n = 1168, 95% CI = 10-46.9, I2 = 96%) in patients undergoing ECT/having elevated creatinine phosphokinase. Excluding ECT samples, the catatonia prevalence was 8.1% (k = 72, n = 109606, 95% CI = 6.1-10.5, I2 = 98%, publication bias P < .01), with sensitivity analyses demonstrating that country of study origin (P < .001), treatment setting (P = .003), main underlying condition (P < .001), and sample size (P < .001)moderated catatonia prevalence, being highest in Uganda (48.5%, k = 1) and lowest in Mexico (1.9%, 95% CI = 0.4-8.8, I2 = 67%, k = 2), highest in nonpsychiatric out- or inpatient services (15.8%, 95% CI = 8.1-28.4, I2 = 97%, k = 15)and lowest in psychiatric outpatients services (3.2%, 95% CI = 1.7-6.1, I2 = 50%, k = 3), highest in presence of medical or neurological illness with no comorbid psychiatric condition (20.6%, 95% CI = 11.5-34.2, I2 = 95%, k = 10)and lowest in mixed psychiatric samples (5.7%, 95% CI = 4.2-7.7, I2 =98%, k = 43), highest in studies with sample sizes <100 (20.7%, 95% CI = 12.8-31.6, I2 = 90%, k = 17) and lowest in studies with sample sizes >1000 (2.3%, 95% CI = 1.3-3.9, I2 = 99%, k = 16). Meta-regression showed that smaller sample size (P < .01) and less major depressive disorder (P = .02) moderated higher catatonia prevalence. Year of data collection did not significantly moderate the results. Results from this first meta-analysis of catatonia frequencies across time and disorders suggest that catatonia is an epidemiologically and clinically relevant condition that occurs throughout several mental and medical conditions, whose prevalence has not decreased over time and does not seem to depend on different rating scales/criteria. However, results were highly heterogeneous, calling for a cautious interpretation.
AimThe aim of this study was to summarize the characteristics, efficacy, and safety of vesicular monoamine transporter-2 (VMAT-2) inhibitors for treating tardive dyskinesia (TD).Materials and methodsWe conducted a literature search in PubMed, Cochrane Database, and ClinicalTrials.gov, screening for systematic reviews, meta-analyses or double-blind, randomized, placebo-controlled trials (DBRPCTs) reporting efficacy or safety data of VMAT-2 inhibitors (tetrabenazine, deutetrabenazine, and valbenazine) in patients with TD. A random effects meta-analysis of efficacy and safety data from DBRPCTs was performed.ResultsTwo acute, 12-week DBRPCTs with deutetrabenazine 12–48 mg/day (n=413) and 4 acute, 4–6-week double-blind trials with valbenazine 12.5–100 mg/day (n=488) were meta-analyzable, without meta-analyzable, high-quality data for tetrabenazine. Regarding reduction in total Abnormal Involuntary Movement Scale (AIMS) scores (primary outcome), both deutetrabenazine (k=2, n=413, standardized mean difference [SMD] =−0.40, 95% confidence interval [CI] =−0.19, −0.62, p<0.001; weighted mean difference (WMD) =−1.44, 95% CI =−0.67, −2.19, p<0.001) and valbenazine (k=4, n=421, SMD =−0.58, 95% CI =−0.26, −0.91, p<0.001; WMD =−2.07, 95% CI =−1.08, −3.05, p<0.001) significantly outperformed placebo. Results were confirmed regarding responder rates (≥50% AIMS total score reduction; deutetrabenazine: risk ratio [RR] =2.13, 95% CI =1.10, 4.12, p=0.024, number-needed-to-treat [NNT] =7, 95% CI =3, 333, p=0.046; valbenazine: RR =3.05, 95% CI =1.81, 5.11, p<0.001, NNT =4, 95% CI =3, 6, p<0.001). Less consistent results emerged from patient-rated global impression-based response (p=0.15) and clinical global impression for deutetrabenazine (p=0.088), and for clinical global impression change for valbenazine (p=0.67). In an open-label extension (OLE) study of deutetrabenazine (≤54 weeks) and a dose-blinded valbenazine study (≤48 weeks), responder rates increased over time. With valbenazine, discontinuation effects were studied, showing TD symptom recurrence towards baseline severity levels within 4 weeks after valbenazine withdrawal. No increased cumulative or specific adverse (AEs) events versus placebo (acute trials) in extension versus acute trial data were observed.ConclusionThe 2 VMAT-2 inhibitors, valbenazine and deutetrabenazine, are effective in treating TD, both acutely and long-term, without concerns about increased risk of depression or suicide in the TD population. No head-to-head comparison among VMAT-2 inhibitors and no high-quality, meta-analyzable data are available for tetrabenazine in patients with TD.
Adherence to antidepressants is crucial for optimal treatment outcomes when treating depressive disorders. However, poor adherence is common among patients prescribed antidepressants. This targeted review summarizes the main factors associated with poor adherence, interventions that promote antidepressant adherence, pharmacological aspects related to antidepressant adherence, and formulates 10 clinical recommendations to optimize antidepressant adherence. Patient-related factors associated with antidepressant non-adherence include younger age, psychiatric and medical comorbidities, cognitive impairment, and substance use disorders. Prescriber behavior-related factors include neglecting medical and family histories, selecting poorly tolerated antidepressants, or complex antidepressant regimens. Multi-disciplinary interventions targeting both patient and prescriber, aimed at improving antidepressant adherence, include psychoeducation and providing the patient with clear behavioral interventions to prevent/minimize poor adherence. Regarding antidepressant choice, agents with individually tailored tolerability profile should be chosen. Ten clinical recommendations include four points focusing on the patient (therapeutic alliance, adequate history taking, measurement of depressive symptoms, and adverse effects improved access to clinical care), three focusing on prescribing practice (psychoeducation, individually tailored antidepressant choice, simplified regimen), two focusing on mental health services (improved access to mental health care, incentivized adherence promotion and monitoring), and one relating to adherence measurement (adherence measurement with scales and/or therapeutic drug monitoring).
Major depression is a high-prevalence mental disease with major socio-economic impact, for both the direct and the indirect costs. Major depression symptoms can be faked or exaggerated in order to obtain economic compensation from insurance companies. Critically, depression is potentially easily malingered, as the symptoms that characterize this psychiatric disorder are not difficult to emulate. Although some tools to assess malingering of psychiatric conditions are already available, they are principally based on self-reporting and are thus easily faked. In this paper, we propose a new method to automatically detect the simulation of depression, which is based on the analysis of mouse movements while the patient is engaged in a double-choice computerized task, responding to simple and complex questions about depressive symptoms. This tool clearly has a key advantage over the other tools: the kinematic movement is not consciously controllable by the subjects, and thus it is almost impossible to deceive. Two groups of subjects were recruited for the study. The first one, which was used to train different machine-learning algorithms, comprises 60 subjects (20 depressed patients and 40 healthy volunteers); the second one, which was used to test the machine-learning models, comprises 27 subjects (9 depressed patients and 18 healthy volunteers). In both groups, the healthy volunteers were randomly assigned to the liars and truth-tellers group. Machine-learning models were trained on mouse dynamics features, which were collected during the subject response, and on the number of symptoms reported by participants. Statistical results demonstrated that individuals that malingered depression reported a higher number of depressive and non-depressive symptoms than depressed participants, whereas individuals suffering from depression took more time to perform the mouse-based tasks compared to both truth-tellers and liars. Machine-learning models reached a classification accuracy up to 96% in distinguishing liars from depressed patients and truth-tellers. Despite this, the data are not conclusive, as the accuracy of the algorithm has not been compared with the accuracy of the clinicians; this study presents a possible useful method that is worth further investigation.
Several preclinical studies have demonstrated neuronal effects of glucocorticoids on the hippocampus (HC), a limbic structure with anterior-posterior anatomical and functional segmentation. We propose a volumetric magnetic resonance imaging analysis of hippocampus head (HH), body (HB) and tail (HT) using Cushing's disease (CD) as model, to investigate whether there is a differential sensitivity to glucocorticoid neuronal damage in these segments. We found a significant difference in the HH bilaterally after 12 months from trans-sphenoidal surgical selective resection of the adrenocorticotropic hormone (ACTH)-secreting pituitary micro-adenomas. This pre-post surgery difference could contribute to better understand the pathopysiology of CD as an in vivo model for stress-related hypercortisolemic neuropsychiatric disorders.
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