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Deepak Cyril D'Souza, Staff Psychiatrist, VA Connecticut Healthcare System; Professor of Psychiatry, Yale University School of Medicine,David Castle, University of Tasmania, Australia,Sir Robin Murray, Honorary Consultant Psychiatrist, Psychosis Service at the South London and Maudsley NHS Trust; Professor of Psychiatric Research at the Institute of Psychiatry
Cannabis and cannabinoids are widely used, both as recreational substances with potential for addiction, and as treatments for a number of disorders. A large body of literature has investigated the harmful and/or beneficial effects of cannabis and/or cannabinoids employing observational and interventional methodologies. These individual studies have been pooled in many meta-analyses. Further, Mendelian Randomization (MR) studies have reported on a causal association between cannabis use and certain outcomes. This chapter reviews existing meta-analyses that pooled observational and interventional studies, and MR studies reporting on health outcomes after exposure to cannabis and/or cannabinoids in the general population, and selected clinical populations. We show that evidence from observational, interventional, and MR studies point towards an association between cannabis and psychosis. Several additional detrimental effects of cannabis emerged, including other psychiatric symptoms, cognitive impairment, and risk of motor vehicle accident (MVA). In terms of therapeutic benefits, cannabidiol seems to be effective for certain types of epilepsy, notably in children. Also, cannabis-based medicines can be effective in improving muscle spasticity in multiple sclerosis, ameliorating chronic pain syndromes, and reducing nausea/vomiting in palliative care settings. Risk–benefit ratios should be discussed with individual patients.
Accumulating evidence suggests that alterations in inflammatory biomarkers are important in depression. However, previous meta-analyses disagree on these associations, and errors in data extraction may account for these discrepancies.
PubMed/MEDLINE, Embase, PsycINFO, and the Cochrane Library were searched from database inception to 14 January 2020. Meta-analyses of observational studies examining the association between depression and levels of tumor necrosis factor-α (TNF-α), interleukin 1-β (IL-1β), interleukin-6 (IL-6), and C-reactive protein (CRP) were eligible. Errors were classified as follows: incorrect sample sizes, incorrectly used standard deviation, incorrect participant inclusion, calculation error, or analysis with insufficient data. We determined their impact on the results after correction thereof.
Errors were noted in 14 of the 15 meta-analyses included. Across 521 primary studies, 118 (22.6%) showed the following errors: incorrect sample sizes (20 studies, 16.9%), incorrect use of standard deviation (35 studies, 29.7%), incorrect participant inclusion (7 studies, 5.9%), calculation errors (33 studies, 28.0%), and analysis with insufficient data (23 studies, 19.5%). After correcting these errors, 11 (29.7%) out of 37 pooled effect sizes changed by a magnitude of more than 0.1, ranging from 0.11 to 1.15. The updated meta-analyses showed that elevated levels of TNF- α, IL-6, CRP, but not IL-1β, are associated with depression.
These findings show that data extraction errors in meta-analyses can impact findings. Efforts to reduce such errors are important in studies of the association between depression and peripheral inflammatory biomarkers, for which high heterogeneity and conflicting results have been continuously reported.
The aim of the current study was to explore the effect of gender, age at onset, and duration on the long-term course of schizophrenia.
Twenty-nine centers from 25 countries representing all continents participated in the study that included 2358 patients aged 37.21 ± 11.87 years with a DSM-IV or DSM-5 diagnosis of schizophrenia; the Positive and Negative Syndrome Scale as well as relevant clinicodemographic data were gathered. Analysis of variance and analysis of covariance were used, and the methodology corrected for the presence of potentially confounding effects.
There was a 3-year later age at onset for females (P < .001) and lower rates of negative symptoms (P < .01) and higher depression/anxiety measures (P < .05) at some stages. The age at onset manifested a distribution with a single peak for both genders with a tendency of patients with younger onset having slower advancement through illness stages (P = .001). No significant effects were found concerning duration of illness.
Our results confirmed a later onset and a possibly more benign course and outcome in females. Age at onset manifested a single peak in both genders, and surprisingly, earlier onset was related to a slower progression of the illness. No effect of duration has been detected. These results are partially in accord with the literature, but they also differ as a consequence of the different starting point of our methodology (a novel staging model), which in our opinion precluded the impact of confounding effects. Future research should focus on the therapeutic policy and implications of these results in more representative samples.
The aim of the current study was to explore the changing interrelationships among clinical variables through the stages of schizophrenia in order to assemble a comprehensive and meaningful disease model.
Twenty-nine centers from 25 countries participated and included 2358 patients aged 37.21 ± 11.87 years with schizophrenia. Multiple linear regression analysis and visual inspection of plots were performed.
The results suggest that with progression stages, there are changing correlations among Positive and Negative Syndrome Scale factors at each stage and each factor correlates with all the others in that particular stage, in which this factor is dominant. This internal structure further supports the validity of an already proposed four stages model, with positive symptoms dominating the first stage, excitement/hostility the second, depression the third, and neurocognitive decline the last stage.
The current study investigated the mental organization and functioning in patients with schizophrenia in relation to different stages of illness progression. It revealed two distinct “cores” of schizophrenia, the “Positive” and the “Negative,” while neurocognitive decline escalates during the later stages. Future research should focus on the therapeutic implications of such a model. Stopping the progress of the illness could demand to stop the succession of stages. This could be achieved not only by both halting the triggering effect of positive and negative symptoms, but also by stopping the sensitization effect on the neural pathways responsible for the development of hostility, excitement, anxiety, and depression as well as the deleterious effect on neural networks responsible for neurocognition.
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