An Untargeted Metabolomics Analysis of Antipsychotic Use in Bipolar Disorder.
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ABSTRACT:
SUBMITTER: Burghardt KJ
PROVIDER: S-EPMC4626284 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundNon-adherence is a significant problem in bipolar disorder. Second-generation antipsychotics (SGA) long-acting injections (LAIs) may improve adherence in bipolar disorder and may prevent relapses. However, the evidence is limited and conflicting.ObjectiveThe objective of this study was to evaluate efficacy and safety of SGA LAIs in bipolar disorder.MethodSystematic review and meta-analysis of randomised controlled trials (RCTs) (≥6 months duration) investigating safety and efficacy of SGA LAIs for bipolar disorder. We searched Pubmed, Embase, CINAHL, Cochrane, PsycINFO, LiLACS, www.clinicaltrials.gov up to October 2016. We also contacted the manufacturers of SGA LAIs. Primary efficacy and safety outcomes were relapse rate and all-cause discontinuation respectively.ResultsTotal of seven RCTs (n = 1192) were included. SGA LAIs show superiority over placebo for study-defined relapse rate (RR = 0.58, 95% CI = 0.49-0.68, P < 0.00001) and all-cause discontinuation (RR = 0.72, 95% CI = 0.64-0.82, P < 0.00001). However, no significant difference was found between SGA LAIs and oral active control for relapse rate (RR = 0.92, P = 0.79) and all-cause discontinuation (RR = 1.2, P = 0.31). In terms of secondary outcomes, SGA LAIs performed better than placebo in relapse to mania/hypomania, young mania rating scales (YMRS), clinical global impression-severity (CGI-S), montgomery-asberg depression rating scale (MADRS). There was no significant difference between SGA LAIs and oral active control regarding relapse to mania/hypomania, YMRS, CGI-S, extra-pyramidal side effects (EPSEs), weight gain. However, the active control performed better than SGA LAIs in relapse to depression, MADRS, and prolactin-related AEs.ConclusionsCurrent evidence is very limited to support the use of SGA LAIs (compared to oral medication) in bipolar disorder. Further high-quality studies, particularly comparing SGA LAIs with active control, are warranted.
| S-EPMC6587954 | biostudies-literature
Project description:ObjectivesNursing home quality measures include the proportion of residents who receive antipsychotics. Residents with bipolar disorder are included even though antipsychotics are FDA-approved for this indication. We evaluated how including residents with bipolar disorder impacted the antipsychotic use quality measure for long-stay residents. We evaluated the agreement of minimum data set (MDS) bipolar disorder diagnoses with Medicare data, whether dementia was diagnosed before bipolar disorder, and how less-specific bipolar disorder diagnoses impacted findings.DesignCross-sectional study.SettingNursing homes in Iowa.Participants21,955 long-stay nursing home residents in the first quarter of 2014.MeasurementsWe identified antipsychotic use and bipolar disorder using MDS data. We compared MDS bipolar disorder diagnoses with Chronic Conditions Warehouse (CCW) "ever" bipolar disorder indicators, and prior year claims. We compared CCW condition onset dates to identify bipolar disorder diagnosed after dementia.ResultsThe mean (SD) proportion receiving antipsychotics was 19.6% (11.1%) with bipolar disorder and 18.3% (10.8%) without. The positive predictive value (PPV) of MDS bipolar disorder diagnoses was 80.2% versus CCW lifetime indicators, and 74.6% versus claims. PPV decreased by 27.1% when "bipolar disorder, unspecified" and "other bipolar disorders" diagnoses were excluded. Nearly three-quarters of residents with bipolar disorder had dementia. Over half of those with dementia had dementia first per CCW records. This proportion was lower among those with more specific bipolar disorder diagnoses or MDS bipolar disorder indicators.ConclusionsBipolar disorder in nursing home residents is often first diagnosed after dementia using nonspecific diagnoses. This practice deserves further evaluation.
| S-EPMC6946118 | biostudies-literature
Project description:The pharmacoepigenetics of antipsychotic treatment in severe mental illness is a growing area of research that aims to understand the interface between antipsychotic treatment and genetic regulation. Pharmacoepigenetics may some day assist in identifying treatment response mechanisms or become one of the components in the implementation of precision medicine. To understand the current evidence regarding the effects of antipsychotics on DNA methylation a systematic review with qualitative synthesis was performed through Pubmed, Embase and Psychinfo from earliest data to June 2019. Studies were included if they analyzed DNA methylation in an antipsychotic-treated population of patients with schizophrenia or bipolar disorder. Data extraction occurred via a standardized format and study quality was assessed. Twenty-nine studies were identified for inclusion. Study design, antipsychotic type, sample source, and methods of DNA methylation measurement varied across all studies. Eighteen studies analyzed methylation in patients with schizophrenia, four studies in patients with bipolar disorder, and seven studies in a combined sample of schizophrenia and bipolar disorder. Twenty-two studies used observational samples whereas the remainder used prospectively treated samples. Six studies assessed global methylation, five assessed epigenome-wide, and 15 performed a candidate epigenetic study. Two studies analyzed both global and gene-specific methylation, whereas one study performed a simultaneous epigenome-wide and gene-specific study. Only three genes were analyzed in more than one gene-specific study and the findings were discordant. The state of the pharmacoepigenetic literature on antipsychotic use is still in its early stages and uniform reporting of methylation site information is needed. Future work should concentrate on using prospective sampling with appropriate control groups and begin to replicate many of the novel associations that have been reported.
| S-EPMC7152563 | biostudies-literature
Project description:The rapidly increasing number of engineered nanoparticles (NPs), and products containing NPs, raises concerns for human exposure and safety. With this increasing, and ever changing, catalogue of NPs it is becoming more difficult to adequately assess the toxic potential of new materials in a timely fashion. It is therefore important to develop methods which can provide high-throughput screening of biological responses. The use of omics technologies, including metabolomics, can play a vital role in this process by providing relatively fast, comprehensive, and cost-effective assessment of cellular responses. These techniques thus provide the opportunity to identify specific toxicity pathways and to generate hypotheses on how to reduce or abolish toxicity.We have used untargeted metabolome analysis to determine differentially expressed metabolites in human lung epithelial cells (A549) exposed to copper oxide nanoparticles (CuO NPs). Toxicity hypotheses were then generated based on the affected pathways, and critically tested using more conventional biochemical and cellular assays. CuO NPs induced regulation of metabolites involved in oxidative stress, hypertonic stress, and apoptosis. The involvement of oxidative stress was clarified more easily than apoptosis, which involved control experiments to confirm specific metabolites that could be used as standard markers for apoptosis; based on this we tentatively propose methylnicotinamide as a generic metabolic marker for apoptosis.Our findings are well aligned with the current literature on CuO NP toxicity. We thus believe that untargeted metabolomics profiling is a suitable tool for NP toxicity screening and hypothesis generation.
| S-EPMC5017021 | biostudies-other
Project description: Not available
| S-EPMC8705952 | biostudies-literature
Project description:Opioid use disorder (OUD) is diagnosed using the qualitative criteria defined by the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). Diagnostic biomarkers for OUD do not currently exist. Our study focused on developing objective biological markers to differentiate chronic opiate users with OUD from chronic opiate users without OUD. Using biospecimens from the Golestan Cohort Study, we compared the metabolomics profiles of high opium users who were diagnosed as OUD positive with high opium users who were diagnosed as OUD negative. High opium use was defined as maximum weekly opium usage greater than or equal to the median usage (2.4 g per week), and OUD was defined as having 2 or more DSM-5 criteria in any 12-month period. Among the 218 high opium users in this study, 80 were diagnosed as OUD negative, while 138 were diagnosed as OUD positive. Seven hundred and twelve peaks differentiated high opium users diagnosed as OUD positive from high opium users diagnosed as OUD negative. Stepwise logistic regression modeling of subject characteristics data together with the 712 differentiating peaks revealed a signature that is 95% predictive of an OUD positive diagnosis, a significant (p < 0.0001) improvement over a 63% accurate prediction based on subject characteristic data for these samples. These results suggest that a metabolic profile can be used to predict an OUD positive diagnosis.
| S-EPMC7862627 | biostudies-literature
Project description:BACKGROUND:Oral antipsychotic (AP) medications are frequently prescribed to people with bipolar I disorder (BD-I). A cross-sectional online survey examined the experiences of people living with BD-I with a history of recent AP use. METHODS:Adults with self-reported physician-diagnosed BD-I (N =?200) who received oral APs during the prior year completed a survey on AP-related experiences, including side effects and their perceived burden on social functioning, adherence, and work. Items also assessed preferences for trade-offs (balancing symptom management and side effects) when considering a hypothetical new AP. The perceived impact of specific, prevalent side effects on adherence, work, and preferences for a hypothetical AP were also examined. Analyses were descriptive. RESULTS:The survey sample had a mean age of 43.2 (SD?=?12.4) years, was 60% female, and 31% nonwhite. Almost all participants (98%) had experienced AP side effects. Common self-reported side effects were feeling drowsy or tired (83%), lack of emotion (79%), anxiety (79%), dry mouth (76%), and weight gain (76%). Weight gain was cited as the most bothersome side effect, rated by most participants (68%) as "very" or "extremely bothersome." Nearly half of participants (49%) reported that AP side effects negatively impacted their job performance; almost all (92%) reported that side effects - most commonly anxiety and lack of emotion - negatively impacted social relationships (e.g., family or romantic partners). The most commonly-reported reason for stopping AP use was dislike of side effects (48%). Side effects most likely to lead to stopping or taking less of AP treatment included "feeling like a 'zombie'" (29%), feeling drowsy or tired (25%), and weight gain (24%). When considering a hypothetical new AP, the most common side effects participants wanted to avoid included AP-induced anxiety (50%), weight gain (48%), and "feeling like a 'zombie'" (47%). CONCLUSIONS:Side effects of APs were both common and bothersome, and impacted social functioning, adherence, and work. Findings highlight the prevailing unmet need for new APs with more favorable benefit-risk profiles.
| S-EPMC7371473 | biostudies-literature
Project description:Genome scans of bipolar disorder (BPD) have not produced consistent evidence for linkage. The rank-based genome scan meta-analysis (GSMA) method was applied to 18 BPD genome scan data sets in an effort to identify regions with significant support for linkage in the combined data. The two primary analyses considered available linkage data for "very narrow" (i.e., BP-I and schizoaffective disorder-BP) and "narrow" (i.e., adding BP-II disorder) disease models, with the ranks weighted for sample size. A "broad" model (i.e., adding recurrent major depression) and unweighted analyses were also performed. No region achieved genomewide statistical significance by several simulation-based criteria. The most significant P values (<.01) were observed on chromosomes 9p22.3-21.1 (very narrow), 10q11.21-22.1 (very narrow), and 14q24.1-32.12 (narrow). Nominally significant P values were observed in adjacent bins on chromosomes 9p and 18p-q, across all three disease models on chromosomes 14q and 18p-q, and across two models on chromosome 8q. Relatively few BPD pedigrees have been studied under narrow disease models relative to the schizophrenia GSMA data set, which produced more significant results. There was no overlap of the highest-ranked regions for the two disorders. The present results for the very narrow model are promising but suggest that more and larger data sets are needed. Alternatively, linkage might be detected in certain populations or subsets of pedigrees. The narrow and broad data sets had considerable power, according to simulation studies, but did not produce more highly significant evidence for linkage. We note that meta-analysis can sometimes provide support for linkage but cannot disprove linkage in any candidate region.
| S-EPMC1180589 | biostudies-literature
Project description:Multi-omics approaches, which integrate genomics, transcriptomics, proteomics, and metabolomics, have emerged as powerful tools in the diagnosis of rare diseases. We used untargeted metabolomics and whole-genome sequencing (WGS) to gain a more comprehensive understanding of a rare disease with a complex presentation affecting female twins from a consanguineous family. The sisters presented with polymicrogyria, a Dandy-Walker malformation, respiratory distress, and multiorgan dysfunctions. Through WGS, we identified two rare homozygous variants in both subjects, a pathogenic variant in ADGRG1(p.Arg565Trp) and a novel variant in CNTNAP1(p.Glu910Val). These genes have been previously associated with autosomal recessive polymicrogyria and hypomyelinating neuropathy with/without contractures, respectively. The twins exhibited symptoms that overlapped with both of these conditions. The results of the untargeted metabolomics analysis revealed significant metabolic perturbations relating to neurodevelopmental abnormalities, kidney dysfunction, and microbiome. The significant metabolites belong to essential pathways such as lipids and amino acid metabolism. The identification of variants in two genes, combined with the support of metabolic perturbation, demonstrates the rarity and complexity of this phenotype and provides valuable insights into its underlying mechanisms.
| S-EPMC10972350 | biostudies-literature
Project description:While in nucleotide sequencing, the analysis of DNA from complex mixtures of organisms is common, this is not yet true for mass spectrometric data analysis of complex mixtures. The comparative analyses of mass spectrometry data of microbial communities at the molecular level is difficult to perform, especially in the context of a host. The challenge does not lie in generating the mass spectrometry data, rather much of the difficulty falls in the realm of how to derive relevant information from this data. The informatics based techniques to visualize and organize datasets are well established for metagenome sequencing; however, due to the scarcity of informatics strategies in mass spectrometry, it is currently difficult to cross correlate two very different mass spectrometry data sets from microbial communities and their hosts. We highlight that molecular networking can be used as an organizational tool of tandem mass spectrometry data, automated database search for rapid identification of metabolites, and as a workflow to manage and compare mass spectrometry data from complex mixtures of organisms. To demonstrate this platform, we show data analysis from hard corals and a human lung associated with cystic fibrosis.
| S-EPMC4379709 | biostudies-literature