Cannabidiol in anxiety research: a translational integration of preclinical and clinical studies

Introduction: Preclinical research suggests that cannabidiol (CBD) may have therapeutic potential in pathological anxiety. Guidelines to inform the study design of future human studies are however lacking. Aims: We aimed to determine the boundary conditions for anxiolytic effects of CBD in humans by integrating, both qualitatively and quantitatively, pharmacokinetic (PK) and pharmacodynamic (PD) (and subsidiary safety) data from preclinical and clinical studies. Methods: We conducted two systematic reviews in Pubmed and Embase up to August 2021, into PK and PD data of systemic CBD exposure in both humans and animals, which includes anxiolytic and potential side effects. Risk of bias was assessed for effects on anxiety outcomes (SYRCLE’s RoB tool [1] and Cochrane RoB 2.0 [2]), PK outcomes, and harm-related outcomes. A control group was an inclusion criterion in outcome studies across species. In human outcome studies, randomisation was required. We excluded studies that co-administered other substances. We used the IB-de-risk tool [3] for a translational integration of PK and PD data. Further, a meta-analysis, stratified by type of anxiety and using three-level random effects models, was conducted to investigate sources of heterogeneity of CBD effects on anxiety outcomes. The Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach [4] was used to rate the quality of the evidence. Results: We synthesized data from 87 articles with the IB-derisk tool. Most studies (70.3%) reported null effects of CBD on anxiety outcomes. There was no identifiable relation between anxiety outcomes and drug levels across species. In all species (humans, mice, rats), anxiolytic effects of CBD seemed to be clustered in certain differential concentration ranges, which differed between species. Data from 61 articles were included in the meta-analysis. The overall pooled effects of CBD on anxiety differed significantly from zero, p≤.02. The effect was moderate to large for conditioned anxiety in animals, Hedge’s G=0.68, 95%CI[0.11, 1.26], moderate for unconditioned anxiety in animals, Hedge’s G=0.50, 95%CI[0.29, 0.70], and large for human experimental anxiety, Hedge’s G=0.79, 95%CI[0.28, 1.31]. In all cases, compared to placebo/vehicle, CBD exerted beneficial effects on anxiety outcomes. No severe adverse effects were reported. There was substantial heterogeneity between average effect sizes within studies, σ2w Conclusions: A straightforward recommendation for optimal dosing was not possible, because there was no consistent linear effect of CBD on anxiety reduction, and concentration-effect relations were variable across species. Acute and (sub)chronic dosing studies with integrated PK and PD outcomes are required for substantiated dose recommendations. The low quality meta-analytic evidence confirmed the often discussed potential of CBD for treating anxiety symptoms. The compound induced anxiolytic effects, regardless of the type of anxiety studied. Moderator analyses will be conducted to determine other sources of heterogeneity of CBD effects, such as type of anxiety test and anxiety outcome

A Type 1 Diabetes Genetic Risk Score Can Identify Patients With GAD65 Autoantibody-Positive Type 2 Diabetes Who Rapidly Progress to Insulin Therapy

This is the author accepted manuscript. The final version is available from American Diabetes Association via the DOI in this record.Objective Progression to insulin therapy in clinically diagnosed type 2 diabetes is highly variable. GAD65 autoantibodies (GADA) are associated with faster progression, but their predictive value is limited. We aimed to determine if a Type 1 Diabetes Genetic Risk Score (T1DGRS) could predict rapid progression to insulin treatment over and above GADA testing. Research Design and Methods We examined the relationship between T1DGRS, GADA (negative or positive) and rapid insulin requirement (within 5 years) using Kaplan-Meier survival analysis and Cox regression in 8,608 participants with clinical type 2 diabetes (onset >35 years, treated without insulin for ≥6 months). T1DGRS was analyzed both continuously (as standardized scores) and categorized based on previously reported centiles of a type 1 diabetes population (50th (high)). Results In GADA positive participants (3.3%), those with higher T1DGRS progressed to insulin more quickly: Probability of insulin requirement at five years [95% CI]: 47.9%[35.0%,62.78%] (high T1DGRS) vs 27.6%[20.5%,36.5%] (medium T1DGRS) vs 17.6%[11.2%,27.2%] (low T1DGRS), p=0.001. In contrast T1DGRS did not predict rapid insulin requirement in GADA negative participants (p=0.4). In Cox regression analysis with adjustment for age of diagnosis, BMI and cohort, T1DGRS was independently associated with time to insulin only in the presence of GADA: hazard ratio per SD increase 1.48 (1.15,1.90), p=0.002. Conclusions A Type 1 Diabetes Genetic Risk Score alters the clinical implications of a positive GADA test in patients with clinical type 2 diabetes, and is independent of and additive to clinical features.The Wellcome Trust United Kingdom Type 2 Diabetes Case Control Collection (GoDARTS) was funded by The Wellcome Trust (084727/Z/08/Z, 085475/Z/08/Z, 085475/B/08/Z) and as part of the EU IMI-SUMMIT program. GADA assessment in GoDARTS and DCS was funded by EU Innovative Medicines Initiative 115317 (DIRECT), resources of which are composed of financial contributions from the European Union's Seventh Framework Programme (FP7/2007-2013), and European Federation of Pharmaceutical Industries and Associations (EFPIA) companies in kind contribution. The DCS cohort was partially funded by the Netherlands Organization for Health Research and Development (Priority Medicines Elderly Programme 113102006). The Diabetes Alliance for Research in England (DARE) study was funded by the Wellcome Trust and supported by the Exeter NIHR Clinical Research Facility. The MASTERMIND study was funded by the UK Medical Research Council (MR/N00633X/) and supported by the NIHR Exeter Clinical Research Facility. The PRIBA study was funded by the National Institute for Health Research (U.K.) (DRF-2010-03-72) and supported by the NIHR Exeter Clinical Research Facility. B.M.S and A.T.H. are supported by the NIHR Exeter Clinical Research Facility. T.J.M. is a National Institute for Health Research Senior Clinical Senior Lecturer. E.R.P. is a Wellcome Trust New Investigator (102820/Z/13/Z). A.T.H. is a Wellcome Trust Senior Investigator and NIHR Senior Investigator. R.A.O is supported by a Diabetes UK Harry Keen Fellowship (16/0005529). A.G.J. is supported by an NIHR Clinician Scientist award (CS-2015-15-018)

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Funding Information: GMP, PN, and CW are supported by NHLBI R01HL127564. GMP and PN are supported by R01HL142711. AG acknowledge support from the Wellcome Trust (201543/B/16/Z), European Union Seventh Framework Programme FP7/2007–2013 under grant agreement no. HEALTH-F2-2013–601456 (CVGenes@Target) & the TriPartite Immunometabolism Consortium [TrIC]-Novo Nordisk Foundation’s Grant number NNF15CC0018486. JMM is supported by American Diabetes Association Innovative and Clinical Translational Award 1–19-ICTS-068. SR was supported by the Academy of Finland Center of Excellence in Complex Disease Genetics (Grant No 312062), the Finnish Foundation for Cardiovascular Research, the Sigrid Juselius Foundation, and University of Helsinki HiLIFE Fellow and Grand Challenge grants. EW was supported by the Finnish innovation fund Sitra (EW) and Finska Läkaresällskapet. CNS was supported by American Heart Association Postdoctoral Fellowships 15POST24470131 and 17POST33650016. Charles N Rotimi is supported by Z01HG200362. Zhe Wang, Michael H Preuss, and Ruth JF Loos are supported by R01HL142302. NJT is a Wellcome Trust Investigator (202802/Z/16/Z), is the PI of the Avon Longitudinal Study of Parents and Children (MRC & WT 217065/Z/19/Z), is supported by the University of Bristol NIHR Biomedical Research Centre (BRC-1215–2001) and the MRC Integrative Epidemiology Unit (MC_UU_00011), and works within the CRUK Integrative Cancer Epidemiology Programme (C18281/A19169). Ruth E Mitchell is a member of the MRC Integrative Epidemiology Unit at the University of Bristol funded by the MRC (MC_UU_00011/1). Simon Haworth is supported by the UK National Institute for Health Research Academic Clinical Fellowship. Paul S. de Vries was supported by American Heart Association grant number 18CDA34110116. Julia Ramierz acknowledges support by the People Programme of the European Union’s Seventh Framework Programme grant n° 608765 and Marie Sklodowska-Curie grant n° 786833. Maria Sabater-Lleal is supported by a Miguel Servet contract from the ISCIII Spanish Health Institute (CP17/00142) and co-financed by the European Social Fund. Jian Yang is funded by the Westlake Education Foundation. Olga Giannakopoulou has received funding from the British Heart Foundation (BHF) (FS/14/66/3129). CHARGE Consortium cohorts were supported by R01HL105756. Study-specific acknowledgements are available in the Additional file : Supplementary Note. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services. Publisher Copyright: © 2022, The Author(s).Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.Peer reviewe

Cannabidiol in anxiety research: a translational integration of preclinical and clinical studies

Introduction: Preclinical research suggests that cannabidiol (CBD) may have therapeutic potential in pathological anxiety. Guidelines to inform the study design of future human studies are however lacking. Aims: We aimed to determine the boundary conditions for anxiolytic effects of CBD in humans by integrating, both qualitatively and quantitatively, pharmacokinetic (PK) and pharmacodynamic (PD) (and subsidiary safety) data from preclinical and clinical studies. Methods: We conducted two systematic reviews in Pubmed and Embase up to August 2021, into PK and PD data of systemic CBD exposure in both humans and animals, which includes anxiolytic and potential side effects. Risk of bias was assessed for effects on anxiety outcomes (SYRCLE’s RoB tool [1] and Cochrane RoB 2.0 [2]), PK outcomes, and harm-related outcomes. A control group was an inclusion criterion in outcome studies across species. In human outcome studies, randomisation was required. We excluded studies that co-administered other substances. We used the IB-de-risk tool [3] for a translational integration of PK and PD data. Further, a meta-analysis, stratified by type of anxiety and using three-level random effects models, was conducted to investigate sources of heterogeneity of CBD effects on anxiety outcomes. The Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach [4] was used to rate the quality of the evidence. Results: We synthesized data from 87 articles with the IB-derisk tool. Most studies (70.3%) reported null effects of CBD on anxiety outcomes. There was no identifiable relation between anxiety outcomes and drug levels across species. In all species (humans, mice, rats), anxiolytic effects of CBD seemed to be clustered in certain differential concentration ranges, which differed between species. Data from 61 articles were included in the meta-analysis. The overall pooled effects of CBD on anxiety differed significantly from zero, p≤.02. The effect was moderate to large for conditioned anxiety in animals, Hedge’s G=0.68, 95%CI[0.11, 1.26], moderate for unconditioned anxiety in animals, Hedge’s G=0.50, 95%CI[0.29, 0.70], and large for human experimental anxiety, Hedge’s G=0.79, 95%CI[0.28, 1.31]. In all cases, compared to placebo/vehicle, CBD exerted beneficial effects on anxiety outcomes. No severe adverse effects were reported. There was substantial heterogeneity between average effect sizes within studies, σ2w Conclusions: A straightforward recommendation for optimal dosing was not possible, because there was no consistent linear effect of CBD on anxiety reduction, and concentration-effect relations were variable across species. Acute and (sub)chronic dosing studies with integrated PK and PD outcomes are required for substantiated dose recommendations. The low quality meta-analytic evidence confirmed the often discussed potential of CBD for treating anxiety symptoms. The compound induced anxiolytic effects, regardless of the type of anxiety studied. Moderator analyses will be conducted to determine other sources of heterogeneity of CBD effects, such as type of anxiety test and anxiety outcome

A saturated map of common genetic variants associated with human height

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Publisher Copyright: © 2022, The Author(s).Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.Peer reviewe

Circulating microRNAs in sera correlate with soluble biomarkers of immune activation but do not predict mortality in ART treated individuals with HIV-1 infection: A case control study

10.1371/journal.pone.0139981PLoS ONE1010e013998