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      Geotemporospatial and causal inferential epidemiological overview and survey of USA cannabis, cannabidiol and cannabinoid genotoxicity expressed in cancer incidence 2003–2017: part 2 – categorical bivariate analysis and attributable fractions

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          Abstract

          Background

          As the cannabis-cancer relationship remains an important open question epidemiological investigation is warranted to calculate key metrics including Rate Ratios (RR), Attributable Fractions in the Exposed (AFE) and Population Attributable Risks (PAR) to directly compare the implicated case burden between emerging cannabinoids and the established carcinogen tobacco.

          Methods

          SEER*Stat software from Centres for Disease Control was used to access age-standardized state census incidence of 28 cancer types (including “All (non-skin) Cancer”) from National Cancer Institute in US states 2001–2017. Drug exposures taken from the National Survey of Drug Use and Health 2003–2017, response rate 74.1%. Federal seizure data provided cannabinoid exposure. US Census Bureau furnished income and ethnicity. Exposure dichotomized as highest v. lowest exposure quintiles. Data processed in R.

          Results

          Nineteen thousand eight hundred seventy-seven age-standardized cancer rates were returned. Based on these rates and state populations this equated to 51,623,922 cancer cases over an aggregated population 2003–2017 of 124,896,418,350. Fifteen cancers displayed elevated E-Values in the highest compared to the lowest quintiles of cannabidiol exposure, namely (in order): prostate, melanoma, Kaposi sarcoma, ovarian, bladder, colorectal, stomach, Hodgkins, esophagus, Non-Hodgkins lymphoma, All cancer, brain, lung, CLL and breast. Eleven cancers were elevated in the highest THC exposure quintile: melanoma, thyroid, liver, AML, ALL, pancreas, myeloma, CML, breast, oropharynx and stomach. Twelve cancers were elevated in the highest tobacco quintile confirming extant knowledge and study methodology. For cannabidiol RR declined from 1.397 (95%C.I. 1.392, 1.402), AFE declined from 28.40% (28.14, 28.66%), PAR declined from 15.3% (15.1, 15.5%) and minimum E-Values declined from 2.13. For THC RR declined from 2.166 (95%C.I. 2.153, 2.180), AFE declined from 53.8% (53.5, 54.1%); PAR declined from 36.1% (35.9, 36.4%) and minimum E-Values declined from 3.72. For tobacco, THC and cannabidiol based on AFE this implies an excess of 93,860, 91,677 and 48,510 cases; based on PAR data imply an excess of 36,450, 55,780 and 14,819 cases.

          Conclusion

          Data implicate 23/28 cancers as being linked with THC or cannabidiol exposure with epidemiologically-causal relationships comparable to those for tobacco. AFE-attributable cases for cannabinoids (91,677 and 48,510) compare with PAR-attributable cases for tobacco (36,450). Cannabinoids constitute an important multivalent community carcinogen.

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            DNA methylation age of human tissues and cell types

            Background It is not yet known whether DNA methylation levels can be used to accurately predict age across a broad spectrum of human tissues and cell types, nor whether the resulting age prediction is a biologically meaningful measure. Results I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance. Conclusions I propose that DNA methylation age measures the cumulative effect of an epigenetic maintenance system. This novel epigenetic clock can be used to address a host of questions in developmental biology, cancer and aging research.
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              Sensitivity Analysis in Observational Research: Introducing the E-Value.

              Sensitivity analysis is useful in assessing how robust an association is to potential unmeasured or uncontrolled confounding. This article introduces a new measure called the "E-value," which is related to the evidence for causality in observational studies that are potentially subject to confounding. The E-value is defined as the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates. A large E-value implies that considerable unmeasured confounding would be needed to explain away an effect estimate. A small E-value implies little unmeasured confounding would be needed to explain away an effect estimate. The authors propose that in all observational studies intended to produce evidence for causality, the E-value be reported or some other sensitivity analysis be used. They suggest calculating the E-value for both the observed association estimate (after adjustments for measured confounders) and the limit of the confidence interval closest to the null. If this were to become standard practice, the ability of the scientific community to assess evidence from observational studies would improve considerably, and ultimately, science would be strengthened.
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                Author and article information

                Contributors
                stuart.reece@bigpond.com
                Journal
                Arch Public Health
                Arch Public Health
                Archives of Public Health
                BioMed Central (London )
                0778-7367
                2049-3258
                30 March 2022
                30 March 2022
                2022
                : 80
                : 100
                Affiliations
                [1 ]GRID grid.1012.2, ISNI 0000 0004 1936 7910, Division of Psychiatry, , University of Western Australia, ; Crawley, Western Australia 6009 Australia
                [2 ]GRID grid.1038.a, ISNI 0000 0004 0389 4302, School of Medical and Health Sciences, , Edith Cowan University, ; Joondalup, Western Australia 6027 Australia
                [3 ]Brisbane, Australia
                Author information
                http://orcid.org/0000-0002-3256-720X
                Article
                812
                10.1186/s13690-022-00812-7
                8969377
                35354495
                4b01d51c-a569-4852-927f-5dd93c476ae3
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 10 August 2021
                : 29 January 2022
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                Public health
                cannabis,cannabinoid,δ9-tetrahydrocannabinol,cannabigerol,cannabidiol,mechanisms,congenital anomalies,oncogenesis,genotoxicity,epigenotoxicity,chromosomal toxicity,multigenerational genotoxicity,transgenerational teratogenicity,dose-response relationship,supra-linear dose response,sigmoidal dose-response

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