Literature DB >> 33781801

A review of low-dose arsenic risks and human cancers.

Steven H Lamm1, Isabella J Boroje2, Hamid Ferdosi3, Jaeil Ahn4.   

Abstract

The linear no-threshold (LNT) model has historically been the default assumption in assessing carcinogenic risk from arsenic ingestion based on epidemiological studies. This contrasts with the threshold model used in assessing carcinogenic risk from arsenic ingestion derived from toxicological investigations of experimental animals. We present here a review of our epidemiological work that has examined models that may better explain the human cancer risk from the ingestion of arsenic, particularly from low level exposures, than does the LNT model. While previous epidemiology studies have demonstrated increased risks of bladder, lung, and skin cancers at arsenic exposures of 200 ug/L or greater, we seek here to examine the dose-response patterns at lower exposure levels. These include ecological, case/control, and cohort designs. Methodologic issues include choice of continuous or stratified analysis of exposure data, search for sources of non-conformity or variability, and distinctions in water sources and geography. Multiple studies have yielded useful data-based models, including threshold models, hockey-stick models, and "J-shaped" linear-quadratic models. These models have found that increased cancer risk may only begin at specific arsenic exposure levels greater than zero. These results provide guidance in seeking toxicological explanations and public health reference levels.
Copyright © 2021 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Arsenic; Carcinogenic risk; Low-dose exposure; Threshold; “J-shaped” model

Year:  2021        PMID: 33781801     DOI: 10.1016/j.tox.2021.152768

Source DB:  PubMed          Journal:  Toxicology        ISSN: 0300-483X            Impact factor:   4.221


  1 in total

1.  The pivotal regulatory factor circBRWD1 inhibits arsenic exposure-induced lung cancer occurrence by binding mRNA and regulating its stability.

Authors:  Xiaofei Li; Sixian Chen; Xin Wang; Ruirui Zhang; Jialei Yang; Haotian Xu; Wanting He; Mingshuang Lai; Shuilian Wu; Aruo Nan
Journal:  Mol Ther Oncolytics       Date:  2022-08-23       Impact factor: 6.311

  1 in total

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