|
1
|
Gil GF, Anderson JA, Aravkin A, Bhangdia K, Carr S, Dai X, Flor LS, Hay SI, Malloy MJ, McLaughlin SA, Mullany EC, Murray CJL, O'Connell EM, Okereke C, Sorensen RJD, Whisnant J, Zheng P, Gakidou E. Health effects associated with chewing tobacco: a Burden of Proof study. Nat Commun 2024; 15:1082. [PMID: 38316758 PMCID: PMC10844244 DOI: 10.1038/s41467-024-45074-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 01/15/2024] [Indexed: 02/07/2024] Open
Abstract
Chewing tobacco use poses serious health risks; yet it has not received as much attention as other tobacco-related products. This study synthesizes existing evidence regarding the health impacts of chewing tobacco while accounting for various sources of uncertainty. We conducted a systematic review and meta-analysis of chewing tobacco and seven health outcomes, drawing on 103 studies published from 1970 to 2023. We use a Burden of Proof meta-analysis to generate conservative risk estimates and find weak-to-moderate evidence that tobacco chewers have an increased risk of stroke, lip and oral cavity cancer, esophageal cancer, nasopharynx cancer, other pharynx cancer, and laryngeal cancer. We additionally find insufficient evidence of an association between chewing tobacco and ischemic heart disease. Our findings highlight a need for policy makers, researchers, and communities at risk to devote greater attention to chewing tobacco by both advancing tobacco control efforts and investing in strengthening the existing evidence base.
Collapse
Affiliation(s)
- Gabriela F Gil
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Jason A Anderson
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Aleksandr Aravkin
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
- Department of Applied Mathematics, University of Washington, Seattle, WA, USA
| | - Kayleigh Bhangdia
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Sinclair Carr
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Xiaochen Dai
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Luisa S Flor
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Simon I Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Matthew J Malloy
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Susan A McLaughlin
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Erin C Mullany
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Christopher J L Murray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Erin M O'Connell
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Chukwuma Okereke
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Reed J D Sorensen
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Joanna Whisnant
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Peng Zheng
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Emmanuela Gakidou
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA.
| |
Collapse
|
|
2
|
HSPA1L rs1061581 polymorphism is associated with the risk of preeclampsia in Han Chinese women. Biosci Rep 2020; 40:222071. [PMID: 32039449 PMCID: PMC7048671 DOI: 10.1042/bsr20194307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 02/06/2020] [Accepted: 02/07/2020] [Indexed: 11/17/2022] Open
Abstract
Preeclampsia (PE) is an excessive systemic inflammation response with dysfunction of endothelial. As a stress protein, heat shock protein 70 (HSP70) plays a pivotal role in protecting cells against apoptosis, oxidative damage and genetic damage. In humans, three genes encode members of the HSP70 class: HSPA1A, HSPA1B and HSPA1L. Our study was to investigate the association between genetic variations of HSPA1L and the susceptibility for PE in Chinese Han population. The polymorphisms of rs2227956, rs1043618 and rs1061581 in HSPA1L were genotyped by TaqMan allelic discrimination real time polymerase chain reaction (PCR) in 929 PE patients and 1024 healthy pregnant women. Statistic difference of the genotypic and allelic frequencies were found in HSPA1L rs1061581 between PE patients and controls (χ2 = 29.863, P < 0.001 by genotype; χ2 = 27.298, P < 0.001, OR = 1.874, 95%CI 1.476-2.379 by allele) and HSPA1L rs1061581 A alleles occurred more frequently in PE patients compared with healthy controls (PE vs. controls 10.28% vs. 5.76%). Furthermore, we divided the PE cases into early-onset/late-onset PE and mild/severe PE subgroups and found statistical differences in genotypic and allelic frequencies of the HSPA1L rs1061581 between early-onset PE, late-onset PE, mild PE, severe PE and controls, respectively. Moreover, HSPA1L rs1061581 A alleles were more frequent in early-onset PE, late-onset PE, mild PE and severe PE than controls respectively. Therefore, we concluded that HSPA1L rs1061581 polymorphism is associated with the risk of PE in Han Chinese women and A alleles may play a role in the susceptibility for PE.
Collapse
|