Dataset Information

The Association of Arsenic Metabolism with Cancer, Cardiovascular Disease, and Diabetes: A Systematic Review of the Epidemiological Evidence.

ABSTRACT:

Background

The available evidence on the role of arsenic metabolism in individual susceptibility to the development of cancer, cardiovascular disease, and diabetes has not been formally and comprehensively reviewed.

Objectives

Our goal was to systematically investigate the association of arsenic metabolism with cancer, cardiovascular disease, and diabetes-related outcomes in epidemiologic studies. As a secondary objective, we characterized the variation of arsenic metabolism in different populations worldwide.

Methods

We searched Medline/PubMed and EMBASE from inception to January 2016 and applied predetermined exclusion criteria. Compositional data analysis was used to describe the distribution of arsenic metabolism biomarkers and evaluate the association between arsenic exposure and metabolism.

Results

Twenty-eight studies met the inclusion criteria, 12 on cancer, nine on cardiovascular disease, and seven on diabetes-related outcomes. The median (interquartile range) for mean iAs%, MMA%, and DMA% was 11.2 (7.8-14.9)%, 13.0 (10.4-13.6)%, and 74.9 (69.8-80.0)%, respectively. Findings across studies suggested that higher arsenic exposure levels were associated with higher iAs% and lower DMA% and not associated with MMA%. For cancer, most studies found a pattern of higher MMA% and lower DMA% associated with higher risk of all-site, urothelial, lung, and skin cancers. For cardiovascular disease, higher MMA% was generally associated with higher risk of carotid atherosclerosis and clinical cardiovascular disease but not with hypertension. For diabetes-related outcomes, the pattern of lower MMA% and higher DMA% was associated with higher risk of metabolic syndrome and diabetes.

Conclusions

Population level of iAs% and DMA%, but not MMA%, were associated with arsenic exposure levels. Overall, study findings suggest that higher MMA% was associated with an increased risk of cancer and cardiovascular disease, while lower MMA% was associated with an increased risk of diabetes and metabolic syndrome. Additional population-based studies and experimental studies are needed to further evaluate and understand the role of arsenic exposure in arsenic metabolism and the role of arsenic metabolism in disease development. https://doi.org/10.1289/EHP577.

SUBMITTER: Kuo CC

PROVIDER: S-EPMC5880251 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The Association of Arsenic Metabolism with Cancer, Cardiovascular Disease, and Diabetes: A Systematic Review of the Epidemiological Evidence.

Kuo Chin-Chi CC Moon Katherine A KA Wang Shu-Li SL Silbergeld Ellen E Navas-Acien Ana A

Environmental health perspectives 20170801 8

<h4>Background</h4>The available evidence on the role of arsenic metabolism in individual susceptibility to the development of cancer, cardiovascular disease, and diabetes has not been formally and comprehensively reviewed.<h4>Objectives</h4>Our goal was to systematically investigate the association of arsenic metabolism with cancer, cardiovascular disease, and diabetes-related outcomes in epidemiologic studies. As a secondary objective, we characterized the variation of arsenic metabolism in di ...[more]

PMID: 28796632

Similar Datasets

Project description:Inorganic arsenic (iAs) exposure has been reported to have an impact on cardiovascular diseases (CVD). However, there is not much known about the cardiac tissue injury of CVD patients in relation to iAs exposure and potential role of single nucleotide polymorphisms (SNPs) of genes related to iAs metabolism, oxidative stress, endothelial dysfunction and inflammation which may play important roles in such CVD cases. In this dual center cross-sectional study, based on the exclusion and inclusion criteria, we have recruited 50 patients out of 270, who came from known arsenic-affected and- unaffected areas of mainly Chittagong, Dhaka and Rajshahi divisions of Bangladesh and underwent open-heart surgery at the selected centers during July 2017 to June 2018. We found that the patients from arsenic affected areas contained significantly higher average iAs concentrations in their urine (6.72 ± 0.54 ppb, P = 0.028), nail (529.29 ± 38.76 ppb, P < 0.05) and cardiac tissue (4.83 ± 0.50 ppb, P < 0.05) samples. Patients' age, sex, BMI, hypertension and diabetes status adjusted analysis showed that patients from arsenic-affected areas had significantly higher iAs concentration in cardiac tissue (2.854, 95%CI 1.017-8.012, P = 0.046) reflecting higher cardiac tissue injury among them (1.831, 95%CI 1.032-3.249, P = 0.039), which in turn allowed the analysis to assume that the iAs exposure have played a vital role in patients' disease condition. Adjusted analysis showed significant association between urinary iAs concentration with AA (P = 0.012) and AG (P = 0.034) genotypes and cardiac iAs concentration with AA (P = 0.017) genotype of AS3MT rs10748835. The AG genotype of AS3MT rs10748835 (13.333 95%CI 1.280-138.845, P = 0.013), AA genotype of NOS3 rs3918181 (25.333 95%CI 2.065-310.757, P = 0.002), GG genotype of ICAM1 rs281432 (12.000 95%CI 1.325-108.674, P = 0.010) and AA genotype of SOD2 rs2758331 (13.333 95%CI 1.280-138.845, P = 0.013) were found significantly associated with CVD patients from arsenic-affected areas. Again, adjusted analysis showed significant association of AA genotype of AS3MT rs10748835 with CVD patients from arsenic affected areas. In comparison to the reference genotypes of the selected SNPs, AA of AS3MT 10748835, AG of NOS3 rs3918181 and AC of rs3918188, GG of ICAM1 rs281432, TT of VCAM1 rs3176867, AA of SOD2 rs2758331 and GT of APOE rs405509 significantly increased odds of cardiac tissue injury of CVD patients from arsenic affected areas. The results showed that the selected SNPs played a susceptibility role towards cardiac tissue iAs concentration and injury among CVD patients from iAs affected areas.

Project description:BackgroundMajor chronic diseases such as cardiovascular disease (CVD), diabetes, and cancer impose a significant burden on people and health care systems around the globe. Recently, deep learning (DL) has shown great potential for the development of intelligent mobile health (mHealth) interventions for chronic diseases that could revolutionize the delivery of health care anytime, anywhere.ObjectiveThe aim of this study is to present a systematic review of studies that have used DL based on mHealth data for the diagnosis, prognosis, management, and treatment of major chronic diseases and advance our understanding of the progress made in this rapidly developing field.MethodsA search was conducted on the bibliographic databases Scopus and PubMed to identify papers with a focus on the deployment of DL algorithms that used data captured from mobile devices (eg, smartphones, smartwatches, and other wearable devices) targeting CVD, diabetes, or cancer. The identified studies were synthesized according to the target disease, the number of enrolled participants and their age, and the study period as well as the DL algorithm used, the main DL outcome, the data set used, the features selected, and the achieved performance.ResultsIn total, 20 studies were included in the review. A total of 35% (7/20) of DL studies targeted CVD, 45% (9/20) of studies targeted diabetes, and 20% (4/20) of studies targeted cancer. The most common DL outcome was the diagnosis of the patient's condition for the CVD studies, prediction of blood glucose levels for the studies in diabetes, and early detection of cancer. Most of the DL algorithms used were convolutional neural networks in studies on CVD and cancer and recurrent neural networks in studies on diabetes. The performance of DL was found overall to be satisfactory, reaching >84% accuracy in most studies. In comparison with classic machine learning approaches, DL was found to achieve better performance in almost all studies that reported such comparison outcomes. Most of the studies did not provide details on the explainability of DL outcomes.ConclusionsThe use of DL can facilitate the diagnosis, management, and treatment of major chronic diseases by harnessing mHealth data. Prospective studies are now required to demonstrate the value of applied DL in real-life mHealth tools and interventions.

Project description:BACKGROUND:Cardiovascular disease (CVD) is a common comorbidity in type 2 diabetes (T2DM). CVD's prevalence has been growing over time. PURPOSE:To estimate the current prevalence of CVD among adults with T2DM by reviewing literature published within the last 10 years (2007-March 2017). METHODS:We searched Medline, Embase, and proceedings of major scientific meetings for original research documenting the prevalence of CVD in T2DM. CVD included stroke, myocardial infarction, angina pectoris, heart failure, ischemic heart disease, cardiovascular disease, coronary heart disease, atherosclerosis, and cardiovascular death. No restrictions were placed on country of origin or publication language. Two reviewers independently searched for articles and extracted data, adjudicating results through consensus. Data were summarized descriptively. Risk of bias was examined by applying the STROBE checklist. RESULTS:We analyzed data from 57 articles with 4,549,481 persons having T2DM. Europe produced the most articles (46%), followed by the Western Pacific/China (21%), and North America (13%). Overall in 4,549,481 persons with T2DM, 52.0% were male, 47.0% were obese, aged 63.6?±?6.9 years old, with T2DM duration of 10.4?±?3.7 years. CVD affected 32.2% overall (53 studies, N?=?4,289,140); 29.1% had atherosclerosis (4 studies, N?=?1153), 21.2% had coronary heart disease (42 articles, N?=?3,833,200), 14.9% heart failure (14 studies, N?=?601,154), 14.6% angina (4 studies, N?=?354,743), 10.0% myocardial infarction (13 studies, N?=?3,518,833) and 7.6% stroke (39 studies, N?=?3,901,505). CVD was the cause of death in 9.9% of T2DM patients (representing 50.3% of all deaths). Risk of bias was low; 80?±?12% of STROBE checklist items were adequately addressed. CONCLUSIONS:Globally, overall CVD affects approximately 32.2% of all persons with T2DM. CVD is a major cause of mortality among people with T2DM, accounting for approximately half of all deaths over the study period. Coronary artery disease and stroke were the major contributors.

Dataset Information

The Association of Arsenic Metabolism with Cancer, Cardiovascular Disease, and Diabetes: A Systematic Review of the Epidemiological Evidence.

Background

Objectives

Methods

Results

Conclusions

Publications

The Association of Arsenic Metabolism with Cancer, Cardiovascular Disease, and Diabetes: A Systematic Review of the Epidemiological Evidence.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets