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  • 1
    Publication Date: 2016-11-17
    Description: Objectives Noise-induced hearing loss is one of the most serious occupational diseases worldwide. It is caused by interactions between environmental and genetic factors. The purpose of this study was to examine the association between the genetic susceptibility of the eye absent homolog 4 (EYA4) gene and the risk of developing noise-induced hearing loss in China. Methods A case–control association study was carried out with 326 hearing loss cases and 326 controls matched with age and duration of noise exposure, drawn from a cohort of steel workers. Five single nucleotide polymorphisms (SNPs) in the EYA4 were selected and genotyped. Logistic regression was performed to analyse the main effect of genotypes and interactions between genotypes and individual/environmental factors adjusted for confounding factors. Moreover, generalised multiple dimensionality reduction was applied to further detect interaction among the 5 selected SNPs. Results Analysis revealed that locus polymorphism of rs3813346 was associated with the risk of developing noise-induced hearing loss in the dominance model, the codominance model and the addictive model (p=0.004, 0.009 and 0.003, respectively). A significant interaction between rs9321402 and cumulative noise exposure was found (p=0.002). A significant main effect p value (p=0.006) was obtained in the high-level exposure group (cumulative noise exposure ≥98 dB(A)). Generalised multiple dimensionality reduction indicated that the combined interaction of the 2 loci—rs3813346 and rs9493627—significantly affected the incidence of noise-induced hearing loss. Conclusions The research suggests that EYA4 genetic variant and its interaction with noise levels may modify the susceptibility to develop noise-induced hearing loss in Chinese population.
    Keywords: Hearing-related
    Print ISSN: 1351-0711
    Electronic ISSN: 1470-7926
    Topics: Medicine
    Published by BMJ Publishing Group
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  • 2
    Publication Date: 2016-10-15
    Description: Background Malignant mesothelioma (MM) has distinct histological subtypes (epithelioid, sarcomatoid and biphasic) with variable behaviour and prognoses. It is well recognised that survival time varies with the histological subtype of MM. It is not known, however, if asbestos exposure characteristics (type of asbestos, degree of exposure) are associated with different histological subtypes. Aim To determine if the pathological MM subtype is associated with the type of asbestos or the attributes of asbestos exposure. Methods Cases of MM for the period 1962 until 2012, their main histological subtype and their most significant source of asbestos exposure were collected from the Western Australian Mesothelioma Registry. Exposure characteristics included, degree of asbestos exposure (including total days exposed, years since first exposure and, for crocidolite only, calculated cumulative exposure), source of exposure (occupational or environmental), form of asbestos handled (raw or processed) and type of asbestos (crocidolite only or mixed fibres). Results Patients with the biphasic subtype were more likely to have occupational exposure (OR 1.83, 1.12 to 2.85) and exposure to raw fibres (OR 1.58, 1.19 to 2.10). However, differences between subtypes in the proportions with these different exposure characteristics were small and unlikely to be biologically relevant. Other indicators of asbestos exposure were not associated with the histological subtype of mesothelioma. Conclusions There was no strong evidence of a consistent role of asbestos exposure indicators in determining the histological subtype of MM.
    Keywords: Asbestos, Other exposures
    Print ISSN: 1351-0711
    Electronic ISSN: 1470-7926
    Topics: Medicine
    Published by BMJ Publishing Group
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  • 3
    Publication Date: 2016-07-16
    Description: Background It is of considerable interest to forecast the future burden of mesothelioma mortality. Data on deaths are available, whereas no measure of asbestos exposure is available. Methods We compare two Poisson models: a response-only model with an age-cohort specification and a multinomial model with epidemiologically motivated frequencies. Results The response-only model has 5% higher peak mortality than the dose–response model. The former performs slightly better in out-of-sample comparison. Conclusions Mortality is predicted to peak at about 2100 deaths around 2017 among males in cohorts until 1966 and below 90 years of age. The response-only model is a simple benchmark that forecasts just as well as more complicated models.
    Keywords: Asbestos, Other exposures
    Print ISSN: 1351-0711
    Electronic ISSN: 1470-7926
    Topics: Medicine
    Published by BMJ Publishing Group
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  • 4
    Publication Date: 2016-04-15
    Description: Objectives Exposure to asbestos fibres increases the risk of mesothelioma and lung cancer. Although the vast majority of mesothelioma cases are caused by asbestos exposure, the number of asbestos-related lung cancers is less clear. This number cannot be determined directly as lung cancer causes are not clinically distinguishable but may be estimated using varying modelling methods. Methods We applied three different modelling methods to the Dutch population supplemented with uncertainty ranges (UR) due to uncertainty in model input values. The first method estimated asbestos-related lung cancer cases directly from observed and predicted mesothelioma cases in an age-period-cohort analysis. The second method used evidence on the fraction of lung cancer cases attributable (population attributable risk (PAR)) to asbestos exposure. The third method incorporated risk estimates and population exposure estimates to perform a life table analysis. Results The three methods varied substantially in incorporated evidence. Moreover, the estimated number of asbestos-related lung cancer cases in the Netherlands between 2011 and 2030 depended crucially on the actual method applied, as the mesothelioma method predicts 17 500 expected cases (UR 7000–57 000), the PAR method predicts 12 150 cases (UR 6700–19 000), and the life table analysis predicts 6800 cases (UR 6800–33 850). Conclusions The three different methods described resulted in absolute estimates varying by a factor of ~2.5. These results show that accurate estimation of the impact of asbestos exposure on the lung cancer burden remains a challenge.
    Keywords: Asbestos, Other exposures
    Print ISSN: 1351-0711
    Electronic ISSN: 1470-7926
    Topics: Medicine
    Published by BMJ Publishing Group
    Location Call Number Limitation Availability
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