Stratification of US adults based on exposure to lead, cadmium, manganese, and mercury and their effects on cardiovascular diseases.
Aim: Although previous studies have reported the predictive role of heavy metals in diseases, only a few studies have focused on the relationship between exposure to metal mixtures and cardiovascular diseases (CVDs). This study aimed to explore the impact of exposure to metal mixtures (lead, cadmium...
| Publicado en: | Journal of Public Health: From Theory to Practice (2198-1833) Vol. 32; no. 11; pp. 2211 - 2222 |
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| Autores principales: | , |
| Formato: | Artículo |
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Springer Nature
Nov2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=181514970&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 181514970 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 21981833 NENI jtl: Journal of Public Health: From Theory to Practice (2198-1833) issn: 21981833 maglogo: N pubinfo: dt: Nov2024 vid: 32 iid: 11 pid: 237 pub: Springer Nature artinfo: ui: 181514970 10.1007/s10389-023-01939-9 ppf: 2211 ppct: 11 formats: tig: atl: Stratification of US adults based on exposure to lead, cadmium, manganese, and mercury and their effects on cardiovascular diseases. aug: au: Chen, Mingzhuang Zha, Jingru affil: https://ror.org/04c4dkn09 Divison of Medical Affairs, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, 230601, Hefei, Anhui, China https://ror.org/04c4dkn09 Office of Party, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, 17 Lujiang Road, Luyang, 230601, Hefei, Anhui, China su: Myocardial infarction risk factors Manganese Statistical models Cadmium Mercury (Element) Cardiovascular diseases risk factors Descriptive statistics Surveys Odds ratio Environmental exposure Metals Confidence intervals Lead Algorithms Regression analysis sug: subj: All other non-metallic mineral product manufacturing Ground or Treated Mineral and Earth Manufacturing Metal service centres All Other Metal Ore Mining Metal Coating, Engraving (except Jewelry and Silverware), and Allied Services to Manufacturers Coating, engraving, cold and heat treating and allied activities Metal Heat Treating Myocardial infarction risk factors Manganese Statistical models Cadmium Mercury (Element) Cardiovascular diseases risk factors Descriptive statistics Surveys Odds ratio Environmental exposure Metals Confidence intervals Lead Algorithms Regression analysis keyword: Cardiovascular disease Heavy metals exposure NHANES Stratification Unsupervised learning Cardiovascular disease Heavy metals exposure NHANES Stratification Unsupervised learning ab: Aim: Although previous studies have reported the predictive role of heavy metals in diseases, only a few studies have focused on the relationship between exposure to metal mixtures and cardiovascular diseases (CVDs). This study aimed to explore the impact of exposure to metal mixtures (lead, cadmium, manganese, and mercury) on CVDs. Patients and methods: Overall, we included 16,143 adults from the National Health and Nutrition Examination Survey 2011–2018. The K-medoids algorithm was used to stratify the study population after clustering of blood metals. Based on metal concentration subgroups, we constructed a generalized linear model and adjusted for multiple variables to further analyze the association between exposure to metal mixtures and CVDs. Results: Participants were divided into three strata by k-medoids. High-exposure, moderate-exposure, and low-exposure groups were identified according to the level of blood concentrations. After adjustment of the model, high exposure to metal mixtures significantly increased the risk of heart attack (odds ratio [OR] 1.63, 95% confidence interval [CI] 1.11–2.38) and stroke (OR 1.78, 95% CI 1.25–2.54). Conclusions: Exposure to high levels of metal mixtures may be a potential predictor of CVDs. Unsupervised clustering method-based stratification of the population using metal mixtures provides a reliable and promising method for the analysis of the relationship between metal mixtures and health endpoints. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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