ArticleFrontiers in cardiovascular medicine2024
The association between blood metals and cardiovascular diseases: findings from National Health and Nutrition Examination Survey 2011-2020.
Article in Frontiers in cardiovascular medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Associations of selenium status with all-cause and cause-specific mortality: a systematic review and meta-analysis of cohort studies.Redox biology · 2025Pooled it
- An Exploration of Machine Learning Methods in Human Biomonitoring.International journal of environmental research and public health · 2026Review
- Association between cardiometabolic index and infertility risk: a cross-sectional analysis of NHANES data (2013-2018).BMC public health · 2025Article
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Authors and funding
11 authors.
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Abstract
Objectives: Previous studies have examined the relationship between cardiovascular diseases (CVDs) and blood metal levels. However, fewer studies have investigated the role of the combinations of blood metals on CVDs. In the current study, our aim is to explore the roles of specific blood metals and further develop a model to differentiate between healthy participants and CVD patients using database from the National Health and Nutrition Examination Survey (NHANES). Methods: Data from the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2020 were collected and utilized in the present study. Demographic characteristics and examination results were gathered and analyzed to compare CVD and non-CVD participants. Logistic regression and random forest analyses were employed to determine the odds ratios and the effects of various blood metals on CVDs. Results: A total of 23,448 participants were included and analyzed. Participants were divided into CVD ( Conclusions: In summary, blood metals may play an important role in the onset and progression of CVDs, and they can be used to develop a predictive model for CVDs, which might be beneficial for the identification and early diagnosis of CVDs.
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