The association between blood metals and cardiovascular diseases: findings from National Health and Nutrition Examination Survey 2011-2020.

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 (n = 2,676, 11.41%) and Non-CVD (N = 20,772, 88.59%) groups. A significant difference in the increased odds ratio of CVDs and higher blood Lead levels was found in the logistic analysis [OR (95% CI) = 13.545 (8.470-21.662) P < 0.001]. Although this significance blunted in the adjusted model, blood lead levels could be identified as the most important score through the random forest model in distinguishing cardiovascular diseases. In addition, the odds ratio of CVDs in logistic regression was 1.029 (95% CI: 1.022-1.035) for participants with higher blood cadmium levels (p < 0.001). The odds ratio increased [OR (95% CI) = 1.041 (95% CI: 1.032-1.049) P < 0.001] after the necessary adjustments were made for the gender, age, BMI, race and education background. In addition, blood selenium seems to be a protective factor of CVDs as the odds ratios were 0.650 and 0.786 in the crude and adjusted models, respectively. Additionally, the AUC was 0.91 in the predivtive model made by using the data of clinical indices and blood metals.

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.