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Association between heavy metals and risk of cardiovascular diseases in US adults with prediabetes from NHANES 2011鈥�2018

成人头条 volume听25, Article听number:听391 (2025) Cite this article

Abstract

Background

The association of plasma metals on the risk of cardiovascular diseases (CVD) in adults with prediabetes remains poorly investigated. To assess the association between plasma metal exposure and the risk of CVD in prediabetic adults in the United States using five plasma metals.

Methods

Five cycles of data (2011鈥�2012, 2013鈥�2014, 2015鈥�2016, and 2017鈥�2018) from the NHANES were adopted in this study. The plasma metals were measured in 1088 participants with prediabetes. We utilized multivariate logistic regression, WQS, and BKMR models to evaluate the associations between the five plasma metals and the risk of CVD.

Results

The risk of CVD in participants with prediabetes were found to link to the 2nd quartile, 3rd and 4th quartiles of cadmium on the basis of multivariate logistic model (OR鈥�=鈥�3.03, 95%CI: 1.17鈥�7.82, P<0.01). Moreover, the joint effect of the five metals on the risk of CVD participants with prediabetes were unveiled using WQS and BKMR models (OR鈥�=鈥�1.79, 95%CI: 1.15鈥�2.77, P<0.01). In addition, when the concentrations of the other four metals were controlled at the 25th, 50th, and 75th percentile, correspondingly, cadmium had a statistically significant positive association with the risk of CVD.

Conclusion

The exposure of metals documented by the five metals links to the risk of CVD in participants with prediabetes in the United States. Among all the five metals, cadmium has the strongest association with the risk of CVD in participants with prediabetes.

Peer Review reports

Introduction

Numerous studies have demonstrated a correlation between cardiovascular disease (CVD) and prediabetes [1,2,3]. It has been shown that the risk of CVD development is significantly higher in persistent prediabetic (without progression to diabetes) populations [4]. Therefore, unraveling the risk factors for CVD in prediabetic population remains a top priority.

Heavy metals are defined as metals with densities equal to or greater than 5听g/cm3 [5]. Due to the increasing urbanization and industrialization, a significant amount of heavy metal pollutants, originating from sources such as pesticides and fertilizers, fuel waste, and industrial wastewater, are being discharged into the environment directly or indirectly, surpassing the self-cleansing capacity of the environment [6]. As a result, there is an increased likelihood of human exposure to these heavy metal pollutants through various routes, including skin contact, dietary intake, drinking water, and fuel waste [7]. The occurrence of many diseases is related to heavy metals, including renal function, diabetes, metabolic syndrome, CVD, and mortality [8].

Numerous studies have demonstrated the impact of heavy metal exposure on CVD [3, 9,10,11,12]. Although the relationship between metal exposure and CVD varies across different research studies [13,14,15,16,17], the current evidence from meta-analyses predominantly supports an increased risk of CVD events associated with exposure to cadmium, lead, selenium, and mercury [18, 19]. In the present-day human lifestyle, there are increased opportunities for exposure to multiple heavy metals. It is important to note that these metals can interact with each other, exerting antagonistic and/or synergistic effects on health. Research has revealed that manganese uptake can be inhibited by cadmium [20], while the presence of manganese can reduce cadmium-induced hepatotoxicity [21]. Consequently, assessing the risk to health posed by exposure to a single metal alone cannot objectively reflect the actual scenario of human exposure to metal mixtures.

In patients with higher blood glucose, metals may have an impact on insulin production, secretion, release, and mode of action [22, 23]. Participants with higher normal blood glucose frequently exhibit decreased metabolism of vital trace metals [24]. Plasma copper levels are found to be higher in patients with elevated blood glucose compared to the normoglycemic population, while zinc levels are significantly decreased [25]. Studies have indicated that disturbances in metal element metabolism may vary with elevated blood glucose levels exceeding the normal range and fluctuations in metabolic control [26,27,28]. Therefore, metal exposure could be a potential key factor in the development of CVD in individuals with prediabetes. However, the precise physiological effects of many other metals in individuals with prediabetes remain unknown. People with abnormal blood sugar levels will have different levels of these metals compared to the normal population, and a large number of studies have focused on the effects of these five metals on CVD [18,19,20,21]. Furthermore, most studies have primarily assessed the impact of individual metals, with limited focus on the reality of simultaneous exposure to multiple metals by individuals [16, 17].

Therefore, the main objective of this study was to investigate whether plasma levels of metals are associated with the risk of developing CVD in pre-diabetic patients from NHANES 2011鈥�2018. The combined and interactive effects of co-exposure to multiple metals on the occurrence of CVD in pre-diabetic patients were also estimated.

Method

Study design and population

Four successive NHANES survey cycles (2011鈥�2012, 2013鈥�2014, 2015鈥�2016, and 2017鈥�2018) provided the data for this study鈥檚 analysis. The NHANES is a measure of the nationally representative health and nutrition status quo of children and adults in the United States. The NHANES features a multistage, stratified sampling strategy. The National Center for Health Statistics Research Ethics Review Board gave its approval to the NHANES survey []. Written consents were acquired by each participant. Prediabetes is identified by self-reported prediabetes status or having HbA1c between 5.7% and 6.4%, FBG between 100听mg/dL and 125听mg/dL [29,30,31]. We, first, included 6156 participants with prediabetes aged鈥夆墺鈥�20 years (age with CVD: 65 years, age with non鈥揅VD:51 years; males with CVD: 75%, males with non鈥揅VD:64%). Next, 5068 participants were excluded because they lacked information on CVD, heavy metals and covariates (age, gender, race, education level, the ratio of family income to poverty, family history of diabetes and heart attack, smoking status poverty income ratio, BMI, and physical activity). Finally, 1088 participants were enrolled in this study. The detailed selection of participants is shown in Fig.听1. The association between the content of each metal in the blood and the risk of CVD in 1088 participants was analyzed using logistic model, and the association between mixed metals and the risk of CVD was analyzed by BKMR and WQS models, and finally the metals associated with the risk of CVD were obtained.

Fig. 1
figure 1

Selection of participants in this study

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Assessment of blood heavy metals

Individuals were examined in mobile clinics, and blood samples were taken. The samples were processed and kept at -80听掳C until they were sent to the National Center for Environmental Health for analysis. All blood metal measurements were carried out by the Centers for Disease Control and Prevention鈥檚 National Center for Environmental Health. Strict quality control was implemented, adhering to a defined protocol that excluded metal background contamination during the material collection and storage processes. The amount of metal in the blood was measured using the inductively coupled plasma mass spectrometry (ICP-MS) technique [32]. After converting the five metal concentrations to natural logarithms, they were examined. To substitute the limit of detection (LOD) for metal concentrations below it, we calculated a number and divided it by the square root of two. For all metals selected in this study, the NHANES methodology substituted quantities below the detection limit with the limit divided by 鈭�2. Detail information of the NHANES laboratory procedure is available at .

Definition of CVD

The diagnosis of CVD was established by the use of a standardized medical condition questionnaire in conjunction with self-reported physician diagnoses obtained through individual interviews. The question that was asked to the participants was, 鈥淗ave a doctor or other health expert ever informed you that you have CHF/CHD/angina pectoris/MI/stroke?鈥�. If a person answered 鈥測es鈥� to any of the aforementioned questions, they were considered to have CVD. Previous studies have confirmed the validity of self-reported CVD [33].

Assessment of covariates

CVD-relating covariables were collected from previous epidemiological studies on CVD (Table听1) [34], namely (1) age; (2) gender [male, female]; (3) race [Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, and any other race]; (4) BMI (5) education level [less than 9th grade, 9-11th grade, high school graduate or equivalent, some college or AA degree, college graduate or above]; (6) poverty income ratio [<鈥�1.00, 1.00鈥�3.00, 鈮モ��3.00] [35]; (7) Physical activity level [active, inactive], according to the Centers for Disease Control and Prevention Physical Activity Guidelines for Americans, participants were classified as physically active if they had at least 150听min of moderate to vigorous physical activity per week; otherwise, they were classified as physically inactive [36]; (8) Family history of diabetes and heart attack [yes, no];9) Smoking status [never smoker, former smoker, or current smoker].

Table 1 Demographic characteristics of participants with pre-diabetes
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Statistical analysis

The chi-square test was used for the comparison of normally distributed continuous variables between different groups, and the t-test was used for comparison between different groups. In compliance with the NHANES analytical reporting rules, we valued the intricate survey design that allocated weights to each respondent in order to account for the oversampling of specific categories. A natural log transformation was used to convert skewed distributions of the concentrations of the five metals to normal distributions. Pearson correlation coefficients between the ln-transformed concentrations of the five metals were calculated.

For the five metals, we utilizedssss multivariate logistic regression to assess the associations between each of the metals and the risk of CVD by comparing the 2nd, 3rd, and 4th quartile of each of the metals with the first quartile. In Model 1, age, race/ethnicity and gender were adjusted; in Model 2, age, race/ethnicity, gender, poverty income ratio, BMI and education level were adjusted; while in Model 3, every covariate was adjusted. Then, we combined the Weighted Quantile Sum (WQS) [37] regression method to assess the joint effect of the five metals on the risk of CVD.

WQS regression is limited in measuring exposures relating to outcomes in the same direction. Therefore, on the basis of pre-set positive and negative WQS coefficients, we separately analyzed the association between the five metals and CVD in different directions. In WQS regression analysis, an amplification bias of unmeasured confounding may occur when there lacks prior consideration for the direction of correlation. The Bayesian Kernel Machine Regression (BKMR) combines Bayesian and statistical learning methods, iteratively regressing the exposure-response function through a Gaussian kernel function. BKMR can identify non-linear and non-additive relationships in chemicals [38]. Thus, we further used the BKMR to evaluate the joint effect of the five metals on the risk of CVD. Given the high association of the five metals in this study, we performed a hierarchical variable selection using the Markov chain Monte Carlo algorithm with 20,000 iterations. We calculated the posterior inclusion probabilities (PIPs) representing the probability of a metal being included in the mode. All of the above statistical models incorporated the covariates (age, gender, race, education level, poverty income ratio, weight, drinking status, physical activity, family history of diabetes and heart attack).

In this study, A threshold of P鈥�<鈥�0.05 (two-tailed) was established to determine statistical significance, the significance level in the WQS model was set at 0.025. Analyses were performed using SPSS and R. The WQS and BKMR were performed using the R packages 鈥済WQS鈥� (version 1.1.0) and 鈥渂kmr鈥� (version 0.2.0), correspondingly.

Results

Baseline characteristics of study participants

A total of 951 participants with CVD and 137 ones without CVD were included. The prevalence of CVD was 12.59%. The participants with CVD differed from those without CVD in age, gender, physical activity level, educational level, poverty income ratio, family history of heart attack and BMI (Table听1).

Association between each metal and the risk of CVD

Table听2 presents the outcomes of the multivariate logistic regression analysis. In model 1, we adjusted age, gender and race, ln-transformed Cd and Mn (as continuous variables) were positively associated with odds of CVD. After adjusting for covariates (model 2, model 3), the results of model 2 and model 3 were in line with the Cd content and the risk of CVD in model 1 (model 2: OR: 1.62; 95% CI: 1.21鈥�2.16; model 3: OR: 1.65; 95% CI: 1.15鈥�2.35), with stronger associations, the association between Mn content and the risk of CVD decreased after models 2 and 3 were adjusted (model 2: OR: 2.87; 95% CI: 1.11鈥�7.43; model 3: OR: 2.84; 95% CI: 1.14鈥�7.09), but still significant. We compared the 2nd, 3rd and 4th quartile of each metal with the first quartile correspondingly. Cd and the risk of CVD were shown to be positively correlated in multivariate logistic regression models that included categorical variables. Even after adjusting model 1, there was still a strong association between Cd and the risk of CVD. (OR: 3.20; 95% CI: 1.37鈥�7.45). In model 2, we additionally adjusted for BMI, educational level and PIR in Model 1, their association persisted (OR鈥�=鈥�3.51, 95%CI: 1.48鈥�8.32). In model 3, we further accounted for confounding factors including smoking status, physical activity, family history of diabetes, and history of heart attack, their association persisted (OR鈥�=鈥�3.03, 95%CI: 1.17鈥�7.82). However, it was discovered that the other four heavy metals had no connection to CVD. (Table听2).

Table 2 Multivariable logistic regression model assessing the contribution of five metals to the odds of increased CVD
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Multi-metal exposures and the risk of CVD

To investigate the association between metal combinations and the risk of CVD, we used WQS regression models. A substantial association was found between the risk of CVD and a quartile increase in the WQS index when examining the positive link between metal mixture and CVD (OR: 1.48, 95%CIs: 1.10鈥�1.98), we additionally adjusted for all covariates, their association persisted (OR鈥�=鈥�1.79, 95%CI: 1.15鈥�2.77) (Table听3). The weights of each metal are presented in Fig.听2, and detailed data are provided in Table听4. Furthermore, we explored the nonlinear connection between the joint of five blood heavy metals and the risk of CVD using the BKMR model. Table听5 summarizes the Posterior Inclusion Probabilities (PIPs) for each metal derived from the BKMR model. As shown in Fig.听3, when the 50th percentile of concentration of mixed metals was set as a reference, the risk of CVD increased with the increase of the concentration of mixed metals. If the concentration of a metal was altered while keeping the concentrations of the other metals at the 50th percentile, we found that cadmium positively linked to the risk of CVD; conversely, the other metals displayed either a negative association or no association with the risk of CVD (Fig.听4). We also explored the association between each metal and the risk of CVD by comparing the 75th percentile with the 25th percentile, when the concentrations of the other four metals were controlled at the 25th, 50th, and 75th percentile, correspondingly (Fig.听5). Notably, cadmium still had a statistically significant positive association with the risk of CVD.

Table 3 Joint effect of the five metals on the risk of CVD in the WQS model
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Fig. 2
figure 2

The weights of the five metals under mixed exposure evaluated by WQS in relation to CVD. CD: cadmium, PB: lead, HG: mercury, SE: selenium, MN: manganese

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Table 4 Weights of five metals in WQS
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Table 5 Posterior inclusion probabilities (PIPs) in BKMR
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Fig. 3
figure 3

Joint effect of the five metals on CVD risks in the BKMR model

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Fig. 4
figure 4

Connection between metal and estimated risk in CVD. MN: manganese, SE: selenium, HG: mercury, CD: cadmium, PB: lead

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Fig. 5
figure 5

This plot compares the continuous potential outcome of a single metal was at 75th vs. 25th percentiles, when all other metals were held at 25th, 50th, or 75th percentiles. MN: manganese, SE: selenium, HG: mercury, CD: cadmium, PB: lead

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Discussion

To the best of our knowledge, this study is the first to assess the association between incident the risk of CVD in people with prediabetes and plasma multiple metal levels. In individuals with prediabetes, we observed a significant association between mixed heavy metal levels and the risk of CVD. The majority of the findings in the secondary studies, which included two novel mixture modeling techniques (WQS and BKMR), were robust.

In this study, we have observed a strong positive association between plasma cadmium levels and the risk of CVD with prediabetes. Previous research has primarily focused on the impact of cadmium on the risk of CVD in the general population [16, 39, 40]. Cadmium is recognized as one of the most toxic environmental substances [40]. Exposure to cadmium occurs through industrial exposure, inhalation (particularly in active smokers), and consumption of contaminated food [41, 42]. High concentrations of cadmium are also found in a variety of foods. Especially in offal as well as shellfish [43,44,45]. Those with prediabetes had comparatively elevated plasma cadmium levels. According to a Systematic Review and Meta-Analysis (PRISMA) study, the risk of prediabetes was positively correlated with plasma cadmium level [46]. There is a potential association between cadmium, selenium, lead and the onset of prediabetes [47]. While the majority of cadmium is deposited in the kidneys, long-term exposure to cadmium has been linked to its accumulation in the pancreas, particularly in the 尾-islet cells. This accumulation can lead to pancreatic damage, resulting in elevated blood glucose levels and decreased insulin production [48]. Cadmium toxicity disrupts energy metabolism and antioxidant systems, causing mitochondrial damage and inflammation in the pancreatic 尾-cells [49]. Furthermore, cadmium may stimulate gluconeogenesis by reducing insulin sensitivity, increasing the activity of gluconeogenic enzymes, and altering the expression of glucose transporters, ultimately reducing glucose uptake [50]. In a recent animal study, cadmium-induced apoptosis in cardiomyocytes was observed, leading to the destruction of myonodules and myofibrils, as well as myocardial fibrosis and focal necrosis in mice [51].

According to the results of the current investigation, prediabetes with high vs. low plasma cadmium had a 62% higher risk of CVD. According to these results, plasma cadmium may have a negative impact on CVD disease in those with prediabetes. The potential mechanism might be that higher levels of blood cadmium in pre-diabetic patients compared to normoglycaemic people, Cd has been positively associated with the development of atherosclerosis through the same mechanisms of increased oxidative stress and inflammation, enhanced lipid synthesis, upregulation of adhesion molecules, and altered glycosaminoglycan synthesis [52,53,54]. These results show that in individuals with prediabetes, long-term exposure to single heavy metals (particularly Cd) or co-exposure to heavy metals is linked to the development of CVDs. Further research is needed to determine the exact mechanisms underlying the significant association between Cd and the risk of CVD in patients with prediabetes.

In this study, the association between Pb, Mn, Se, Hg, and the risk of CVD disease in patients with prediabetes was not statistically significant, despite existing literature suggesting an increased risk of CVD disease associated with these metals [55]. The inconsistent findings may be attributed to variations in metal concentrations across studies, differences in population characteristics, genetic susceptibility, and the influence of various confounding factors. In this study, the weighted contributions of Pb, Hg, Se, and Mn in the mixture of five metals were 0.16, 0.11, 0.09, and 0.01, respectively, with relatively lower weights in the blood. Furthermore, alterations in metal levels have been documented in patients with prediabetes compared to those with normal blood glucose levels, indicating abnormal metabolism of certain metals (such as Hg, Se, and Mn) in the prediabetic population. Therefore, further large-scale prospective studies are needed to elucidate the role of multiple metals in the mechanisms underlying CVD disease in prediabetic population.

WQS and BKMR, two recently developed models used to analyze the impact of co-exposure of environmental pollutant on health, were used in this study. However, the two models each have their own strengths and weaknesses. The WQS regression model focuses on the full-body burden of the five metals on the basis of weights determined by bootstrap sampling experience, thereby reflecting complex exposure in real life. Because the BKMR analysis can identify nonlinear effects [38], the BKMR analysis has more ability to capture the exposure-response association when other metals are fixed at a certain level than the WQS regression model. The kernel algorithm of the BKMR model limits the application of this model. Fixing other metals at a certain level to infer the exposure-response function is unsuitable for the estimation of the impact of joint exposure patterns at both high and low levels of metals.

In our study, we were the first team to explore the association between blood metal content and the risk of CVD in prediabetic populations, and the study used BKMR and WQS models to explore the association between mixed metals for the risk of CVD and exclude the role of other metals and the association between single metals for the risk of CVD, which is better than previous studies that did not consider the effects of other metals in the body and only studied the association between single metals on the risk of CVD [16, 56, 57]. In our study, we were the first research team to explore the associations between serum Pb, Cd, and Hg concentrations and mortality in CMM participants, elucidating the potential underlying mechanisms involved. There are several limitations in this study. First, owing to the cross-sectional study design, we cannot infer causality and rule out the possibility of reverse causality. Second, we are unable to trace the source of metals exposure, as NAHNES does not provide environmental testing data. Third, owing to the missing data, some missing data were likely to have important characteristics relevant to prediabetes, five heavy metals and CVD. Finally, NHANES cannot design trials based on the conditions required, owing to the restrictions of race and population cannot be generalized to the entire population, future studies need to change population and ethnicity to verify the generality of the study.

There is a strong association between metal combinations and CVD in people with prediabetes. Therefore, for prediabetic people, reducing contact with these metals should be the primary concern, which is of great significance for preventing the occurrence of CVD in prediabetic people.

Conclusions

In conclusion, our research shows a favorable association between metal combinations and people in the US who have prediabetes and a higher risk of CVD. Among all the five metals, cadmium has the strongest association with the risk of CVD in the adult population with prediabetes.

Data availability

The data that support the findings of this study are openly available in the National Centre for Health Statistics鈥� questionnaires, datasets, and related documentation at .

Abbreviations

BKMR:

Bayesian kernel machine regression

BMI:

Body Mass Index

CI:

Confidence interval

NHANES:

The National Health and Nutrition Examination Surveys

WQS:

Weighted quantile sum

Pb:

Lead

Cd:

Cadmium

Hg:

Mercury

Mn:

Manganese

Se:

Selenium

References

  1. Rijkelijkhuizen JM, Nijpels G, Heine RJ, Bouter LM, Stehouwer CD, Dekker JM. High risk of cardiovascular mortality in individuals with impaired fasting glucose is explained by conversion to diabetes: the Hoorn study. Diabetes Care. 2007;30(2):332鈥�6.

    听 听 听

  2. Brannick B, Wynn A, Dagogo-Jack S. Prediabetes as a toxic environment for the initiation of microvascular and macrovascular complications. Experimental biology and medicine. (Maywood NJ). 2016;241(12):1323鈥�31.

    听 CAS听 听

  3. Fayed A, Alzeidan R, Esmaeil S, Elmorshedy H, Ismail D, Elkouny R, et al. Cardiovascular Risk among Saudi adults with prediabetes: a Sub-cohort Analysis from the Heart Health Promotion (HHP) study. Int J Gen Med. 2022;15:6861鈥�70.

    听 听 听 听

  4. Gong Q, Zhang P, Wang J, An Y, Gregg EW, Li H, et al. Changes in mortality in people with IGT before and after the Onset of Diabetes during the 23-Year follow-up of the Da Qing diabetes Prevention Study. Diabetes Care. 2016;39(9):1550鈥�5.

    听 听 听 听

  5. Tchounwou PB, Yedjou CG, Patlolla AK, Sutton DJ. Heavy metal toxicity and the environment. Experientia supplementum. 2012;101:133鈥撯��64.

  6. Fan X, Lu X, Yu B, Zuo L, Fan P, Yang Y, et al. Risk and sources of heavy metals and metalloids in dust from university campuses: a case study of Xi鈥檃n, China. Environ Res. 2021;202:111703.

    听 CAS听 听 听

  7. Chen L, Sun Q, Peng S, Tan T, Mei G, Chen H, et al. Associations of blood and urinary heavy metals with rheumatoid arthritis risk among adults in NHANES, 1999鈥�2018. Chemosphere. 2022;289:133147.

    听 CAS听 听 听

  8. Domingo-Relloso A, Grau-Perez M, Briongos-Figuero L, Gomez-Ariza JL, Garcia-Barrera T, Due帽as-Laita A, et al. The association of urine metals and metal mixtures with cardiovascular incidence in an adult population from Spain: the Hortega Follow-Up study. Int J Epidemiol. 2019;48(6):1839鈥�49.

    听 听 听 听

  9. Shen Y, Hu Y, Liu L, Zhong J, Zhang Y, Wu S, et al. Association between the copper-to-zinc ratio and Cardiovascular Disease among Chinese adults: a China multi-ethnic cohort (CMEC) study. Cardiovasc Toxicol. 2024;24(10):1005鈥�17.

    听 CAS听 听 听

  10. Sardar MB, Raza M, Fayyaz A, Nadir MA, Nadeem ZA, Babar M. Environmental Heavy Metal Exposure and Associated Cardiovascular diseases in Light of the triglyceride glucose index. Cardiovasc Toxicol. 2024;24(11):1301鈥�9.

    听 CAS听 听 听

  11. Yang X, Luo M, Jiang Y. The regulatory effect of zinc on the association between periodontitis and atherosclerotic cardiovascular disease: a cross-sectional study based on the National Health and Nutrition Examination Survey. 成人头条 Oral Health. 2024;24(1):703.

    听 CAS听 听 听 听

  12. Li X, Ling J, Hu Q, Fang C, Mei K, Wu Y, et al. Association of serum copper (Cu) with cardiovascular mortality and all-cause mortality in a general population: a prospective cohort study. 成人头条. 2023;23(1):2138.

    听 CAS听 听 听 听

  13. Guo X, Su W, Li N, Song Q, Wang H, Liang Q, et al. Association of urinary or blood heavy metals and mortality from all causes, cardiovascular disease, and cancer in the general population: a systematic review and meta-analysis of cohort studies. Environ Sci Pollut Res Int. 2022;29(45):67483鈥�503.

    听 CAS听 听 听

  14. Shi L, Yuan Y, Xiao Y, Long P, Li W, Yu Y, et al. Associations of plasma metal concentrations with the risks of all-cause and cardiovascular disease mortality in Chinese adults. Environ Int. 2021;157:106808.

    听 CAS听 听 听

  15. Bergdahl IA, Ahlqwist M, Barregard L, Bj枚rkelund C, Blomstrand A, Skerfving S, et al. Mercury in serum predicts low risk of death and myocardial infarction in Gothenburg women. Int Arch Occup Environ Health. 2013;86(1):71鈥�7.

    听 CAS听 听 听

  16. Zhang A, Wei P, Ding L, Zhang H, Jiang Z, Mi L, et al. Associations of serum lead, cadmium, and mercury concentrations with all-cause and cause-specific mortality among individuals with cardiometabolic multimorbidity. Ecotoxicol Environ Saf. 2024;280:116556.

    听 CAS听 听 听

  17. Sun Y, Liu B, Rong S, Zhang J, Du Y, Xu G, et al. Association of Seafood Consumption and Mercury exposure with Cardiovascular and all-cause Mortality among US adults. JAMA Netw open. 2021;4(11):e2136367.

    听 听 听 听

  18. Chowdhury R, Ramond A, O鈥橩eeffe LM, Shahzad S, Kunutsor SK, Muka T, et al. Environmental toxic metal contaminants and risk of cardiovascular disease: systematic review and meta-analysis. BMJ (Clinical Res ed). 2018;362:k3310.

    听 听

  19. Hu XF, Lowe M, Chan HM. Mercury exposure, cardiovascular disease, and mortality: a systematic review and dose-response meta-analysis. Environ Res. 2021;193:110538.

    听 CAS听 听 听

  20. Hansen AF, Simi膰 A, 脜svold BO, Romundstad PR, Midthjell K, Syversen T, et al. Trace elements in early phase type 2 diabetes mellitus-A population-based study. The HUNT study in Norway. J Trace Elem Med Biology: Organ Soc Minerals Trace Elem (GMS). 2017;40:46鈥�53.

    听 CAS听 听

  21. Walter RM Jr., Uriu-Hare JY, Olin KL, Oster MH, Anawalt BD, Critchfield JW, et al. Copper, zinc, manganese, and magnesium status and complications of diabetes mellitus. Diabetes Care. 1991;14(11):1050鈥�6.

    听 听 听

  22. Abou-Seif MA, Youssef AA. Evaluation of some biochemical changes in diabetic patients. Clin Chim Acta. 2004;346(2):161鈥�70.

    听 CAS听 听 听

  23. Asemi Z, Jamilian M, Mesdaghinia E, Esmaillzadeh A. Effects of selenium supplementation on glucose homeostasis, inflammation, and oxidative stress in gestational diabetes: Randomized, double-blind, placebo-controlled trial. Nutrition (Burbank, Los Angeles County. Calif). 2015;31(10):1235鈥�42.

    CAS听 听

  24. Farrokhian A, Bahmani F, Taghizadeh M, Mirhashemi SM, Aarabi MH, Raygan F, et al. Selenium supplementation affects insulin resistance and serum hs-CRP in patients with type 2 diabetes and Coronary Heart Disease. Hormone Metabolic Res = Hormon- und Stoffwechselforschung = Horm et Metab. 2016;48(4):263鈥�8.

    听 CAS听 听

  25. Faghihi T, Radfar M, Barmal M, Amini P, Qorbani M, Abdollahi M, et al. A randomized, placebo-controlled trial of selenium supplementation in patients with type 2 diabetes: effects on glucose homeostasis, oxidative stress, and lipid profile. Am J Ther. 2014;21(6):491鈥�5.

    听 听 听

  26. Nguyen HD. An evaluation of the effects of mixed heavy metals on prediabetes and type 2 diabetes: epidemiological and toxicogenomic analysis. Environ Sci Pollut Res Int. 2023;30(34):82437鈥�57.

    听 CAS听 听 听

  27. Niedermayer F, Su Y, von Kr眉chten R, Thorand B, Peters A, Rathmann W, et al. Trajectories of glycaemic traits exhibit sex-specific associations with hepatic iron and fat content: results from the KORA-MRI study. Liver International: Official J Int Association Study Liver. 2023;43(10):2153鈥�66.

    听 CAS听 听

  28. Hong H, Lin X, Xu Y, Tong T, Zhang J, He H, et al. Cadmium induces ferroptosis mediated inflammation by activating Gpx4/Ager/p65 axis in pancreatic 尾-cells. Sci Total Environ. 2022;849:157819.

    听 CAS听 听 听

  29. Zou X, Zhou X, Zhu Z, Ji L. Novel subgroups of patients with adult-onset diabetes in Chinese and US populations. Lancet Diabetes Endocrinol. 2019;7(1):9鈥�11.

    听 听 听

  30. Zhang Y, Lu R, Wang J, Zhang X, Ma X. Trends in cardiovascular burden and mortality among adults with prediabetes in the US, 1999鈥�2018. Diabetes Vasc Dis Res. 2023;20(4):14791641231188704.

    听 听

  31. 5. Prevention or Delay of type 2 diabetes: standards of Medical Care in Diabetes-2018. Diabetes Care. 2018;41(Suppl 1):S51鈥�4.

    听

  32. Akinbami LJ, Chen TC, Davy O, Ogden CL, Fink S, Clark J et al. National Health and Nutrition Examination Survey, 2017-March 2020 Prepandemic File: Sample Design, Estimation, and Analytic Guidelines. Vital and health statistics Ser 1, Programs and collection procedures. 2022(190):1鈥�36.

  33. Zhang Q, Xiao S, Jiao X, Shen Y. The triglyceride-glucose index is a predictor for cardiovascular and all-cause mortality in CVD patients with diabetes or pre-diabetes: evidence from NHANES 2001鈥�2018. Cardiovasc Diabetol. 2023;22(1):279.

    听 听 听 听

  34. Yu J, Hua H, Yin M. U-shaped association between HbA1c and all-cause mortality in CVD patients with diabetes. Sci Rep. 2024;14(1):28386.

    听 CAS听 听 听 听

  35. Johnson CL, Paulose-Ram R, Ogden CL, Carroll MD, Kruszon-Moran D, Dohrmann SM et al. National health and nutrition examination survey: analytic guidelines, 1999鈥�2010. Vital and health statistics Series 2, Data evaluation and methods research. 2013(161):1鈥�24.

  36. Chen Y, Zhen C, Zeng L, Feng H, Wang J, Ai QYH, et al. Association of blood cadmium and physical activity with mortality: a prospective cohort study. Ecotoxicol Environ Saf. 2024;290:117541.

    听 听 听

  37. Carrico C, Gennings C, Wheeler DC, Factor-Litvak P. Characterization of Weighted Quantile Sum Regression for highly correlated data in a risk analysis setting. J Agricultural Biol Environ Stat. 2015;20(1):100鈥�20.

    听 听

  38. Bobb JF, Valeri L, Claus Henn B, Christiani DC, Wright RO, Mazumdar M, et al. Bayesian kernel machine regression for estimating the health effects of multi-pollutant mixtures. Biostatistics (Oxford England). 2015;16(3):493鈥�508.

    听 听 听

  39. Mansouri B, Rezaei A, Sharafi K, Azadi N, Pirsaheb M, Rezaei M, et al. Mixture effects of trace element levels on cardiovascular diseases and type 2 diabetes risk in adults using G-computation analysis. Sci Rep. 2024;14(1):5743.

    听 CAS听 听 听 听

  40. Verzelloni P, Urbano T, Wise LA, Vinceti M, Filippini T. Cadmium exposure and cardiovascular disease risk: a systematic review and dose-response meta-analysis. Environ Pollution (Barking Essex: 1987). 2024;345:123462.

    听 CAS听 听

  41. Snoj Tratnik J, Kocman D, Horvat M, Andersson AM, Juul A, Jacobsen E, et al. Cadmium exposure in adults across Europe: results from the HBM4EU Aligned studies survey 2014鈥�2020. Int J Hyg Environ Health. 2022;246:114050.

    听 CAS听 听 听

  42. Filippini T, Michalke B, Malagoli C, Grill P, Bottecchi I, Malavolti M, et al. Determinants of serum cadmium levels in a Northern Italy community: a cross-sectional study. Environ Res. 2016;150:219鈥�26.

    听 CAS听 听 听

  43. Nordberg GF, Fowler BA, Nordberg M, Friberg L, editors. Handbook on the toxicology of metals, 3rd Edition. 2007.

  44. Menke A, Muntner P, Silbergeld EK, Platz EA, Guallar E. Cadmium levels in urine and mortality among U.S. adults. Environ Health Perspect. 2009;117(2):190鈥�6.

    听 CAS听 听 听

  45. Nawrot TS, Staessen JA, Roels HA, Munters E, Cuypers A, Richart T, et al. Cadmium exposure in the population: from health risks to strategies of prevention. Biometals: Int J role Metal ions Biology Biochem Med. 2010;23(5):769鈥�82.

    听 CAS听 听

  46. Filippini T, Wise LA, Vinceti M. Cadmium exposure and risk of diabetes and prediabetes: a systematic review and dose-response meta-analysis. Environ Int. 2022;158:106920.

    听 CAS听 听 听

  47. Weiss MC, Sun J, Jackson BP, Turyk ME, Wang L, Brown EL, et al. Accelerated longitudinal glycemic changes in relation to urinary Toxic/Essential metals and Metal mixtures among Mexican americans living in Starr County, Texas. Diabetes Care. 2024;47(11):1908鈥�15.

    听 CAS听 听 听

  48. Tinkov AA, Filippini T, Ajsuvakova OP, Aaseth J, Gluhcheva YG, Ivanova JM et al. The role of cadmium in obesity and diabetes. Sci Total Environ. 2017;601鈥�2:741鈥�55.

  49. Buha A, 膼uki膰-膯osi膰 D, 膯ur膷i膰 M, Bulat Z, Antonijevi膰 B, Moulis JM et al. Emerging links between Cadmium exposure and insulin resistance: Human, Animal, and cell Study Data. Toxics. 2020;8(3).

  50. Edwards J, Ackerman C. A review of diabetes Mellitus and exposure to the Environmental Toxicant Cadmium with an emphasis on likely mechanisms of action. Curr Diabetes Rev. 2016;12(3):252鈥�8.

    听 CAS听 听 听 听

  51. Chou SH, Lin HC, Chen SW, Tai YT, Jung SM, Ko FH, et al. Cadmium exposure induces histological damage and cytotoxicity in the cardiovascular system of mice. Food Chem Toxicology: Int J Published Br Industrial Biol Res Association. 2023;175:113740.

    听 CAS听 听

  52. Buha A, Matovic V, Antonijevic B, Bulat Z, Curcic M, Renieri EA et al. Overview of cadmium thyroid disrupting effects and mechanisms. Int J Mol Sci. 2018;19(5).

  53. Nakagawa H, Nishijo M, Morikawa Y, Miura K, Tawara K, Kuriwaki J, et al. Urinary cadmium and mortality among inhabitants of a cadmium-polluted area in Japan. Environ Res. 2006;100(3):323鈥�9.

    听 CAS听 听 听

  54. Tinkov AA, Filippini T, Ajsuvakova OP, Skalnaya MG, Aaseth J, Bj酶rklund G, et al. Cadmium and atherosclerosis: a review of toxicological mechanisms and a meta-analysis of epidemiologic studies. Environ Res. 2018;162:240鈥�60.

    听 CAS听 听 听

  55. Cai X, Zhang Y, Li M, Wu JH, Mai L, Li J, et al. Association between prediabetes and risk of all cause mortality and cardiovascular disease: updated meta-analysis. BMJ (Clinical Res ed). 2020;370:m2297.

    听

  56. Lu Y, Lu L, Zhang G, Zhang W, Cheng Y, Tong M. Serum 25-hydroxyvitamin D mediates the association between heavy metal exposure and cardiovascular disease. 成人头条. 2024;24(1):542.

    听 CAS听 听 听 听

  57. Wu YJ, Wang SB, Sun JT, Gu LF, Wang ZM, Deng B, et al. Association between urinary cadmium level and subclinical myocardial injury in the general population without cardiovascular disease aged鈥夆墺鈥�50 years. Environ Sci Pollut Res Int. 2023;30(31):77551鈥�9.

    听 CAS听 听 听

Acknowledgements

The authors thank the members of the National Center for Health Statistics, Centers for Disease Control and Prevention, for gathering the data and making it available for public use. The authors also thank the participants involved in the survey.

Funding

Yawen Liu is supported by National Natural Science Foundation of China (Grant No. 81973120); National Key R&D Program of China (Grant No. #2018YFC1311600); and Project for Improving Capability of Health Science and Technology in Jilin Province (Grant No. 2021JC038).

Author information

Authors and Affiliations

  1. Department of Epidemiology and Biostatistics, School of Public Health of Jilin University, Changchun, 130021, China

    Sijia Yang,听Zhuoshuai Liang,听Yue Qiu,听Xiaoyang Li,听Yuyang Tian听&听Yawen Liu

  2. State Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Diseases, Key Laboratory for Zoonosis Research of the Ministry of Education, School of Public Health, Jilin University, Changchun, 130062, China

    Yawen Liu

Authors
  1. Sijia Yang

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  2. Zhuoshuai Liang

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  3. Yue Qiu

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  4. Xiaoyang Li

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  5. Yuyang Tian

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  6. Yawen Liu

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Contributions

Sijia Yang, Zhuoshuai Liang, Yue Qiu, Xiaoyang Li, Yuyang Tian, Yawen Liu designed the study and initial analysis plan. Sijia Yang, Zhuoshuai Liang, Yue Qiu contributed to the statistical analysis. Xiaoyang Li, Yuyang Tian and Yawen Liu contributed to discussion, Sijia Yang and Zhuoshuai Liang wrote the draft of the manuscript. All authors participated in revising the manuscript and approved the final manuscript. Sijia Yang, Zhuoshuai Liang, Xiaoyang Li, and Yue Qiu are the guarantors of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Corresponding author

Correspondence to Yawen Liu.

Ethics declarations

Ethics approval and consent to participate

All participants gave written informed consent before enrollment in the study, which was conducted in accordance with the principles of the Declaration of Helsinki, and the NHANES data collection was approved by the Research Ethics Review Board of the National Center for Health Statistics [16]. The NHANES has strict protocols and procedures in place to ensure confidentiality and protect against identification. As our study was a secondary data analysis, which lacked personal identifiers, and the data in NHANES are freely accessible to the public on the web [39], it did not require review by an institutional review board [16]. The National Center for Health Statistics Research Ethics Review Boa1/13/2025rd gave its approval to the NHANES survey. Written consents were acquired by each participant.

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Not applicable.

Competing interests

The authors declare no competing interests.

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Yang, S., Liang, Z., Qiu, Y. et al. Association between heavy metals and risk of cardiovascular diseases in US adults with prediabetes from NHANES 2011鈥�2018. 成人头条 25, 391 (2025). https://doi.org/10.1186/s12889-025-21552-7

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  • DOI: https://doi.org/10.1186/s12889-025-21552-7

Keywords

成人头条

ISSN: 1471-2458

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