Associations of Exposure to 56 Serum Trace Elements with the Prevalence and Severity of Acute Myocardial Infarction: Omics, Mixture, and Mediation Analysis

Abstract

Several studies have reported associations between specific heavy metals and essential trace elements and acute myocardial infarction (AMI). However, there is limited understanding of the relationships between trace elements and AMI in real-life co-exposure scenarios, where multiple elements may interact simultaneously. This cross-sectional study measured serum levels of 56 trace elements using inductively coupled plasma mass spectrometry. We identified individual trace elements linked to AMI using four feature selection methods and evaluated their associations with AMI prevalence and severity through multiple-element logistic regression. Restricted cubic spline analysis was employed to examine non-linear associations. Additionally, we explored the associations between trace element mixtures and AMI prevalence and severity using Bayesian kernel machine regression (BKMR) and element risk score (ERS). Finally, we investigated the potential mechanisms linking trace element exposure to AMI. We detected stable positive associations and linear relationships between Cu and Rb and AMI prevalence and severity. Furthermore, lower Fe concentrations were associated with higher AMI prevalence, while higher Sb concentrations were linked to greater AMI severity. Both BKMR and ERS models indicated positive associations between trace element mixtures and AMI prevalence and severity. Mediation analysis suggested that high-sensitivity C-reactive protein partially mediated the associations between trace elements and AMI prevalence and severity. We provide the first epidemiological evidence of the associations between serum trace element mixtures and AMI prevalence and severity. Under conditions of trace element co-exposure, Cu, Rb, Fe, and Sb were closely associated with AMI. Additionally, our results indicate that hsCRP (inflammation) may be a potential mechanism linking trace elements to AMI.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12011-024-04509-6.

Introduction

Acute myocardial infarction (AMI) is a severe clinical emergency marked by myocardial necrosis resulting from extended ischemia and hypoxia in the coronary arteries [1]. The global burden of cardiovascular diseases, including AMI, has shifted predominantly toward low- and middle-income countries, where deaths due to these conditions currently exceed 80% of the worldwide total. According to World Bank estimates, the number of patients with AMI in China is projected to reach 23 million by 2030 [2–4].

Growing evidence indicates that exposure to heavy metals such as Pb, Cd, and As is a critical risk factor for cardiovascular diseases, including subclinical atherosclerosis, coronary artery stenosis, and calcification, which may increase the risk of cardiovascular mortality [5, 6]. Additionally, several studies have explored the relationships between essential trace elements and AMI [7, 8]. However, real-life exposures frequently involve a broader spectrum of elements beyond those typically examined. Furthermore, the effects of combined exposure to multiple elements on AMI are not yet fully understood.

High-sensitivity C-reactive protein (hsCRP) is well-recognized as a marker of inflammation. Elevated hsCRP levels contribute substantially to the development and progression of atherosclerotic plaques and have been linked to adverse cardiovascular events following acute coronary syndrome [9–11]. Additionally, numerous trace elements are associated with inflammation [12–14]. However, the exact mechanisms linking these trace elements with AMI are yet to be elucidated comprehensively.

To narrow these gaps in knowledge, we conducted a population-based cross-sectional study. Using inductively coupled plasma mass spectrometry (ICP-MS) technology, we measured 62 elements in both patients with AMI and controls and identified the associations of trace element concentrations with AMI prevalence and severity.

Materials and Methods

Study Design and Population

This cross-sectional study was conducted at the Second Affiliated Hospital of Nanjing Medical University from October 2015 to May 2020. AMI was defined by two independent cardiovascular physicians based on the participant’s symptoms at admission, myocardial enzyme profile within 24 h of admission, electrocardiography, and coronary angiography [15]. The severity of AMI was assessed using the Gensini score, which was divided into tertiles among individuals with AMI [16, 17]. Controls were defined as individuals who underwent a physical examination using the same questionnaire and clinical assessment, had no noticeable stenosis in the coronary arteries, and had no personal or family history of coronary artery disease.

Exclusion criteria included coronary artery spasm; valvular heart disease; type 1 diabetes mellitus; severe infectious, autoimmune, or malignant diseases; significant hepatic or renal dysfunction; and recent surgery or trauma within the past 6 months. Of the 1578 participants, we ultimately included 195 individuals who satisfied the inclusion criteria and had complete data for elementomic analysis.

The study adhered to the Declaration of Helsinki standards and was approved by the Medical Ethics Committee of the Second Affiliated Hospital of Nanjing Medical University (2011-KY-002). All patients provided written informed consent.

Elemental Detection

The serum element concentrations were analyzed as described in followed our previous reports [18, 19]. Briefly, fasting whole-blood samples were collected before coronary angiography. Within 1 h of collection, the whole-blood samples were centrifuged at 3500 rpm for 10 min. The supernatants were then carefully transferred to new centrifuge tubes and centrifuged again at 12,000 rpm for 10 min. The resulting serum samples were preserved at − 80 °C until analysis. Before processing, 100 μL of serum sample was transferred to 1.5-mL centrifuge tubes containing a solution of 1% nitric acid, 40 ppb Li, 20 ppb Rh, 20 ppb In, and 20 ppb Re. Samples were then thawed at 4 °C for approximately 1 h.

Sixty-two elements were analyzed in serum samples using an iCAP Qc ICP-MS system (Agilent 7700 × ICP-MS, USA) (Fig. S1). Quality control measures included standard and serum quality control samples every ten samples, with the testing order randomized. The relative standard deviation (SD) for serum quality control samples was maintained below 10% (Table S1). Correlation coefficients (R-values) for the calibration standard curves of 62 elements were all above 0.99, except for Ge, which had an R-value of 0.98 (Table S1). The limit of detection (LOD) was calculated as three times the mean of ten consecutive blank measurements. For elements with concentrations lower than the LOD, estimates were made by dividing the LOD by the square root of 2. In the final analysis, we excluded elements with a detection rate below 90% and common macro-elements such as Na, Mg, K, and Ca (Table S1).

Information Collection

We collected the following information using a structured questionnaire: (a) personal basic data: age, gender, body mass index (BMI), and smoking status; (b) medical status: hypertension, diabetes, cerebral infarction, history of coronary artery stent implantation, and renal function; (c) medication history: aspirin, clopidogrel, beta-blockers, angiotensin-converting enzyme inhibitors or angiotensin receptor blockers, calcium channel blockers, and lipid-lowering drugs. Levels of serum creatinine (SCr), hsCRP, and other biochemical metrics were determined through laboratory analysis of fasting blood samples collected before coronary angiography. BMI was determined using height and weight measurements. Smoking status, including both current and former smokers, was defined as daily smoking for more than 6 months. The estimated glomerular filtration rate (eGFR) was calculated based on SCr levels, age, and gender [20].

Statistical Analysis

Continuous variables are presented as mean ± SD, with comparisons between groups performed using the t-test. Categorical variables are presented as frequencies (n) and percentages (%), with differences between groups assessed using the chi-square test. Based on baseline characteristics and relevant literature, age, sex, smoking behavior, stent implantation, and eGFR were identified as covariates in the statistical model [21–23]. Before performing statistical analyses, all elemental concentrations exhibiting a right-skewed distribution were log-transformed. Correlations between log-transformed elemental concentrations were assessed using Spearman’s rank correlation.

Given the moderate-to-high intercorrelation observed across the spectrum of elements, we used orthogonal partial least squares discriminant analysis, least absolute shrinkage and selection operator regression, weighted quantile sum regression, and single-element logistic regression to identify trace elements significantly associated with AMI [24, 25]. Subsequently, we applied multiple-element binary logistic regression and multiple-element ordinal logistic regression models to assess the independent associations of selected trace elements with AMI prevalence and severity, respectively. Restricted cubic spline (RCS) models with three knots were used to examine the non-linear associations of trace elements with AMI prevalence and severity [26]. We used a hierarchical Bayesian kernel machine regression (BKMR) to explore the associations of trace element mixtures with AMI prevalence and severity, with the hierarchy based on principal component analysis [27]. The element risk score (ERS) was used to validate the associations of trace element mixtures with AMI prevalence and severity, with ERS weights determined using the regression coefficients from the multiple logistic regression model, as shown in the equation below: ERS = ${\sum }_{k=1}^K{\beta }_k{E}_k+{\sum }_{i=1}^{K-1}{\sum }_{j=i+1}^K{\beta }_{\mathit{ij}}{E}_i{E}_j$ [18]. Finally, we assessed the potential mediating effects of hsCRP on the associations between individual and mixed trace elements with AMI prevalence and severity using parallel mediation models and structural equation models [28, 29].

All statistical analyses were performed using R software, v4.1.0 (R Foundation for Statistical Computing). Statistical significance was considered to be achieved with two-sided P-values below 0.05.

Results

General Characteristics

Table 1 presents the general characteristics of the 195 participants. Compared with the controls, the AMI group had decreased renal function and increased hsCRP levels. Table S2 presents the distribution of trace elements in serum, showing significant differences in the levels of 17 trace elements between the two groups. Additionally, we observed moderate to high correlations among 56 trace elements, with the pairwise correlation coefficients displaying a right-skewed distribution (Fig. S2).

Table 1.

Detailed description of the study population

Variables	Total	Control	AMI	P-value
	(N = 195)	(N = 101)	(N = 94)
Age (year), mean (SD)	61.54 ± 9.72	60.56 ± 7.26	62.60 ± 11.75	0.152
Gender, n (%)				0.084
Female	47 (24.10)	30 (29.70)	17 (18.09)
Male	148 (75.90)	71 (70.30)	77 (81.91)
BMI (kg/m2), mean (SD)	25.26 ± 3.37	25.28 ± 2.94	25.24 ± 3.80	0.942
Smoking status, n (%)				0.111
No	142 (72.82)	79 (78.22)	63 (67.02)
Yes	53 (27.18)	22 (21.78)	31 (32.98)
TC (mmol/L), mean (SD)	4.27 ± 0.96	4.25 ± 0.97	4.29 ± 0.95	0.805
TG (mmol/L), mean (SD)	1.82 ± 1.16	1.77 ± 1.08	1.87 ± 1.25	0.569
HDL-C (mmol/L), mean (SD)	1.67 ± 6.82	1.17 ± 0.33	2.19 ± 9.82	0.316
LDL-C (mmol/L), mean (SD)	2.59 ± 0.81	2.51 ± 0.79	2.68 ± 0.83	0.134
BUN (mmol/L), mean (SD)	5.57 ± 1.89	5.13 ± 1.19	6.04 ± 2.34	< 0.001*
SCr (μmol/L), mean (SD)	82.60 ± 61.54	72.56 ± 16.89	93.40 ± 85.82	0.023*
hsCRP (mg/L), mean (SD)	8.84 ± 20.79	3.66 ± 9.86	14.42 ± 27.14	< 0.001*
Aspirin, n (%)				< 0.001*
No	68 (34.87)	54 (53.47)	14 (14.89)
Yes	127 (65.13)	47 (46.53)	80 (85.11)
Clopidogrel, n (%)				< 0.001*
No	121 (62.05)	91 (90.10)	30 (31.91)
Yes	74 (37.95)	10 (9.90)	64 (68.09)
ACEI/ARB, n (%)				0.091
No	146 (74.87)	70 (69.31)	76 (80.85)
Yes	49 (25.13)	31 (30.69)	18 (19.15)
Beta blocker, n (%)				0.759
No	142 (72.82)	75 (74.26)	67 (71.28)
Yes	53 (27.18)	26 (25.74)	27 (28.72)
CCB, n (%)				0.017*
No	160 (82.05)	76 (75.25)	84 (89.36)
Yes	35 (17.95)	25 (24.75)	10 (10.64)
Lipid-lowering drug, n (%)				< 0.001*
No	57 (29.23)	48 (47.52)	9 (9.57)
Yes	138 (70.77)	53 (52.48)	85 (90.43)
eGFR (mL/min/1.73 m2), mean (SD)	91.12 ± 22.30	94.64 ± 12.47	87.33 ± 29.03	0.026*
Hypertension, n (%)				0.942
No	70 (35.90)	37 (36.63)	33 (35.11)
Yes	125 (64.10)	64 (63.37)	61 (64.89)
Diabetes, n (%)				0.102
No	150 (76.92)	83 (82.18)	67 (71.28)
Yes	45 (23.08)	18 (17.82)	27 (28.72)
Cerebral infarction, n (%)				1.000
No	176 (90.26)	91 (90.10)	85 (90.43)
Yes	19 ( 9.74)	10 (9.90)	9 (9.57)
Stent implantation, n (%)				< 0.001*
No	170 (87.18)	101 (100.00)	69 (73.40)
Yes	25 (12.82)	0 (0.00)	25 (26.60)
CK-MB (ng/mL), mean (SD)	17.11 ± 38.62	1.87 ± 1.32	33.49 ± 50.85	< 0.001*
cTnI (ng/mL), mean (SD)	3.23 ± 11.98	0.17 ± 0.34	6.53 ± 16.68	< 0.001*
SMB (ng/mL), mean (SD)	114.28 ± 385.68	43.40 ± 45.14	190.44 ± 544.79	0.011*
Gensini score, mean (SD)	27.60 ± 36.78	0.93 ± 1.78	56.26 ± 34.88	< 0.001*

AMI acute myocardial infarction, BMI body mass index, TC total cholesterol, TG triglyceride, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, BUN blood urea nitrogen, SCr serum creatinine, hsCRP high-sensitivity C-reactive protein, ACEI/ARB angiotensin-converting enzyme inhibitors or angiotensin receptor blocker, CCB calcium channel blocker, eGFR estimated glomerular filtration rate, CK-MB creatine kinase isoenzyme MB, cTnI cardiac troponin I, SMB serum myoglobin

^*P-value < 0.05

Selection of Serum Trace Elements

Figure 1 shows the use of four models to progressively identify key trace elements associated with AMI. Despite slight variations in ranking the importance of trace elements by different methods, several of the most relevant trace elements exhibited stability across all models. Ultimately, Fe, Cu, Rb, Nb, Mo, Sb, and Gd in serum were identified as important factors associated with AMI.

Fig. 1.

The screening models for serum trace elements associated with acute myocardial infarction. Model 1, unadjusted model; Model 2, adjusted for age, gender, smoking status, stent implantation; Model 3, additionally, adjusted for eGFR. OPLS-DA, orthogonal partial least squares discriminant analysis; LASSO, least absolute shrinkage and selection operator; WQS, weighted quantile sum; OR, odds ratio; CI, confidence interval

Independent Associations of Serum Trace Elements with AMI Prevalence and Severity

Multiple-element logistic regression was used to evaluate the independent associations of trace elements with the prevalence and severity of AMI. In models adjusted for covariates, Mo showed a significant association with AMI prevalence, while Sb and Mo showed a marginally significant association with AMI severity. In contrast, Cu and Rb showed significant positive associations with both AMI prevalence and severity across all three models. Fe exhibited a significant negative association solely with AMI prevalence (Fig. 2). All variance inflation factors in these models were below 2 (Fig. 2).

Fig. 2.

Multiple logistic regression of serum trace elements with AMI prevalence and severity. Model 1, unadjusted model; Model 2, adjusted for age, gender, smoking status, stent implantation; Model 3, additionally, adjusted for eGFR. OR, odds ratio; CI, confidence interval

Non-linear Associations of Serum Trace Elements with AMI Prevalence and Severity

The RCS analysis detected no significant non-linear associations between Fe, Cu, Rb, Nb, Mo, Sb, and Gd and the prevalence or severity of AMI. As illustrated in Fig. 3, the estimated curves in the AMI prevalence model revealed linear correlations with increasing trends for Cu (P-overall < 0.001, P-non-linear = 0.469), Rb (P-overall = 0.004, P-non-linear = 0.801), and Sb (P-overall = 0.006, P-non-linear = 0.853). Conversely, Fe (P-overall = 0.035, P-non-linear = 0.523), Mo (P-overall = 0.036, P-non-linear = 0.319), and Gd (P-overall = 0.017, P-non-linear = 0.976) showed linear correlations with decreasing trends, while Nb (P-overall < 0.001, P-non-linear = 0.047) displayed an approximately linear correlation with a decreasing trend.

Fig. 3.

Non-linear associations between serum trace elements with AMI prevalence and severity. These models were adjusted for age, gender, smoking status, eGFR, and stent implantation

In the AMI severity model, Cu (P-overall = 0.001, P-non-linear = 0.463), Rb (P-overall = 0.011, P-non-linear = 0.159), and Sb (P-overall = 0.001, P-non-linear = 0.497) showed linear correlations with increasing trends. Nb (P-overall = 0.001, P-non-linear = 0.083) and Gd (P-overall = 0.032, P-non-linear = 0.179) showed linear correlations with decreasing trends, while Fe (P-overall = 0.072, P-non-linear = 0.592) and Mo (P-overall = 0.063, P-non-linear = 0.361) displayed borderline statistical significance.

Associations of Serum Trace Element Mixtures with AMI Prevalence and Severity

To explore the combined effects of various trace elements on the prevalence and severity of AMI, we employed a BKMR model incorporating serum levels of Fe, Cu, Rb, Nb, Mo, Sb, and Gd. Our analysis revealed that as the concentration of these trace elements increased, both the prevalence and severity of AMI increased. Notably, the risk of AMI prevalence was statistically significant at the 40th percentile, while the risk of progression was significant from the 30th to the 55th percentile (Fig. 4). Hierarchical variable selection models revealed that the group posterior inclusion probabilities for both AMI prevalence and severity models exceeded 0.8. In the AMI prevalence model, Fe, Cu, and Rb were the top elements with conditional posterior inclusion probabilities (cPIPs) of 0.29, 0.76, and 1.00, respectively. For AMI severity, Cu, Rb, and Sb were the top elements with cPIPs of 0.58, 1.00, and 0.42, respectively (Fig. 4). Lastly, interaction models indicated no significant interactions between the trace elements (Fig. S3).

Fig. 4.

Associations of serum trace element mixtures with AMI prevalence and severity. These models were adjusted for age, gender, smoking status, eGFR, and stent implantation. Group 1 includes Fe, Nb, Mo, and Gd; Group 2 includes Cu and Sb; Group 3 includes Rb. gPIPs, group posterior inclusion probabilities; cPIPs, conditional posterior inclusion probabilities

Based on these results, we further calculated the ERS. In the fully adjusted model, a higher ERS was associated with a higher prevalence of AMI (odds ratio (OR) per unit of ERS, 2.718; 95% confidence interval (CI), 1.914, 3.861; P < 0.001) and greater AMI severity (OR per unit of ERS, 1.940; 95% CI, 1.535, 2.452; P < 0.001).

Mediation Analysis of the Associations of Serum Trace Elements with AMI Prevalence and Severity

Mediation analysis was performed to evaluate potential mechanisms that link individual and mixed trace elements to AMI prevalence and severity. Results indicated that hsCRP significantly mediates the effects of Fe and Rb on AMI prevalence, with mediation proportions of 29.06% and 15.61%, respectively. Additionally, hsCRP partially mediates the influence of trace element mixtures on AMI prevalence and severity, with mediation proportions of 8.56% and 48.50%, respectively (Table 2).

Table 2.

Mediation analysis of the associations of single trace element and trace element mixtures with AMI prevalence and severity

Pathways	Indirect effect β (95% CI)	Direct effect β (95% CI)	Total effect β (95% CI)	Mediating proportion	P-value
Fe → hsCRP → AMI prevalence	− 0.0003 (− 0.0318, 0)	− 0.0006 (− 0.0143, 0.0014)	− 0.0009 (− 0.0367, 0)	0.2906	< 0.001*
Cu → hsCRP → AMI prevalence	0 (0, 0.0215)	0.0001 (0, 0.0497)	0.0001 (0, 0.0667)	0.2985	0.090
Rb → hsCRP → AMI prevalence	0 (0, 0.0094)	0.0002 (0.0001, 0.0725)	0.0002 (0.0002, 0.078)	0.1561	0.030*
Nb → hsCRP → AMI prevalence	0.0009 (− 0.0246, 0.0297)	− 0.1891 (− 0.2632, − 0.1083)	− 0.1884 (− 0.2673, − 0.1017)	− 0.0045	0.904
Mo → hsCRP → AMI prevalence	0.0111 (− 0.005, 0.0287)	− 0.0764 (− 0.128, − 0.0359)	− 0.0654 (− 0.1189, − 0.018)	− 0.1691	0.166
Sb → hsCRP → AMI prevalence	− 0.0153 (− 0.0491, 0.0006)	0.1327 (0.066, 0.1945)	0.1079 (0.0548, 0.1446)	− 0.1419	0.058
Gd → hsCRP → AMI prevalence	− 0.0013 (− 0.0119, 0.0033)	− 0.0193 (− 0.0603, − 0.0009)	− 0.0197 (− 0.0645, − 0.001)	0.0675	0.330
ERS → hsCRP → AMI prevalence	0 (0, 0.0011)	0 (0, 0.0125)	0 (0, 0.0137)	0.0856	0.032*
Fe → hsCRP → AMI severity	− 0.1233 (− 0.2469, 0.0002)	− 0.3312 (− 0.7882, 0.1258)	− 0.4545 (− 0.9107, 0.0016)	0.2714	0.050
Cu → hsCRP → AMI severity	0.3077 (− 0.1043, 0.7197)	1.2961 (0.2724, 2.3198)	1.6038 (0.6654, 2.5422)	0.1918	0.143
Rb → hsCRP → AMI severity	0.5251 (− 0.0304, 1.0806)	0.3262 (− 0.3876, 1.04)	0.8513 (0.2515, 1.451)	0.6168	0.064
Nb → hsCRP → AMI severity	0.0078 (− 0.2529, 0.2685)	− 0.4724 (− 0.7816, − 0.1631)	− 0.4645 (− 0.7443, − 0.1848)	− 0.0168	0.953
Mo → hsCRP → AMI severity	0.0833 (− 0.0327, 0.1994)	− 0.2207 (− 0.3694, − 0.0721)	− 0.1374 (− 0.2762, 0.0014)	− 0.6066	0.159
Sb → hsCRP → AMI severity	− 0.285 (− 0.7048, 0.1348)	1.1228 (0.5578, 1.6879)	0.8378 (0.391, 1.2847)	− 0.3402	0.183
Gd → hsCRP → AMI severity	− 0.1522 (− 0.5306, 0.2262)	− 0.4074 (− 0.9954, 0.1806)	− 0.5596 (− 1.0834, − 0.0359)	0.2720	0.431
ERS → hsCRP → AMI severity	0.1819 (0.0766, 0.2872)	0.1931 (0.032, 0.3543)	0.375 (0.2397, 0.5104)	0.4850	0.001*

These models were adjusted for age gender smoking status eGFR and stent implantation

AMI acute myocardial infarction, hsCRP high-sensitivity C-reactive protein, CI confidence interval

^*P-value < 0.05

Sensitivity Analysis

In the sensitivity analysis, we re-fitted the multiple-element logistic regression models for trace elements with AMI prevalence and severity by excluding patients with prior stent implantation, adding medical status and medication history as covariates, and converting continuous covariates into categorical variables. Results indicated that the associations of Cu and Rb with AMI prevalence and severity remained stable. The associations of Fe and Mo with AMI prevalence were significant or marginally significant in all models. However, the associations of Sb and Mo with AMI severity became non-significant in some models (Table S3–S6).

Discussion

In this study, we comprehensively measured trace element exposure in serum to reveal the extent of element exposure in patients with AMI. We found that higher concentrations of Cu and Rb were associated with increased AMI prevalence and severity across various models, exhibiting a linear dose–response relationship. Additionally, our findings suggest a potential negative correlation between Fe and AMI prevalence and a potential positive correlation between Sb and AMI severity. This study confirms the findings of earlier research linking certain elements to AMI prevalence and severity and identifies new elements associated with these outcomes in a broad trace element exposure environment.

We observed that high concentrations of Cu in trace element mixtures may increase AMI prevalence and severity, consistent with other relevant studies [30–33]. Notably, a meta-analysis revealed that while Cu levels in serum samples of Asian patients with AMI were markedly higher than those in control participants, this conclusion may be influenced by ethnicity and sample type [34]. The relationship between Rb and AMI prevalence and severity has not been documented. However, studies have reported that Rb is associated with elevated blood pressure [35], reduced renal function [36, 37], and biological aging [38, 39]. Similar to the findings of previous research, we also observed a potential negative association between Fe and AMI prevalence [40–43]. Increasing dietary Fe intake has been shown to substantially reduce the risk of AMI [44]. Although a positive correlation between urinary Sb and AMI has been documented [8, 45, 46], no previous report has identified a link between serum Sb with AMI. Our study found a positive correlation between serum Sb and AMI, with a stronger association between serum Sb levels and AMI severity than with AMI prevalence.

The specific mechanisms through which trace elements influence AMI prevalence and severity remain unclear. Previous research indicates that low serum Fe levels can trigger low-grade inflammation [47], and a synergistic effect exists between low Fe and high hsCRP levels influencing AMI [48]. Additionally, studies have shown a positive association between Rb and myeloperoxidase [49], as well as obesity [50], both of which are markedly correlated with elevated levels of hsCRP [51, 52]. In our study, the mediation analysis suggested that Fe and Rb may influence AMI prevalence through inflammation, as indicated by hsCRP levels. We also observed that hsCRP partially mediated the effect of trace element mixtures on AMI prevalence and severity, with a higher mediation proportion for AMI severity than AMI prevalence. These results imply hsCRP (inflammation) may be a potential mechanism linking trace elements to AMI.

This study has several strengths. First, it provides the most comprehensive profile of serum element exposure in patients with AMI to date, identifying new trace elements associated with AMI prevalence and severity. Second, the study explores the associations of both individual and mixed trace elements with AMI prevalence and severity, offering epidemiological evidence of these relationships under co-exposure. Third, the mediation analysis revealed that inflammation links trace elements to AMI prevalence and severity, suggesting a new direction for clinical intervention in patients with AMI. Finally, the study enhances the stability and robustness of its conclusions through various feature selection and sensitivity analyses. However, our research also has certain limitations. First, consistent with previous studies on trace elements [27, 35, 53, 54], the sample size was relatively small. Second, we did not account for potential confounding factors such as diet and physical activity. Third, this study was conducted as a single-center study in Nanjing, China, and did not consider regional differences in elemental concentrations and their associations with AMI under varying environmental conditions (e.g., soil, air, and water). Future studies should address these regional variations by examining populations from areas with diverse environmental exposures. Finally, as a cross-sectional study, our research design limits causal inference. Future work should include prospective cohort studies or randomized controlled trials to validate these findings.

Conclusion

In summary, our findings demonstrate an association between elevated serum levels of multiple trace elements and increased AMI prevalence and severity. Specifically, Cu and Rb were identified as major contributors to both AMI prevalence and severity, whereas Fe emerged as a potential protective factor against AMI prevalence, and Sb significantly promoted AMI severity. Additionally, hsCRP (inflammation) may be a potential mechanism linking trace elements to AMI. Our results provide crucial epidemiological evidence on the impact of trace elements on AMI under co-exposure conditions. We hope that further research will confirm our conclusions.

Supplementary Information

Below is the link to the electronic supplementary material.

Author Contribution

Y.X., X.L., J.W., and Z.X.L. participated in the study design. Y.L., X.Y.T., P.Z., H.F., and Y.Y.W. (Yangyang Weng) participated in data collection. Z.H.S., Y.Y.W. (Yongyue Wei), and C.Q. performed the statistical analysis. Z.H.S. and Z.X.L. drafted the article. All the authors read and approved the final manuscript.

Funding

This study was funded by the National Natural Science Foundation of China (grant numbers 81970374, 81300999, and 82372165), the Jiangsu Province Basic Research Program Natural Science Foundation (grant number BK20242002), the Jiangsu Province Key Research Project on Aging Health (grant number LKZ2024005), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (grant number 21KJA320003), the Scientific Research Project of Jiangsu Commission of Health (grant number LKZ2023001), and the Clinical Competence Improvement Project of Jiangsu Province Hospital (grant number JSPH-MB-2022–13).

Data Availability

No datasets were generated or analysed during the current study.

Declarations

Ethics Approval and Consent to Participate

The study adhered to the Declaration of Helsinki standards and was approved by the Medical Ethics Committee of the Second Affiliated Hospital of Nanjing Medical University (2011-KY-002). All patients provided written informed consent.

Competing Interests

The authors declare no competing interests.