Plasma Ionomic Profile and Interaction Patterns in Coronary Artery Disease Patients

doi:10.21203/rs.3.rs-3994072/v1

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Plasma Ionomic Profile and Interaction Patterns in Coronary Artery Disease Patients

https://doi.org/10.21203/rs.3.rs-3994072/v1

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Humans are exposed to various chemical elements that have been associated with the development and progression of diseases such as coronary artery disease (CAD). However, existing research has primarily focused on the relationships between individual elements and CAD without considering the overall ionomic profile. Therefore, our aim is to employ a multi-element approach to investigate CAD patients and those with comorbid conditions such as diabetes (CAD-DIA), high blood pressure (CAD-HBP), or high blood lipids (CAD-HBL). Plasma concentrations of 21 elements, including lithium, boron, aluminum, calcium, titanium, vanadium, chromium, manganese, ferrum, cobalt, nickel, copper, zinc, arsenic, selenium, strontium, cadmium, stannum, stibium, barium, and plumbum, were measured in CAD patients (n = 201) and healthy subjects (n = 110) using inductively coupled plasma-mass spectrometry (ICP-MS). Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) models were utilized to analyze the ionomic profiles. Spearman correlation analysis was employed to identify the interaction patterns among individual elements. We found that levels of Ba, Li, and Pb were elevated in the CAD group compared to the healthy group, while Sb, Ti, Fe, and Se were lower. Furthermore, the CAD-DIA group exhibited higher levels of Ni and Cd, while the CAD-HBP group showed lower levels of Co and Mn. In the CAD-HBL group, Ti was increased, whereas Ba, Cr, Cu, Co, Mn, Ni, and Ti were reduced. In conclusion, ionomic profiles can be utilized to differentiate CAD patients from healthy individuals, potentially providing insights for future treatment or dietary interventions.

Coronary artery disease Ionomic profile Element interaction ICP-MS OPLS-DA model

Coronary artery disease (CAD) is a prevalent form of heart disease and remains a primary cause of morbidity and mortality in both China and worldwide [1, 2]. CAD not only imposes physical and psychological strain on patients but also places a substantial financial burden on public healthcare systems. Risk factors such as high blood pressure, diabetes, dyslipidemia, and unhealthy lifestyle habits like smoking and alcohol consumption are recognized as contributors to the increased susceptibility to CAD. However, these factors do not fully account for the development of CAD [3]. Emerging research indicates a potential link between CAD and environmental factors [4]. The continuous release of various chemical compounds, particularly toxic elements, into the environment may be absorbed by humans through inhalation, ingestion, or skin contact.

The equilibrium of various ions in the biological system is crucial, and any disruption may lead to the onset of diseases, including CAD [5]. For instance, a prospective case-control study revealed that arsenic may significantly increase the risk of CAD (HR: 1.71, CI: 1.19 to 2.44, P < 0.001) [6]. Furthermore, exposure to plumbum [7], ferrum [8], selenium [9], and cadmium [10] has been reported to be significantly associated with CAD. These findings suggest that elements may play a crucial role in CAD and could serve as sensitive and practical indicators of disease susceptibility. However, existing studies have primarily focused on individual ions, lacking a comprehensive analysis of the elemental spectrum and the interrelationships among elements in CAD. It is increasingly recognized that interactions among body elements are widespread, and changes in one element may impact the absorption and metabolism of others [11]. Therefore, it is essential and meaningful to investigate the characteristics of the ionomic profile and the interaction patterns among ions in CAD patients.

In the past, ionomic profile research has primarily been conducted within the realms of plant science [12] and microbiology [13]. However, there has been a recent expansion of this research into human diseases, including Alzheimer's disease [14], type 2 diabetes [15], skin disease [16], and cancer [17]. The current diagnostic methods for coronary artery disease (CAD), such as coronary angiograms, have notable limitations, including being time-consuming, expensive, and painful for patients. Therefore, there is a need to investigate whether elemental profiling can be utilized as a cost-effective and rapid diagnostic tool for CAD. Various biological sample media, such as blood, plasma, urine, and bone, have been employed to assess the uptake and health impacts of chemicals, particularly toxic elements [18]. Plasma, in particular, serves as a rapid and reliable medium for providing information on the metabolism of elements in the human body. Consequently, plasma was chosen for this study to monitor ion exposure using inductively coupled plasma mass spectrometry (ICP-MS) due to its availability and accessibility. The advancement of reliable ICP-MS techniques and the simultaneous analysis of all significant elemental components have greatly facilitated the exploration of complex ionomic profiles within organisms and laid the groundwork for the clinical application of ionomic studies [19].

Considering the aforementioned factors, the objective of this study was to investigate the plasma ionomic profile and interaction patterns in CAD patients.

Study population and design

We enrolled a total of 201 patients diagnosed with coronary artery disease (CAD) and 110 healthy individuals as control subjects at Xiangya Hospital in Changsha, China, between May 2018 and October 2019. All participants underwent physical examinations and biochemical assessments, including blood routine, blood glucose, blood lipids, hepatic, and renal function tests. The demographic and clinical characteristics of the study population are presented in Table 1. The diagnosis of CAD in patients was confirmed by the cardiology department of Xiangya Hospital [20]. The exclusion criteria for patients were as follows: (a) aged ≤ 18 or ≥ 80 years old; (b) hemolysis of samples; (c) diagnosis of malignant tumor; (d) with clinically significant infectious diseases. Hypertension was defined as systolic blood pressure ≥ 140 mm Hg or diastolic blood pressure ≥ 90 mm Hg [21]. Diabetes was determined by a fasting glucose level of ≥ 7.0 mmol/L or a 2-hour oral glucose tolerance test (OGTT) glucose level of ≥ 11.1 mmol/L. Hyperlipidemia was diagnosed when one of the following criteria was met: total cholesterol ≥ 6.2 mmol/L, triglycerides (TC) ≥ 2.3 mmol/L, low density lipoprotein cholesterol (LDL-C) ≥ 4.1 mmol/L.

Table 1

Basic characteristics of study participants at baseline.
Characteristics	Healthy (N = 110)	CAD (N = 201)	P-value
Age (years)	56.8 ± 10.27	61.93 ± 9.29*	< 0.001
Sex Male Female	44 66	131* 70*	< 0.001
Diabetes (N; %)	0; 0	66; 32.84*	< 0.001
Hypertension (N; %)	0; 0	137; 68.16*	< 0.001
Hyperlipidemia (N; %)	0; 0	64; 31.84*	< 0.001
Erythrocyte (10^12/L)	4.57 ± 0.42	4.49 ± 0.57	0.180
Hemoglobin (g/L)	137.01 ± 13.3	136.36 ± 16.4	0.724
Thrombocyte (10^9/L)	215.33 ± 47.34	205.91 ± 61.76	0.165
GPT (u/L)	18.23 ± 7.86	33.16 ± 28.8*	< 0.001
GOT (u/L)	21.13 ± 4.96	43.15 ± 69.84*	0.001
TB (µmol/L)	12.14 ± 4.14	12.15 ± 5.91	0.979
FBG (mmol/L)	4.53 ± 0.37	6.78 ± 3.19*	< 0.001
TG (mmol/L)	1.33 ± 0.61	2.05 ± 2.19*	0.001
TC (mmol/L)	4.6 ± 0.65	4.13 ± 1.21*	< 0.001
HDL (mmol/L)	1.34 ± 0.29	0.97 ± 0.26*	0.001
LDL (mmol/L)	2.71 ± 0.54	2.61 ± 0.92	0.335
Urea (mmol/L)	5.29 ± 1.29	6.06 ± 2.23*	0.001
SCr (µmol/L)	68.55 ± 14.49	86.71 ± 25.12*	< 0.001
Uric (µmol/L)	308.75 ± 76.47	357.19 ± 94.66*	< 0.001
*Signifcantly diferent values between CAD patients and the healthy group, Student’s t-test or Mann–Whitney test (p<0.05). Note: GPT: glutamic-pyruvic transaminase; GOT: glutamic-oxalacetic transaminase; TB: total bilirubin; FBG: fast blood glucose; TG: triglyceride; TC: total cholesterin; HDL: high density lipoprotein; LDL: low density lipoprotein; SCr: serum creatinine.

The study protocol was approved by the Ethics Committee of Xiangya Hospital, Central South University (No. 2018111098), and written informed consents were obtained from all patients. All procedures involving human participants were in accordance with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Sample preparation and element analysis

Blood collected from the peripheral veins using an ethylenediaminetetraacetic acid (EDTA) anticoagulant tube was centrifuged at 3000 rpm for 10 min at 4°C. Then the upper plasma samples were separated and stored at − 80°C. Hemolysis samples were discarded due to an interfusion of hemocyte content. Prior to elemental analysis, the plasma samples were thawed gradually at 4°C and homogenized for 10 seconds using a vortex mixer, followed by a 1:10 dilution with 1% nitric acid (HNO3). The final solutions were then filtered through 0.22 µm cellulose acetate membranes before analysis.

The concentration of 21 elements, including lithium (Li), boron (B), aluminum (Al), calcium (Ca), titanium (Ti), vanadium (V), chromium (Cr), manganese (Mn), ferrum (Fe), cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), selenium (Se), strontium (Sr), cadmium (Cd), stannum (Sn), stibium (Sb), barium (Ba), and plumbum (Pb) were determined using ICP-MS (Agilent 7700x, Tokyo, Japan). The operating parameters of ICP-MS are summarized in Table S1. The accuracy and precision of the analytical method were assessed by adding known quantities of standards to the samples. The recoveries ranged from 85–115%. Case and control specimens were analyzed in a random sequence, and laboratory personnel were unaware of the case-control status.

Statistical analysis

Baseline characteristics of cases and controls were presented as mean ± standard deviation (SD) and compared using t-tests for continuous variables or chi-square tests for categorical variables. The concentrations of individual elements were shown as 50th (25th–75th) and compared through Mann-Whitney U tests. Then, logistic regression was performed to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) between the elements and CAD. The PCA and OPLS-DA models were utilized to provide an overview of the ionic profile. Permutation tests with 999 iterations were performed to validate the quality of the OPLS-DA model. Variable importance in projection (VIP) and S-plots were used to identify the potential biomarkers. It was reported that the accumulated values of Q2 > 0.3 possessed statistical significance, > 0.5 were considered good, and > 0.9 indicated that the model was perfect [22]. Variables with a VIP score of 1 or higher were deemed more pertinent for sample classification. Additionally, Spearman’s rank correlation analysis was implemented to explore correlations between each ion pair. P < 0.05 was considered to be statistically significant. SPSS v.22 software was used for general statistical analyses. SMICA-P v.14.1 software was employed to perform multivariate statistical analysis through the principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) models. The heat map and correlation coefficient matrix were plotted by R v.3.4.5 software using the “heatmap” package and the “ggplot” package. The interaction networks of ions were generated by Cytoscape v.3.7.1 for Windows.

Study population and element concentrations

The main clinical characteristics of this study population are presented in Table 1. In comparison with the healthy group (N = 110), the CAD group (N = 201) were more likely to have diabetes (32.84%), hypertension (68.16%), and hyperlipidemia (31.84%) at baseline. Additionally, both groups displayed slightly but significantly abnormal hepatic and renal function. No significant differences were observed in blood routine, LDL, or total bilirubin (TB). In this study, we measured the levels of 21 elements in the plasma by ICP-MS. Comparisons of individual elements between the CAD group and the healthy group are listed in Table 2. Concentrations of Ba, Cd, Cr, Li, Ni, Pb, and Zn were significantly higher in the CAD group compared to the healthy group, whereas the levels of Sb, B, Ca, Cu, Fe, Se, Ti, and V were significantly lower. The remaining elements, such as Al, As, Co, Mn, Sr, and Sn, displayed no differences between the two groups. About 70% of element status has undergone alterations, suggesting that multiple elements play a greatly important part in CAD.

Table 2

Concentrations of plasma elements among study participants.
Element (µg/L)	Healthy (N = 110)	CAD (N = 201)	P-value
Aluminum (Al)	79.77 (55.19–98.63)	70.62 (54.05–102.1)	0.373
Stibium (Sb)	5.71 (5.13–6.64)	4.91 (3.48–5.82) *	< 0.001
Arsenic (As)	1.08 (0.84–1.53)	1.02 (0.81–1.34)	0.27
Barium (Ba)	8.94 (7.3-20.12)	19.39 (7.96–22.77) *	< 0.001
Boron (B)	81.09 (57.44–142.70)	69.67 (53.33-104.95)	0.028
Cadmium (Cd)	0.1 (0.06–0.17)	0.17 (0.1–0.42) *	< 0.001
Calcium (Ca)	19128.07 (18509.9-19961.5)	18393.7 (17616.7-19494.6) *	< 0.001
Chromium (Cr)	3.3596 (2.9690–3.8422)	3.61 (3-4.22)	0.012
Cobalt (Co)	0.28 (0.20–0.35)	0.27 (0.23–0.34)	0.896
Copper (Cu)	994.48 (903.44-1088.18)	929.68 (822.33-1075.74)	0.022
Ferrum (Fe)	2103.5 (1696.26-2578.27)	1270.44 (1062.4-1546.3) *	< 0.001
Lithium (Li)	4.68 (3.46–4.68)	14.6 (9.15–21.23) *	< 0.001
Manganese (Mn)	3.34 (2.72–4.14)	3.57 (2.77–4.35)	0.195
Nickel (Ni)	4.95 (1.51–11.21)	10.02 (6.79–12.92) *	< 0.001
Plumbum (Pb)	5.32 (3.41–8.74)	12.81 (7.17–19.83) *	< 0.001
Selenium (Se)	161.24 (147.48-177.96)	137.41 (121.48-151.98) *	< 0.001
Strontium (Sr)	31.55 (26.79–37.02)	29.6 (23.99–36.09)	0.038
Stannum (Sn)	1.85 (1.6–2.75)	2.11 (1.5–3.3)	0.214
Titanium (Ti)	37.2 (33.32–42.36)	29.4 (26.17–32.63) *	< 0.001
Vanadium (V)	1.28 (1.18–1.4)	1.12 (1.02–1.22) *	< 0.001
Zinc (Zn)	892.73 (850.63-1020.33)	1058.2 (882.83-1289.94) *	< 0.001
Note: The concentrations of individual elements were shown as 50th (25th-75th) and compared though Mann-Whitney U tests.

In order to investigate the associations between elements and CAD, logistic regression was utilized to calculate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for each element. In model 1, sex and age were adjusted. The model 2 was additionally adjusted for diabetes, hypertension, and hyperlipidemia. As shown in Table 3, the levels of Sb, Ti, Fe, and Se were inversely associated with CAD prevalence (ORs of the model 2: 0.531, 0.679, 0.997, and 0.951, respectively). In contrast, the levels of Ba, Li, and Pb were positively related to CAD susceptibility (ORs of model 2: 1.092, 1.359, and 1.181, respectively). However, no statistically significant differences were observed for the remaining elements, including B, Al, Ca, V, Cr, Mn, Co, Ni, Cu, Zn, As, Sr, Cd, and Sn.

Table 3

Logistic regression of elements and CAD.
Elements	OR	95%CI
Al Unadjusted Model 1 Model 2	1.002 1.002 1.002	0.998 ~ 1.005 0.998 ~ 1.006 0.991 ~ 1.014	0.286 0.269 0.669
Sb Unadjusted Model 1 Model 2	0.640 0.634 0.531	0.543 ~ 0.754* 0.532 ~ 0.754* 0.357 ~ 0.789	< 0.001 < 0.001 0.002
As Unadjusted Model 1 Model 2	0.972 0.927 0.136	0.825 ~ 1.146 0.774 ~ 1.110 0.016 ~ 1.173	0.737 0.414 0.070
Ba Unadjusted Model 1 Model 2	1.061 1.062 1.092	1.029 ~ 1.093* 1.028 ~ 1.098* 1.022 ~ 1.168	< 0.001 < 0.001 0.010
B Unadjusted Model 1 Model 2	0.995 0.995 0.994	0.991 ~ 0.998 0.991 ~ 1.000 0.981 ~ 1.007	0.006 0.029 0.366
Cd Unadjusted Model 1 Model 2	2.889 2.158 0.813	1.271 ~ 6.57 0.991 ~ 4.699 0.172 ~ 3.845	0.011 0.053 0.794
Ca Unadjusted Model 1 Model 2	1.000 1.000 1.000	1.000 ~ 1.000 1.000 ~ 1.000 0.999 ~ 1.000	0.006 0.015 0.242
Cr Unadjusted Model 1 Model 2	0.984 0.981 0.979	0.941 ~ 1.029 0.936 ~ 1.028 0.888 ~ 1.078	0.484 0.428 0.661	Arsenic (As) Model 1 Model 2
Co Unadjusted Model 1 Model 2	0.943 1.280 0.007	0.306 ~ 2.900 0.333 ~ 4.923 0.00 ~ 11.296	0.918 0.719 0.187
Cu Unadjusted Model 1 Model 2	0.999 1.000 0.995	0.998 ~ 1.000 0.998 ~ 1.001 0.99 ~ 1.000	0.086 0.469 0.057
Fe Unadjusted Model 1 Model 2	0.998 0.998 0.997	0.998 ~ 0.999* 0.997 ~ 0.998* 0.996 ~ 0.999	< 0.001 < 0.001 0.001
Li Unadjusted Model 1 Model 2	1.472 1.440 1.359	1.335 ~ 1.622* 1.308 ~ 1.586* 1.110 ~ 1.665	< 0.001 < 0.001 0.003
Mn Unadjusted Model 1 Model 2	1.128 1.142 0.901	0.958 ~ 1.329 0.957 ~ 1.364 0.499 ~ 1.624	0.148 0.141 0.728
Ni Unadjusted Model 1 Model 2	1.110 1.074 0.995	1.059 ~ 1.163* 1.024 ~ 1.126 0.923 ~ 1.073	< 0.001 0.003 0.896	Arsenic (As) Model 1 Model 2
Pb Unadjusted Model 1 Model 2	1.188 1.176 1.181	1.128 ~ 1.251* 1.114 ~ 1.241* 1.058 ~ 1.396	< 0.001 < 0.001 0.006
Se Unadjusted Model 1 Model 2	0.967 0.971 0.951	0.957 ~ 0.977* 0.961 ~ 0.981* 0.920 ~ 0.983	< 0.001 < 0.001 0.003
Sr Unadjusted Model 1 Model 2	0.981 0.983 0.958	0.962 ~ 1.001 0.961 ~ 1.005 0.888 ~ 1.034	0.062 0.119 0.272
Sn Unadjusted Model 1 Model 2	1.155 1.194 0.946	0.979 ~ 1.364 0.991 ~ 1.438 0.564 ~ 1.589	0.087 0.063 0.835
Ti Unadjusted Model 1 Model 2	0.849 0.867 0.679	0.812 ~ 0.889* 0.828 ~ 0.908* 0.533 ~ 0.867	< 0.001 < 0.001 0.002
V Unadjusted Model 1 Model 2	0.979 0.978 1.106	0.763 ~ 1.255 0.758 ~ 1.261 0.792 ~ 1.545	0.865 0.865 0.555
Zn Unadjusted Model 1 Model 2	1.003 1.003 1.004	1.002 ~ 1.004* 1.002 ~ 1.005* 0.999 ~ 1.008	< 0.001 < 0.001 0.09
Note: The model 1 was adjusted for sex and age. The model 2 was additionally adjusted for diabetes, hypertension and hyperlipidemia.

Characteristics of the ionomic profile

Like most biochemical indicators, changes in the concentration of elements within the body are not limited to a single element when diseases occur. Rather, multiple elements are altered, leading to a disruption in the overall ionomic profile. To investigate the potential for distinguishing between individuals with coronary artery disease (CAD) and healthy subjects based on differences in ionomic profiles, PCA and OPLD-DA were performed. As shown in Fig. 1A, relatively obvious separation (R2X = 0.486, Q2 = 0.159) was found between the CAD group and the healthy group through the PCA model. A more distinct separation (R2X = 0.547, R2Y = 0.654, Q2 = 0.621) was achieved with the OPLS-DA model (Fig. 1B). Permutation tests (Fig. 1C) strongly supported the validity of the OPLS-DA model, as indicated by the lower blue Q2-values on the left compared to the original points on the right.

To identify the potential markers contributing to classification, VIP values were computed. A detailed statistical overview is presented in Table S2. Elements with VIP values greater than or equal to 1 were deemed more pertinent for sample classification. In this investigation, Li, Pb, Ba, Ni, Al, Fe, and Sb were recognized as potential markers exhibiting satisfactory deviations (see Fig. 1D). To further validate this finding, an S-plot was jointly analyzed. As depicted in Fig. 1E, all variables were distributed in either the second or fifth quadrant, indicating a well-established model. Despite Ba and Al having VIP values exceeding 1 in Fig. 1D, their positions were at the center of the coordinate axis (p (corr) < 0.4) in the S-plot. Consequently, Ti and Al were excluded. Ultimately, based on the VIP value and S-plot results, five potential markers (Li, Pb, Ni, Fe, and Sb) were chosen. These elements were visually represented on a heatmap (Fig. 1F).

Ionomic correlation analysis and construction of interaction network

Ionomics is not only a collection of elements but also an investigation of the ways in which these elements interact. To find out the correlation of ions based on the number and strength of interactions between each ion pair, Spearman’s rank correlation analysis was performed. The correlation coefficients of each ion pair in the healthy group and CAD group are shown in Figure S1. Subsequently, the ionomic networks of the CAD group and the healthy group were generated and rearranged with the Cytoscape software based on the correlation coefficients and significance. Nearly all the elements were observed to be related to other elements, indicating complex relationships among ions. Obviously, the network of the CAD group (Fig. 2B), consisting of 21 nodes and 116 edges, exhibits a more complicated interaction pattern than the healthy group (Fig. 2A), which includes 21 nodes and 87 edges. Besides, there are variations in the detailed information regarding the interaction patterns between ions. For instance, the Cr-Ba ion pair (r = 0.73) presents the strongest positive connection in the healthy group, whereas the Al-Ba ion pair (r = 0.69) exhibits the strongest positive correlation in the CAD group. Interestingly, a few ion pairs persisted in the same connection in two groups, such as Cr-Mn, B-Al, Ba-Sn, and Mn-Ba, suggesting an inherent relationship between them. Meanwhile, it is worth noting that more positive correlations between ions are found compared to the negative ones in both groups. This observation may suggest that an increase in the level of one element could be linked to the enhancement of another element.

To investigate the significant changes in elements, we conducted a sorting of the elements based on their interactions with other elements. As depicted in Fig. 2C, the top three elements are Ca, Mn, and Cr, while the last three are Cd, Sb, and B in the healthy group. However, in the CAD group (Fig. 2D), Mn, Al, and Ba are the top three elements, with Se, Ni, and Fe ranking as the bottom three. To further illuminate the change in intergroup interaction, we calculated the difference in the connection number of ions between the CAD group and the healthy group. As shown in Fig. 2E, the top three altered elements are Cd, As, and Ti, which are commonly recognized as harmful elements [23, 24]. Conversely, the last three are Ni, Fe, and Ca, which are typically considered beneficial elements [24]. These findings may suggest that harmful elements are more likely to be activated to participate in the occurrence of CAD. In contrast, the protective function of beneficial elements such as Ca and Fe may be inhibited during this period. Further research is necessary to unveil the underlying mechanism.

Ionomic profile of CAD-DIA, CAD-HBP and CAD-HBL

Since CAD patients are always combined with other complications such as diabetes (DIA), high blood pressure (HBP), and high blood lipids (HBL), which are also closely associated with certain elements, we further performed studies to explore the differences in ionomic profiles among CAD patients with these complicating diseases. As shown in Fig. 3, the levels of Ni and Cd (Fig. 3A-B) in the CAD-DIA group are significantly higher compared to the CAD group. Moreover, the concentrations of Co and Mn (Fig. 3C-D) are remarkably lower in the CAD-HBP group in comparison to the CAD group. Furthermore, Ti (Fig. 3E) is increased, while Ba, Cr, Cu, Co, Mn, and Ni (Fig. 3F-K) are decreased in the CAD-HBL group compared to the CAD group. However, no statistical difference was observed in other elements (P-values > 0.05). These findings may indicate that complications like hyperlipidemia may further disrupt ion homeostasis.

In this study, we applied ICP-MS to measure the plasma concentrations of 21 elements in the CAD patients and the healthy subjects. Unlike previous studies, we did not focus on the associations between the individual elements and CAD prevalence. Instead, a multi-element approach was implemented to explore the relationships between the ionomic profiles and CAD, which is more reliable and precise than examining individual elements. Firstly, we found that, in our study population, Sb, Ti, Fe, and Se may play a protective role in CAD, while Ba, Li, and Pb may have a detrimental influence on CAD. These results demonstrate that the status of elements in the human body may partly change when physiological conditions alter, as reported in other studies. For instance, accumulating evidence highlights the function of Se in the cardiovascular system, owing to its role in regulating the inflammatory response and antioxidant properties, as well as the importance of Se supplementation for patients undergoing cardiac surgery [25, 26]. On the contrary, Pb was widely interpreted as a harmful factor in CAD due to its role in inducing oxidative stress [27, 28]. However, certain contradictions exist. Specifically, it has been reported that both iron excess and deficiency may be positively associated with CAD [3, 29] or may not be related [5]. This conflict may be attributed to differences in sample size, ethnic groups, complications like anemia, lifestyle, and living environment, which may result in discrepant exposure to ions. Nevertheless, larger sample sizes and further research are needed to validate the relationship between iron and clinical outcome, verify its functions, and unveil mechanisms in CAD.

Subsequently, the PCA and OPLS-DA models revealed a distinct differentiation between patients with coronary artery disease (CAD) and healthy individuals based on their ionomic profiles. These results may suggest that elements with different physiological statuses may have distinguishable ionomic profiles, potentially aiding in the diagnosis of diseases such as CAD. More importantly, Li, Pb, Ni, Fe, and Sb were found to be the more important elements in the model according to VIP and S-plot analyses. This implies that simplifying the detection process by focusing on these five elements instead of the original 21 could potentially reduce time and cost in clinical diagnosis. Increasing studies took advantage of the prosperity of the ionomic profile to assist clinical work. For example, Sun et al. first utilized the ionomic profile to investigate associations between ion modules and networks and metabolic disorders [30]. Another study showed that the plasma ionomic profile may serve as a quick and convenient tool to reflect the therapeutic effect of cisplatin-based chemoradiotherapy in cervical cancer patients [31]. These results all indicate that it is possible to use the characteristics of an ionomic profile to identify patients and healthy individuals or evaluate treatment efficacy.

Furthermore, we found that the interaction network of ions in the CAD group was more intricate than in the healthy group, along with alterations in the correlations between elements. A notable example is the relationship of Sb, which is positively related to Zn and Fe in the healthy group while negatively associated with Ba, Mn, B, Sr, and Al in the CAD group (Fig. 2A-B). Simultaneously, it is worth noting that the level of Sb was lower in the CAD group than the healthy group, which may result from the fluctuation of other elements like Ba, Mn, B, Sr, or Al. These results imply that some ions may share similar uptake or transport mechanisms. For instance, Collins et al. summarized the possible mechanisms for iron-copper interactions, mainly involving ATPase, hephaestin, ceruloplasmin, and the transferrin-induced transport process [32]. Another study demonstrated that exposure to zinc sulfate increases iron uptake by respiratory epithelial cells, leading to the up-regulation of divalent metal transporter 1 (DMT1) and ferritin [33]. Given the intricate nature of the relationships and underlying mechanisms among multiple ions within organisms, it is both challenging and imperative to illuminate these complexities in future research.

Finally, some differences in ionomic profiles were also observed in CAD patients with complications like diabetes, hypertension, and hyperlipidemia, highlighting the significance of elements in regulating bodily functions. Understanding the ionomic profile could potentially aid in uncovering the association between elements and the disease, and clinicians may consider utilizing it as a supplementary biochemical indicator for diagnosing CAD.

It is undeniable that several limitations existed in this study. Firstly, the sample size is relatively small, which may impair the statistical power of these findings. Secondly, biological experiments should be conducted to explain the mechanisms of interactions among ions involved in our research in CAD. Thirdly, the study only measured plasma concentrations of elements at a single timepoint, thus lacking information on the dynamic changes in ionomic profiles. Future research should focus on monitoring element levels over extended periods and conducting multi-dimensional analysis of the elements in the body, including samples from urine, saliva, and hair. Despite tremendous efforts to explore the diagnostic and prognostic value of elements in CAD, the relationships between elements and CAD are complex and multifactorial. Therefore, a shift from single to multiple elements is necessary to gain a better understanding of the occurrence and progression of CAD, identify potential biomarkers, and potentially develop intervention therapies involving elements in the future.

Our study found that Ba, Li, and Pb were higher in the CAD group than in the healthy group, while Sb, Ti, Fe, and Se were lower. Similarly, compared to the CAD group, Ni and Cd were found to be higher in the CAD-DIA group, while Co and Mn were lower in the CAD-HBP group. As for the CAD-HBL group, Ti was increased, while Ba, Cr, Cu, Co, Mn, Ni, and Ti were reduced. More importantly, it is feasible to differentiate the CAD patients from the healthy subjects based on the characteristics of their ionomic profiles using the PCA and OPLS-DA analyses. Furthermore, the correlations between elements were significantly altered in the CAD group, generating a more complicated interaction network. Generally, this study provides a more sensitive multi-element approach to finding significant indicators for the clinical diagnosis of CAD and the potential theory of dietary intervention. However, further studies are required to understand the potential mechanisms of action of these ions in CAD and their interactions with each other.

The authors declare that there is no conflict of interest regarding the publication of this article.

Author Contribution

QQ-Z,XL-S(First Author):Conceptualization, Methodology, Software, Investigation, Formal Analysis, Writing - Original Draft;ZY,QG: Data Curation, Writing - Original Draft;YH,YQ-Y: Visualization, Investigation;MP-L:Resources, Supervision;GW,XP-C:Software, ValidationJT,BL-C(Corresponding Author): Visualization, Writing - Review & Editing

Acknowledgements

This research was supported by grants from the National Natural Scientific Foundation of China (No. 81403018, 81673516) and Natural Science Foundation of Hunan Province (No. 2015JJ3169, 2019JJ80013).

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Plasma Ionomic Profile and Interaction Patterns in Coronary Artery Disease Patients

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Abstract

Figures

Introduction

Materials and methods

Study population and design

Sample preparation and element analysis

Statistical analysis

Results

Study population and element concentrations

Characteristics of the ionomic profile

Ionomic correlation analysis and construction of interaction network

Ionomic profile of CAD-DIA, CAD-HBP and CAD-HBL

Discussion

Conclusion

Declarations

Author Contribution

Acknowledgements

References

Additional Declarations

Supplementary Files

Status:

Version 1