| BackgroundCoronary artery disease (CAD) is the leading cause of death and disease burden in China and other most countries. With the developing of economy, the change of life style and the population aging in our country, the morbidity and mortality of CAD has been increasing significantly. Therefore, identifying the risk factors and population at high risk early is very important for preventing and controlling CAD. Although many risk factors for CAD such as hypertension, hyperlipemia and smoking have been identified, however, data showed that these traditional risk factors only account for50%of the CAD etiology, which indicates that other factors also play an important role in the incidence of CAD.Iron is an essential microelement for life and health which plays an important role in the transportation of oxygen, respiration of cell, metabolism of energy, expression of gene, and repairation and replication of DNA in the cellular process. Approximately66%is found in hemoglobin in the ferrous (Fe2+) form. An additional27%of the body’s iron is incorporated as tissue ferritin inthe ferric (Fe3+) state. When the intracellular storage capacity is exceeded, additional intracellular iron is incorporated into hemosiderin and iron may besequestered in the reticuloendothelial system. Free Fe2+can produce hydroxyl radical (·OH-) through Fenton and Haber-Weiss reaction that promotes lipid peroxidation and causes severe damage to membranes, proteins, and DNA, and induces oxidative stress. Besides, iron may act as a nitric oxide (NO) scavenger and thus induce endothelial cell dysfunction.As we all know, the incidence of CAD in premenopausal women is significantly lower than men, and the CAD risk in postmenopausal women increased gradually to be similar with men. Based on the above-mentioned phenomenon and the relevant researches, Sullivan proposed the "iron hypothesis" in1981suggesting that body iron stores were positively related to CAD risk. The theory was that the low level of iron by the menstruation periodic iron loss could be helpfulfor the prevention of CAD in women, while progressive accumulation of the body’s iron in men lead to increased risk of CAD. Since the hypothesis was first proposed, extensive debates have been generated from epidemiological studies and clinical trials. The possible main reasons for the conflicting results are as follows:①Different biomarkers of body iron stores:So far, a good deal of serum iron indicators including serum ferritin (SF), serum iron (SI), total iron-binding capacity (TIBC), and serum transferrin receptor (sTfR) were used in the present publications to assess the relation of body iron stores. Among them, almost no one could independently and exactly reflect the body iron levelsfor various weakness. For example, SF, an acute-phase protein, was prone to be increased by infection and inflammation. Based on the above-mentioned reasons, Ramakrishna suggested that a well designed research with complete measurements of SF, SI, TIBC and sTfR should conducted to properly reflect the status of iron overload.②Inconsistent outcomes:Myocardial infarction, coronary artery disease and total death of cardiovascular diseases are all used in studies about association of body iron and cardiovascular disease as outcomes. Although atherosclerosis is the pathological mechanism of the above diseases, the etiology of them might be not the same as they are all multiple factor diseases.③Partial adjustment of covariates:As CAD is multiple factor and complex disease, smoking, hypertension and hyperlipemia are all important risk factors, thus, it is hard to indicate the effects of iron overload on CAD risk without complete adjustments.Above all, we conducted a hospital-based case-control study included newly diagnosed CAD patients by percutaneous coronary angiography and healthy controls and simultaneously measured SF, SI, TIBC and sTfR to evaluate the association of body iron and CAD and explore the ideal iron indicator or combination of iron indicators that has best effect on CAD risk. Besides, CAD is a chronic disease of multifactorial origin that develops from the interplay of lifestyle, this study explored the high-order interaction between iron overload indicators and enviroment factors on CAD risk. A meta-analysis was also performed to confirm the association of body iron overload and CAD risk. This study was sponsored by a grant from National Natural Science Foundation of China (81072357).Objectives1.To assess the association between body iron indicators and CAD risk;2. To explore the ideal iron indicator that has best effect on CAD risk;3.To study the interaction effects between iron indicators and enviroment factors on CAD risk.Materials and Methods1. A hospital-based case-control study was conducted with newly-diagnosed CAD patients. Healthy controls were recruited from the healthy persons who came to the Physical Examination Center for a medical checkup.The concentration of SF、SI、TIBC and sTfR were measured by enzyme-linked immunosorbent assay (ELISA).2. Bayesian model averaging (BMA) was applied to select the significant covariates that influence on CAD risk. Restricted cubic spline was performed to assess the concentration-risk association between each serum iron parameter and CAD risk.3. The areas under each Receiver Operating Characteristic (ROC) curve (AUC) were compared with each other toindicate the one showing strongest association with CAD risk.4. Generalized Multifactor Dimensionality Reduction (GMDR) was used to explore the high-order interaction between iron overload indicators and enviroment factors on CAD risk.5. A meta-analysis was performed with comprehensive search for relevant articles. Fixed or random effect pooled measure was selected on the basis of the results of homogeneity test, I2was used to evaluate the heterogeneity among studies. Meta regression and subgroup analysis were used to explore potential sources of between-study heterogeneity. An analysis of influence was carried out, which describes how robust the pooled estimator is to removal of individual studies. Publication bias was estimated using funnel plot and Egger’s test.Results1. Study characteristic:A hospital-based case-control study was conducted with258newly-diagnosed CAD patients and282healthy controls. The traditional CAD risk factors such as age, hypertension, BMI, prevalence of smoking, serum8-iso-prostaglandin F2a and diabetes in the CAD group are significantly higher than those in controls (P<0.05). Serum high density lipoprotein in the control group are significantly higher than that in CAD group (P<0.05).2. Univariate analysis:All iron parameters showed significant differences among the cases and controls except for SF(P=0.06). SI in the CAD group is significantly higher than that in controls (P<0.01), TIBC and sTfR in the CAD group are significantly lower than those in controls (P<0.01). 3. Covariates selection:There were25models in Occam’s window, and these were used to calculate the BMA estimates of the regression coefficients. The posterior probabilities and BIC value of the best model are0.15and-2950.08, respectively. Nine variables were selected by BMA on the selected models and they were age, history of diabetes, total cholesterol, low density lipoprotein, hypertension, alcohol, sex, serum8-iso-prostaglandin F2a and serum high density lipoprotein.4. Multivariate analysis:(1) SF:After adjusted for age, history of diabetes, total cholesterol, low density lipoprotein, hypertension, alcohol, sex, serum8-iso-prostaglandin F2a and serum high density lipoprotein, the overall (P<0.01) and non-linear (P<0.01) associations between SF and CAD were both signifcant in the multivariable analysis. The reference value for SF was200ug/L in the concentration-risk analysis, and the OR(95%CI) were1.69(1.18-2.41),1.15(1.00-1.31),1.07(0.93-1.21),1.40(0.99-1.97) and1.83(1.09-3.07) for100ug/L,150ug/L,250ug/L,300ug/L and350ug/L, respectively.(2) SI:After adjusted for age, history of diabetes, total cholesterol, low density lipoprotein, hypertension, alcohol, sex, serum8-iso-prostaglandin F2a and serum high density lipoprotein, the overall (P<0.01) and non-linear (P<0.01) associations between SI and CAD were both signifcant in the multivariable analysis. The reference value for SI was19dμmol/L in the concentration-risk analysis, and the OR(95%CI) were0.36(0.21-0.60),0.66(0.54-0.81),1.41(1.20-1.66),2.00(1.45-2.75) and2.52(1.67-3.82) for12μmol/L,16μmol/L,22μmol/L,26μmol/Land30μmol/L, respectively.(3) TIBC:After adjusted for age, history of diabetes, total cholesterol, low density lipoprotein, hypertension, alcohol, sex, serum8-iso-prostaglandin F2a and serum high density lipoprotein, the overall (P<0.01) and non-linear (P<0.01) associations between TIBC and CAD were both signifcant in the multivariable analysis. The reference value for TIBC was90μmol/L in the concentration-risk analysis, and the OR(95%CI) were4.58(2.61-8.03),2.03(1.58-2.62),0.59(0.49-0.68),0.41(0.30-0.53) and0.33(0.22-0.49) for50μmol/L,70μmol/L,110μmol/L,130μmol/L and150μmol/L, respectively.(4) sTfR:After adjusted for age, history of diabetes, total cholesterol, low density lipoprotein, hypertension, alcohol, sex, serum8-iso-prostaglandin F2a and serum high density lipoprotein, the overall (P<0.01) and linear (P>0.05) associations between sTfR and CAD were both signifcant in the multivariable analysis. The reference value for sTfR was10mg/L in the concentration-risk analysis, and the OR(95%CI) were:1.33(0.97-1.84),1.16(1.00-1.35),0.87(0.79-0.96),0.67(0.53-0.86) and0.53(0.35-0.79) for6mg/L,8mg/L,12mg/L,16mg/L and20mg/L, respectively.5. ROC analysis:The AUC(95%CI) were0.73(0.69-0.77),0.74(0.69-0.78),0.53(0.48-0.58), and0.61(0.56-0.66) for SI, TIBC, SF, and sTfR, respectively. After comparing the AUC with each other, the combination of SI and TIBC (AUC (95%CI):0.86(0.83-0.90)) was superior to other examined iron parameters or the combination of iron indicators (P<0.05).6. High-order interaction:The best prediction model identified in our analysis included SI-TIBC, BMI and HDL that scored10for cross-validation consistency,10for permutation test (P<0.01) and0.85for testing accuracy, suggesting that these three factors together significantly contributed to CAD risk. The accuracy of the best model with covariates (age and sex) adjustment was better than that without adjustment.7. Meta-analysis:A total of26studies on SF, TIBC, SI and sTfR were included in the meta-analysis.(1) For the pooled SMD analysis, a total of4,410cases and7,357controls were eligible for the meta-analysis performed on SMD. The SMD(95%CI) for SF, TIBC, SI and sTfR were0.74(0.46,1.01),-0.06(-0.28,0.16),-0.24(-0.59,0.10) and0.13(0.02,0.24), respectively. Substantial between-study heterogeneity were found in SF, TIBC and SI, and after excluding the studies exerting substantial impact on between-study heterogeneity, the SMD(95%CI) for SF, TIBC and SI were0.12(0.07,0.17),-0.22(-0.39,-0.07) and-0.17(-0.31,-0.04), respectively. No significant influence and publication bias were observed before and after sensitivity analysis.(2) For the pooled OR analysis, a total of5,738cases and98,362controls were included for the meta-analysis performed on OR. The OR(95%CI) for SF, TIBC, SI and sTfR were1.13(0.98,1.30),1.21(0.95,1.55),0.97(0.94,0.99) and1.54(0.85,2.77), respectively. Substantial between-study heterogeneity was found in SI, and after excluding the studies exerting substantial impact on between-study heterogeneity, the pooled OR(95%CI) was1.19(1.08,1.45) for SI. No significant influence and publication bias were observed before and after sensitivity analysis. Conclusions1. In this study, compared with SF concentrations less than200ug/L, SF was found significantly positively correlated with CAD risk in nonlinear in the concentration-risk analysis. Compared with SI concentrations less than19μmol/L, SI was found significantly positively correlated with CAD risk in nonlinear. Compared with TIBC concentrations more than90μmol/L, TIBC was found significantly negatively correlated with CAD risk in non-linear. Compared with sTfR concentrations more than10mg/L, sTfR was found significantly negatively correlated with CAD risk in linear. Thus, after adjusted for the important covariates screened by BMA, iron overload were positively correlated with CAD.2. The combination of SI and TIBCwere found to be the ideal iron indicator that have best effect on CAD risk.3. Our results showed that combination of SI-TIBC, BMI and HDL conferred a significant three factors interaction on CAD risk.4. In this meta-analysis, SF, SI and TIBC were found significantly associated with CAD risk. Meta analysis of sTfR can not be fully conducted because of insufficient studies.Innovations1. This study assessed the concentration-risk association between body iron parameters and CAD risk with restricted cubic spline, and indicated the non-linear association of SF, SI and TIBC and CAD risk which improved the predictive power of the model.2. Information is presently absent about better iron status biomarkers on CAD disease risk, this study explored the ideal iron indicator(SI-TIBC) that has best effect on CAD risk with ROC analysis and provided evidence for future studies related to iron overload metabolism research worldwide.3. This research explored the statistically significant high-order interaction among SI-TIBC, BMI and HDL associated with CAD risk, further studies with big sample are needed to confirm these novel results. |
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