Introduction

Percutaneous coronary intervention (PCI), as an important strategy for cardiac revascularization in acute/chronic coronary artery disease (CAD), has been widely used in clinical practice. Compared with people without CAD, the CAD patients usually show higher long-term cardiovascular events, long-term revascularization events, and rehospitalization rates1,2. Some biomarkers such as high sensitivity cardiac troponin (hs-cTn), C-reactive protein (CRP), etc. are considered to be related to the long-term poor prognosis of CAD3,4. For patients with acute coronary syndrome (ACS) undergoing PCI, history of diabetes mellitus, uric acid level, blood urea nitrogen level, and activated partial thromboplastic time (APTT) were all considered risk factors of poor prognosis5. Compared with these biomarkers, serum albumin (sALB) has more and more been focused in recent years. In 2020, a study based on the CGPS (Copenhagen General Population Study) noted that lower sALB levels may be associated with an increased risk of long-term cardiovascular disease in healthy individuals6. In addition, some studies have suggested that low sALB levels may be associated with poor cardiovascular outcomes in patients with CAD7,8,9,10. Some previous studies have suggested that total cholesterol levels (TC) and low-density lipoprotein cholesterol levels (LDL-C), are risk factors for poor prognosis in CAD12,13,14. Moreover, some studies have shown that albumin is closely related to the metabolism and transport of cholesterol [23288948, 29685194, 35737216]. Therefore, we speculated that TC and serum albumin levels may jointly affect poor prognostic risk in patients with CAD. However, to date, this hypothesis has not been fully evaluated. To address this gap in knowledge, our study aimed to examine whether poor prognostic risk in patients with CAD is jointly affected by TC and serum albumin levels in baseline.

Results

Baseline characteristics of participants

The characteristics of the participants are shown in Table 1. Compared with the high sALB group (≥ 4 g/dL), the low sALB group (< 4 g/dL) had lower levels of BMI, Hb, T-chol, TG, LDL, eGFR, ALT, LVEF, and higher CRP level. Given that the low sALB group were older, the lower levels of BMI, sALB, lipoprotein, eGFR, LVEF and liver enzyme might be associated with malnutrition because of aging.

Table 1 Baseline characteristics of the study participants.
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Relationship between the sALB and MACE

Among the 203 participants included in the analyses, a total of 28 MACE occurred, including 22 in the low sALB group and 6 in the high sALB group. Cox model was used to evaluate the association between sALB and the risk of MACE. After fully adjusting for covariates, the HR of MACE was 0.22 (95% CI 0.11–0.44, P < 0.0001) (Table 2). Cox models were established by dividing the sALB of participants into the low sALB group (< 4 g/dL) and the high sALB group (≥ 4 g/dL) according to the median level. The results showed that, compared with the high sALB group, the HR of MACE in the low sALB group was 3.52 (95% CI 1.30–9.54, P = 0.0133) in the adjusted model. which did not change markedly comparing with the previous results15. Figure 1 showed a trend in the association between sALB and the risk of MACE (logHR), indicating that the risk of MACE decreased as the baseline sALB increased. When sALB level was more than 4.0 g/dL, it remained a protective factor and the risk of MACE remained unchanged.

Table 2 Relationship between sALB and MACE in adjusted model.
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Figure 1
figure 1

Smooth curves between sALB and MACE. The graph displays the adjusted association between sALB and the risk of MACE. The model adjusted for Age; MALE (sex); BMI; ALT; T-chol; eGFR.

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Statistical interaction between sALB and TC on MACE

Figure 2 presents the association between sALB level and the risk of MACE (logHR), stratified by TC. The solid line, which represents those whose TC less than 200 mg/dL, shows a decline in MACE risk with increasing sALB level after adjusting for covariates. Contrarily, there is no such association in those whose TC more than 200 mg/dL. Because of sample size limitation, our primary outcomes were the tests for interaction between sALB and TC on MACE. The result was statistically significant in crude model (P = 0.0005), and compared with crude model, the effect of TC on the relationship between sALB and MACE did not change markedly after adjusting for age, sex, BMI, ALT, eGFR in the multivariable analysis (P = 0.0005) (Table 3).

Figure 2
figure 2

Smooth curves between sALB and MACE stratified by TC. The graph displays the adjusted association between sALB and the risk of MACE stratified by TC. The model adjusted for Age, MALE (sex), BMI, ALT, eGFR.

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Table 3 Effect modification of TC on the relationship between sALB and MACE.
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Discussion

As some previous studies reported, baseline TC and sALB seem to be risk factors of poor prognosis in CAD patients. We conducted this study mainly to explore whether there is an interaction between them. We analyzed the interaction between TC and sALB and adjusted for covariates that may have influenced the results based on literature reports and clinical practice. Our analysis showed an interaction between TC and sALB levels after adjusting for covariates, which may suggested that baseline TC may influence the effect of sALB on the risk of poor prognosis in patients with coronary artery disease undergoing percutaneous coronary intervention. The relationship between sALB and cardiovascular risk has ever been reported by some previous studies. The study by Suzuki et al. suggested that the baseline sALB was a risk factor of long-term MACE in patients undergoing PCI15. Shih-Chieh Chien et al. pointed out that sALB is one of the reliable risk factors of CVD prognosis16. Oduncu et al. retrospectively analyzed 1706 patients with ST-segment elevation myocardial infarction (STEMI) who underwent PCI and suggested that lower sALB was independent risk factor of long-term mortality17. The potential mechanisms linking TC with sALB in cardiovascular prognostic risk have not been clearly elucidated. The molecular mechanism of albumin's effect on the prognosis of CVD may be multifactorial, which may include maintaining vascular endothelial integrity, regulating vasodilation, binding toxins, anticoagulation, and antioxidation, etc.7. Burl R Don et al. pointed out that the poor nutritional status reflected by low sALB may also be one of the risk factors of poor prognosis18. Studies have pointed out that albumin plays an important role in the transport of serum and cellular cholesterol and the maintenance of cholesterol homeostasis11,16,19,20. Albumin can shuttle free cholesterol among various receptors, which will increase the movement of free cholesterol under concentration gradients between the numerous cholesterol pools present in plasma and tissues, thereby promoting and maintaining cholesterol metabolism to restore steady-state levels20. The improving of cholesterol efflux may improve the prognosis of CAD21. What’s more, Deepak Kumar et al. found that the pseudoesterase activity of albumin might be an important determinant of cholesterol biosynthesis22. These study results may suggest that the influence of albumin level on the risk of poor prognosis may be closely related to cholesterol level. In conclusion, based on our analysis, we believe that TC level may influence the effect of sALB on the risk of poor prognosis in CAD patients treated by percutaneous coronary intervention. The potential limitations of the current analysis should also be considered in interpreting the study results. First, the data of our study came from a single-center sample, mainly elderly patients with CAD who underwent PCI. Therefore, for the extension of the research conclusions, further research is still needed. Second, the patients with chronic liver diseases, end stage renal diseases, malabsorption disease can effect serum albumin and cholesterol levels, but our data did not include these situation of the participants. Thus, we do not know whether TC affects the association of sALB with MACE in these patients, so our conclusions should be applied with caution in this group of patients. Finally, we have to emphasize that our results are merely hypothesis-generating; confirmation of our findings in an independent randomized trial is essential.

Method

Research design and population

This was a secondary scientific analysis of a retrospective, single-center cohort study where the dataset was collected by Sho et al. and is now available on Dryad (via:https://doi.org/10.5061/dryad.fn6730j)15. As previously described, the cohort included hospitalized patients newly diagnosed with stable coronary artery disease and treated with PCI at Shinonoi general hospital from October 2014 to October 2017 in Shinonoi General Hospital. Patients with old myocardial infarction or malignancy were excluded. All participants were enrolled after the approval of Shinonoi General Hospital Ethics Committee and written informed consent. The research was consistent with the principles outlined in Helsinki Declaration. The outcome was major adverse cardiac events (MACE; defined as all cause mortality, non fatal myocardial infarction, and non fatal stroke). Events were validated by chart review and the median duration of follow-up was 783 days15. In our analysis, we included all the cases to revalidate the relationship between the sALB and MACE by adjusting more covariates than the previous research15, and more importantly, we tried to find the effects of TC on their association by interaction tests.

Statistical analysis

The baseline characteristics of the participants were analyzed and divided into two groups according to the median baseline sALB level. The continuous variables with normal distribution were presented as Mean ± SD (comparison between groups by t test), the continuous variables with abnormal distribution were presented as Median (IQR) (comparison between groups by Kruskal Wallis rank sum test) and the categorical variables were presented as N (%) (comparison between groups by χ2 test).

The primary outcome was to determine the effect of TC on the association between MACE and baseline sALB level. Therefore, the results of unadjusted and adjusted covariates model analysis were presented based on STROBE recommendations. When the covariate was included or excluded from the model, the hazard ratio of the match was changed by at least 10%, and this covariate needed to be adjusted23. Besides, if it was related to serum albumin and MACE in clinical practice, covariate wound be included with other congener covariates excluded. Moreover, covariates adjusted in previous congener studies were also included24. And finally we adjusted, if not stratified, for age, sex, BMI, TC, eGFR, ALT in the multivariable model.

In the multivariable regression analysis, baseline sALB level was first analyzed as a continuous variable and then divided into two groups according to the median level. Interaction tests were conducted according to TC (< 200 mg/dL and ≥ 200 mg/dL), and the models were divided into crude model and adjusted model according to the adjusted covariates.

P values less than 0.05 (two-tailed) were considered statistically significant. EmpowerStats ver.3.0 (http://www.empowerstats.net/analysis/, X&Y Solutions,Inc., Boston, MA) and the R software ver.3.3.1 (http://www.R-project.org/, The R Foundation), were used for all statistical analyses.