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Association between C-reactive protein to albumin ratio and subclinical myocardial injury in the general population free from cardiovascular disease

Journal of Cardiothoracic Surgery volume 19, Article number: 487 (2024) Cite this article

Abstract

Objective

The study aimed to examine the role of the C-reactive protein to albumin ratio (CAR) as an inflammatory biomarker in relation to subclinical myocardial injury (SC-MI), addressing the limited knowledge of their association.

Methods

The study included 5,949 individuals without cardiovascular disease (CVD) from the National Health and Nutrition Examination Survey. SC-MI was identified through a Cardiac Infarction Injury Score (CIIS) of ≥ 10 units based on a 12-lead electrocardiogram. The study used multivariate logistic regression models, adjusted for potential confounders, to evaluate the relationship between CAR and SC-MI. Subgroup analyses were conducted to substantiate the results, and the non-linear correlation was assessed via restricted cubic spline (RCS) regression.

Results

The RCS curve showed a significant positive correlation between CAR and SC-MI (P for nonlinear = 0.2496). When adjusted for all confounders, individuals in the highest tertile of CAR exhibited a higher likelihood of SC-MI compared to those in the lowest tertile, with an odds ratio (OR) of 1.21 (95% CI: 1.06–1.39, P for trend = 0.029). A 10-unit increment in CAR was linked to a 3.6% heightened risk of SC-MI [OR = 1.036 (95% CI: 1.006, 1.066)], with this association being more prominent among male adults, non-smokers, married individuals, those without diabetes mellitus, and those with no history of cancer.

Conclusion

The findings of this study suggest a positive correlation between CAR and SC-MI among the US adult population, indicating the potential of CAR in enhancing SC-MI prevention strategies in the general population.

Peer Review reports

Introduction

Currently, the incidence of cardiovascular disease (CVD) is on the rise, paralleling improvements in living standards and the advent of an aging global population [1, 2]. Over the past three decades, the global prevalence of CVD has seen a marked increase, nearly doubling from 271 million cases in 1990 to 523 million in 2019. Concurrently, the number of deaths attributable to CVD has risen substantially, from 12.1 million in 1990 to 18.6 million in 2019 [3]. Thus, it is crucial to focus on primary prevention strategies before ischemic heart disease develops. Clinical evaluation and accurate correction of CVD risk factors could possibly avert the development of structural heart damage and dysfunction and reduce the incidence of CVD [4]. For example, a higher level of triglyceride glucose index-waist circumference is associated with an increased risk of first myocardial infarction in patients with hypertension [5]. Metabolic score for insulin resistance may serve as a significant predictor of high-risk CVD [6]. Subclinical myocardial injury (SC-MI) represents an early phase in the progression of CVD. Timely detection and intervention of SC-MI have the potential to delay or even reverse the onset and advancement of CVD. SC-MI is characterized by a cardiac infarction/injury score (CIIS) of 10 or higher, in the absence of clinically apparent coronary heart disease and heart failure [7]. Serving as an early stage of CVD, SC-MI has been demonstrated to have a direct association with both the incidence and mortality rates of CVD [8, 9]. Therefore, it was very important to identify the risk factors and predictors of SC-MI. Hence, identifying the risk factors and predictors of SC-MI is of significant importance.

C-reactive protein (CRP) is a non-specific acute-phase reactant traditionally associated with inflammation and is considered to play a vital role in the progression of CVD [10,11,12]. Higher CRP concentrations may be indicative of an acute infection or inflammation. Sustained higher levels of CRP over time are associated with an increased risk of developing CVDs and conditions that contribute to atherosclerosis [13]. Serum albumin, synthesized by the liver and serving as a negative acute-phase reactant, is a 69 kDa protein that constitutes over half of the total serum protein composition in the body [14]. Research indicates that serum albumin levels are correlated with a high risk of mortality due to CVD across a range of clinical conditions [15,16,17]. Recently, the CRP to albumin ratio (CAR), a novel inflammatory marker that combines two metrics (elevated CRP and reduced albumin levels), has emerged as a more significant predictive biomarker for assessing inflammatory status and prognosis in diverse clinical environments compared to using CRP or albumin independently [18,19,20,21]. A higher CAR indicates greater inflammation and oxidative stress, leading to subclinical myocardial damage before clinical symptoms appear. Despite the proposed inflammatory hypothesis in CVD pathogenesis, the relationship between the CAR and SC-MI has not yet been examined. In light of this, our objective is to investigate the link between CAR and the risk of SC-MI in a population-based sample without prior CVD history and to analyze the association of CAR with the risk of SC-MI within the same group of participants.

Therefore, we aimed to investigate the cross-sectional relationship between the CAR and SC-MI within a subset of participants from the third National Health and Nutrition Examination Survey (NHANES III) who have not been clinically diagnosed with CVD. Our hypothesis posits that CAR is linked to the prevalence of SC-MI independent of other potential confounding factors.

Methods

Study population

The participants were selected from NHANES III, which reflects the civilian non-institutionalized US population from 1988 to 1994. The data used in this study, including demographic characteristics, medical history, laboratory measurements, and electrocardiogram data, were obtained from the publicly available NHANES III database. Further information about the NHANES III study protocol and data collection procedures has been detailed in previous publications [22]. The NHANES III study protocol received approval from the Institutional Review Board of the National Center for Health Statistics, which is part of the Centers for Disease Control and Prevention. All participants in the current study gave their written informed consent upon enrollment, ensuring that the study adhered to the ethical guidelines established by the 1975 Declaration of Helsinki. Individuals diagnosed with CVDs, such as heart failure, stroke, and myocardial infarction, those presenting significant electrocardiograph (ECG) abnormalities, and those lacking ECG and CAR measurement data were excluded from the study. Finally, 5949 participants were enrolled in the study (Fig. 1).

Fig. 1
figure 1

The flow chart of the selection process. The major ECG abnormalities identified in NHANES III typically include: Myocardial Infarction (MI); Left Ventricular Hypertrophy (LVH); Bundle Branch Block (BBB); Atrial Fibrillation (AF); ST-T Changes; Premature Ventricular Contractions

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Variables collection and definitions

Participant demographics, including age, sex, race, marital status, family income to poverty ratio (PIR), smoking history, cancer history, hypertension, and diabetes, were collected through a standardized interview questionnaire. The study categorized race into four groups: non-Hispanic White, non-Hispanic Black, Mexican American, and Other. Marital status was divided into married and unmarried categories. Individuals who reported smoking more than 100 cigarettes in their lifetime were classified as smokers. Anthropometric data, including height, weight, waist circumference, systolic blood pressure (SBP) and diastolic blood pressure (DBP), were collected during physical examinations. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared (kg/m2). Blood markers, such as total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), fasting plasma glucose (FPG), hemoglobin A1c (HbA1c), creatinine, blood urea nitrogen (BUN), uric acid, albumin, and CRP, were determined through standard biochemical assays. Information on the specific methods used to determine each covariate and the quality control processes involved was available through the NHANES website.

Definition of SC-MI

During visits for mobile examinations, skilled technicians utilized a Marquette MAC 12 system (Marquette Medical Systems, Milwaukee, Wisconsin) to perform resting 12-lead electrocardiographs (ECGs). The identification of SC-MI was achieved through a cost-effective and accessible method using a 12-lead ECG to calculate a risk score known as the Cardiac Infarction/Injury Score (CIIS) [7]. This was done by employing a complex decision-making classification system based on ECG data, aimed at determining the extent of myocardial injury or ischemia. This system, developed by experts, analyzes specific ECG waveforms indicative of myocardial ischemia. More detailed information on this methodology is available in other comprehensive documents [23]. As previously mentioned, SC-MI was characterized by a CIIS of 10 or higher in the absence of clinically diagnosed coronary heart disease and heart failure [7, 23]. CAR was calculated by dividing CRP levels by albumin levels. Incorporating CIIS > 10 as a marker of SC-MI into risk calculators could enable the identification of individuals with subclinical myocardial damage who may be at higher risk for future cardiovascular events or complications. Previous studies have demonstrated that SC-MI, as identified by CIIS > 10, is associated with an increased risk of cardiovascular morbidity and mortality [9]. Therefore, including CIIS > 10 in risk prediction models could potentially improve their predictive accuracy by adding an independent risk factor that captures subclinical myocardial injury beyond traditional risk factors. CIIS detected MI with a sensitivity of 85% and a specificity of 95%, and at a higher severity level, a specificity of 99% was achieved with a sensitivity of 71% [23].

Statistical analysis

In the NHANES study, sample weights were used to adjust for unequal probability of distribution, oversampling, and sampling bias. Sample weights were utilized to estimate demographic characteristics across various survey cycles. For continuous variables, data were presented as weighted means with their 95% confidence intervals (CIs), while for categorical variables, data were expressed as weighted percentages with 95% CIs. Variables that have more than 10% missing values were excluded from the multivariate adjustment process. The potential nonlinear correlation between CAR and SC-MI, after adjusting for age, sex, race, smoking status, marital status, history of diabetes, history of hypertension, history of malignancy, SBP, DBP, TC, HDL-C, creatinine, uric acid, and BUN, was assessed using logistic regression models with restricted cubic splines. Participants were categorized into quartiles based on their CAR values. To address potential confounding factors, no variables were adjusted for in Model I. In Model II, adjustments were made for age, gender, race, smoking status, and marital status to account for basic demographic and lifestyle factors. Model III included a more comprehensive adjustment for age, sex, race, smoking status, marital status, and medical history factors such as diabetes, hypertension, and malignancy, as well as physiological measurements, including SBP, DBP, TC, HDL-C, creatinine, uric acid, and BUN. Subgroup analysis was conducted using multivariable logistic regression, stratified by factors such as age (< 60 or ≥ 60 years), sex (male or female), race (including non-Hispanic White, non-Hispanic Black, Mexican American, and others), smoking status (yes or no), marital status (married or unmarried), presence of hypertension (yes or no), presence of diabetes mellitus (yes or no), and history of malignancy (yes or no). This analysis aimed to explore the relationship between CAR levels and SC-MI, adjusting for all covariates listed in Model III, except for the variable used for stratification. To assess the statistical significance of interactions, interaction terms were created between CAR and different subgroups. For dichotomous variables, Wald tests were employed, while likelihood ratio tests were utilized for continuous variables. To validate the robustness of our findings, we conducted a sensitivity analysis to determine the optimal cut-off value for the C-reactive protein to albumin ratio (CAR) using Receiver Operating Characteristic (ROC) analysis. The optimal cut-off value was identified at the point with the maximum Youden index, which balances sensitivity and specificity.

Results

Characteristics of study population

A total of 5,949 adults were included in the final analysis (weighted mean age, 55.13 years; 95% CI: 54.44–55.83 years), with 2,722 being male (weighted percentage, 44.22%; 95% CI: 42.46–45.99%). The weighted mean (standard error, SE) for CAR was 10.42 ± 0.34. The demographic baseline characteristics of the participants included in the study are presented in Table 1. Compared to individuals without SC-MI, participants with SC-MI were older and showed a higher percentage of smoker and higher prevalence of hypertension and diabetes mellitus (P < 0.05). The levels of C-reactive protein, albumin, systolic blood pressure, fasting plasma glucose, and HbA1c were also higher among the population with SC-MI.

Table 1 Baseline characteristics of study population, weighted
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Association between CAR and SC-MI

Participants were divided into three groups according to the tertiles of CAR. The results in Table 2 present a multivariate regression analysis investigating the association between CAR levels and SC-MI using three progressively adjusted models. The odds ratios (OR) and 95% confidence intervals (CI) are reported for changes in CAR levels and their categorization into tertiles (low, middle, high). Model I, which does not include any adjustments, shows that each 10-units increase in CAR is associated with an OR of 1.041 (95% CI: 1.013–1.071, P = 0.00449). For CAR tertiles, the middle and high tertiles are associated with ORs of 1.196 (95% CI: 1.055–1.356, P = 0.00503) and 1.294 (95% CI: 1.140–1.469, P = 0.00007) respectively, compared to the low tertile, which serves as the reference group. Model II adjusts for age, gender, race, smoking status, and marital status. Each 10-units increase in CAR yields an OR of 1.036 (95% CI: 1.007–1.066, P = 0.01355). The middle and high tertiles have ORs of 1.141 (95% CI: 1.003–1.298, P = 0.04568) and 1.251 (95% CI: 1.096–1.429, P = 0.00090) respectively. Model III, which includes adjustments for additional factors such as medical history (diabetes, hypertension, malignancy), physiological measurements (systolic and diastolic blood pressure, total cholesterol, HDL-C, creatinine, uric acid, blood urea nitrogen), and demographics maintained from Model II, reports an OR of 1.036 (95% CI: 1.006–1.066, P = 0.01667) for each 10 units increase in CAR. For the tertiles, the ORs are 1.129 (95% CI: 0.990–1.287, P = 0.07103) for middle and 1.210 (95% CI: 1.056–1.387, P = 0.00619) for high tertiles. The statistical significance of the trend across tertiles was also tested, yielding P-values of 0.00209, 0.00591, and 0.02916 for Models I, II, and III respectively, indicating a significant trend in the association of CAR levels with SC-MI risk across all models. In addition, restricted cubic spline analysis with fully-adjusted Model III showed that there was a linear positive correlation between CAR and the risk of SC-MI (P for nonlinearity = 0.2496; Fig. 2).

Table 2 Multivariate regression analysis of CAR and SC-MI
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Fig. 2
figure 2

The dose–response relation between C-reactive protein to albumin ratio and subclinical myocardial injury

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Subgroup analyses and sensitivity analysis

In subgroup analyses stratified by age, sex, race, smoking status, marital status, malignancy, hypertension, and diabetes, we identified a significant positive correlation between CAR and the risk of SC-MI within the male subgroup, nonsmokers, married participants, those without diabetes mellitus, and individuals free from malignancy. The interaction analysis revealed no significant modifications in the effect of CAR on SC-MI risk across the studied variables (P for interaction > 0.05) (Fig. 3). Sensitivity analyses were conducted to assess the robustness of our findings. After excluding participants with minor ECG abnormalities, the results remained stable, confirming the reliability of the primary outcomes (Table S1). The ROC analysis revealed an optimal CAR cut-off value of 5.68. This cut-off value was subsequently used in the sensitivity analyses to evaluate the robustness of the association between CAR and SC-MI. The results of the sensitivity analysis are consistent with the primary findings, further validating the significant association between higher CAR levels and increased risk of SC-MI. Participants with CAR values above the optimal cut-off exhibited a markedly higher risk of SC-MI, as evidenced by an OR of 1.152 (95% CI:1.030–1.289, P = 0.0132) after adjusting for all potential confounders.

Fig. 3
figure 3

Subgroup analysis between C-reactive protein to albumin ratio and subclinical myocardial injury

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Discussion

Our analysis of the National Health and Nutrition Examination Survey data revealed significant insights into the relationship between CAR and SC-MI in individuals without cardiovascular disease. The findings from the RCS regression illustrated a linear association between CAR levels and the likelihood of SC-MI, which was statistically significant across various models of adjustment. Individuals in the highest tertile of CAR had a 21% increased risk of SC-MI compared to those in the lowest tertile. Moreover, a 10-unit increment in CAR was associated with a 3.6% increased risk of SC-MI. These associations were more pronounced in specific subgroups, including male adults, non-smokers, married individuals, those without diabetes mellitus, and those without a history of cancer, indicating potential demographic and health-related factors that might influence this relationship. The findings from this study are novel as it is the first to explore the association between the CAR and subclinical myocardial injury, filling a gap in the existing literature.

The positive link between CAR and SC-MI found in our study supports earlier research that identifies CAR as an important inflammatory marker for assessing cardiovascular risk. Higher CAR levels are associated with an increased risk of cardiovascular disease (CVD) events. Previous studies have shed light on the potential mechanisms underlying the roles of CRP and albumin in contributing to cardiovascular pathology. CRP, a classical acute-phase reactant, is primarily produced by hepatocytes in response to inflammatory stimuli, such as interleukin-6 (IL-6) and tumor necrosis factor-alpha [24, 25]. Elevated CRP levels are associated with endothelial dysfunction, a key initiating event in atherosclerosis. CRP can directly induce the expression of adhesion molecules on endothelial cells, promoting monocyte recruitment and subsequent foam cell formation, ultimately leading to atherosclerotic plaque development [26, 27]. On the other hand, albumin possesses antioxidant and anti-inflammatory properties. It acts as a scavenger of reactive oxygen species (ROS) and can bind to pro-inflammatory molecules like fatty acids, thereby mitigating their deleterious effects [28]. Lower albumin levels have been associated with endothelial dysfunction, increased oxidative stress, and impaired anti-inflammatory capacity, contributing to the pathogenesis of atherosclerosis and cardiovascular complications [29, 30]. Our results highlight the potential clinical significance of CAR as a novel biomarker for assessing the risk of subclinical myocardial injury, which could aid in the early prevention of cardiovascular disease. A study on a Chinese community population found that individuals in the highest CAR quartile had a 26% higher risk of developing CVD compared to those in the lowest quartile [31].

Previous studies have investigated the predictive value of CAR for various cardiovascular outcomes [21, 31, 32]. However, our study uniquely contributes to the existing literature by specifically focusing on the relationship between CAR and SC-MI in a population free from clinically apparent CVD. These studies primarily examine the relationship between CAR and clinical outcomes in patients with established cardiovascular conditions such as coronary artery disease (CAD) and heart failure. Our study extends this knowledge by exploring the potential of CAR as a marker for early myocardial injury in a generally healthy population, which has significant implications for early intervention and prevention strategies. Furthermore, our findings offer new perspectives on its specific association with subclinical forms of myocardial injury. The consistency of our findings with these studies supports the hypothesis that inflammation plays a crucial role in the early stages of myocardial injury. Nevertheless, when examining the strength of associations and the particular subgroups impacted, some inconsistencies may occur, possibly due to variations in study populations, research methods, or the definitions used for cardiovascular outcomes.

The biological mechanisms of the observed association between CAR and SC-MI can be attributed to the roles of CRP and albumin in the inflammatory and nutritional pathways. CRP, an acute-phase protein, increases in response to inflammation, which is a known risk factor for myocardial damage [33]. Albumin, on the other hand, has antioxidant and anti-inflammatory properties [34]. A high CAR may reflect a state of heightened inflammation and oxidative stress coupled with a reduced anti-inflammatory reserve, creating conditions that may lead to subclinical damage to the heart muscle. These mechanisms are critical in understanding the potential progression from subclinical injury to cardiovascular disease.

The implications of our findings are twofold. In clinical practice, measuring CAR could enhance the stratification of risk for SC-MI, potentially guiding early interventions in high-risk individuals. This could be particularly useful in primary prevention settings, where identifying subclinical disease may allow for the implementation of targeted strategies to prevent progression to clinical heart disease. From a public health perspective, the findings highlight the significance of including inflammatory markers like CAR in screening protocols to improve cardiovascular risk assessment. However, caution is necessary when interpreting and utilizing CAR due to its association with various confounders and the need for further validation in diverse populations.

This study’s strengths include its use of a nationally representative dataset and robust statistical methods to adjust for multiple confounders, enhancing the generalizability and reliability of the findings. However, the observational nature of the study prevents the determination of causality, and the possibility of residual confounding factors cannot be completely ruled out. One limitation of our study is the inherent bias introduced by using the CIIS as the criterion to segregate participants into SC-MI positive and SC-MI negative groups. The marked difference in CIIS between these groups is self-evident, as it was the basis for group assignment. This introduces the potential for circular reasoning and limits the generalizability of our findings to populations defined by alternative criteria for SC-MI or myocardial injury. As an electrocardiographic tool designed to assess the extent of injury in acute myocardial infarction, the CIIS may not accurately capture the true extent of myocardial injury in the asymptomatic population we studied. Therefore, our extrapolations and conclusions should be interpreted with caution, and future studies using independent markers or definitions of SC-MI are warranted to validate our findings. Future studies should focus on validating the predictive value of CAR for SC-MI in prospective cohorts and exploring its utility in diverse populations. Moreover, mechanistic studies are necessary to further clarify how CAR affects myocardial injury, potentially identifying new targets for therapeutic intervention.

Conclusions

In conclusion, our study highlights the significant association between higher CAR levels and an increased risk of SC-MI in a general population free from cardiovascular disease. These findings suggest that CAR may serve as a useful biomarker for identifying individuals at risk of SC-MI, thereby contributing to more effective prevention strategies. However, further research is necessary to confirm these results and clarify the mechanisms underlying this association.

Data availability

No datasets were generated or analysed during the current study.

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Acknowledgements

Not applicable.

Funding

The study was supported by the Jiangbei District Science and Technology Programme (No. 2021C02) and the Jiangbei District Science and Technology Programme (No. 2022C04).

Author information

Author notes

  1. Shuiying Li and Yichen Wang contributed equally to this work.

Authors and Affiliations

  1. Department of Cardiology, Ningbo Ninth Hospital, No. 68 Xiangbei Road, Jiangbei District, Ningbo, 315000, Zhejiang, China

    Shuiying Li, Na Xu & Daqi Xie

  2. Department of Hyperbaric Oxygen, Rehabilitation Hospital Affiliated to National Research Center for Rehabilitation Technical Aids, Beijing, 100000, China

    Yichen Wang

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  2. Yichen Wang
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  3. Na Xu
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  4. Daqi Xie
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Contributions

LSY and WYC conceived the study, contributed to methodology and data collection, and wrote the first draft of manuscript, XN was responsible for data analysis and visualization, and XDQ completed manuscript revisions. All authors reviewed the manuscript.

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Correspondence to Daqi Xie.

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Ethical review and approval were waived for this study, due to study data obtained from the National Health and Nutrition Examination Survey.

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Li, S., Wang, Y., Xu, N. et al. Association between C-reactive protein to albumin ratio and subclinical myocardial injury in the general population free from cardiovascular disease. J Cardiothorac Surg 19, 487 (2024). https://doi.org/10.1186/s13019-024-02988-1

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Keywords

Journal of Cardiothoracic Surgery

ISSN: 1749-8090

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