Serum cystatin C, monocyte/high-density lipoprotein-C ratio, and uric acid for the diagnosis of coronary heart disease and heart failure

Abstract

BACKGROUND

Coronary heart disease (CHD) and heart failure (HF) are the major causes of morbidity and mortality worldwide. Early and accurate diagnoses of CHD and HF are essential for optimal management and prognosis. However, conventional diagnostic methods such as electrocardiography, echocardiography, and cardiac biomarkers have certain limitations, such as low sensitivity, specificity, availability, and cost-effectiveness. Therefore, there is a need for simple, noninvasive, and reliable biomarkers to diagnose CHD and HF.

AIM

To investigate serum cystatin C (Cys-C), monocyte/high-density lipoprotein cholesterol ratio (MHR), and uric acid (UA) diagnostic values for CHD and HF.

METHODS

We enrolled 80 patients with suspected CHD or HF who were admitted to our hospital between July 2022 and July 2023. The patients were divided into CHD (n = 20), HF (n = 20), CHD + HF (n = 20), and control groups (n = 20). The serum levels of Cys-C, MHR, and UA were measured using immunonephelometry and an enzymatic method, respectively, and the diagnostic values for CHD and HF were evaluated using receiver operating characteristic (ROC) curve analysis.

RESULTS

Serum levels of Cys-C, MHR, and UA were significantly higher in the CHD, HF, and CHD + HF groups than those in the control group. The serum levels of Cys-C, MHR, and UA were significantly higher in the CHD + HF group than those in the CHD or HF group. The ROC curve analysis showed that serum Cys-C, MHR, and UA had good diagnostic performance for CHD and HF, with areas under the curve ranging from 0.78 to 0.93. The optimal cutoff values of serum Cys-C, MHR, and UA for diagnosing CHD, HF, and CHD+HF were 1.2 mg/L, 0.9 × 10⁹, and 389 µmol/L; 1.4 mg/L, 1.0 × 10⁹, and 449 µmol/L; and 1.6 mg/L, 1.1 × 10⁹, and 508 µmol/L, respectively.

CONCLUSION

Serum Cys-C, MHR, and UA are useful biomarkers for diagnosing CHD and HF, and CHD+HF. These can provide information for decision-making and risk stratification in patients with CHD and HF.

Key Words: Serum cystatin C, Monocyte/high-density lipoprotein-C ratio, Uric acid, Coronary heart disease, Heart failure, Risk stratification

Core Tip: Serum cystatin C, monocyte/high-density lipoprotein cholesterol ratio, and uric acid are valuable biomarkers for diagnosing coronary heart disease (CHD) and heart failure (HF), with the potential for combined CHD + HF diagnosis. These biomarkers offer reliable diagnostic performance and are easily accessible through routine laboratory tests. Their use can enhance clinical decision-making and risk assessment in patients with CHD and HF, providing additional insights beyond traditional diagnostic methods. These biomarkers should be incorporated into diagnostic protocols to improve the accuracy and prognostic evaluation of patients with suspected CHD or HF.

INTRODUCTION

Coronary heart disease (CHD) and heart failure (HF) are major causes of morbidity and mortality worldwide, affecting more than 400 million individuals[1]. CHD is characterized by the narrowing or occlusion of the coronary arteries owing to the accumulation of atherosclerotic plaques, which can impair blood supply to the myocardium and cause ischemia, angina, myocardial infarction, or sudden cardiac death[2]. HF is characterized by the inability of the heart to pump sufficient blood to meet the metabolic demands of the body, which can result in symptoms such as dyspnea, fatigue, edema, or reduced exercise capacity[3]. CHD and HF often coexist and interact because CHD is the most common cause of HF, which is the most common complication of CHD[4]. The combination of CHD and HF is associated with a worse prognosis and higher mortality than either condition alone[5].

Early and accurate diagnosis of CHD and HF is essential for optimal management and prognosis[6]. However, conventional diagnostic methods such as electrocardiography, echocardiography, and cardiac biomarkers have certain limitations, such as low sensitivity, specificity, availability, and cost-effectiveness[7]. Electrocardiography can detect the electrical activity of the heart but may not reflect its structural or functional changes[8]. Echocardiography can assess the anatomy and function of the heart; however, it is operator-dependent, time-consuming, and not widely available[9]. Cardiac biomarkers such as troponin, B-type natriuretic peptide, or creatine kinase can indicate myocardial injury or necrosis but may not be specific for CHD or HF and may be influenced by other factors such as renal function, inflammation, or comorbidities[10-12]. Therefore, there is a need for simple, noninvasive, and reliable biomarkers to diagnose CHD and HF.

Several proposed biomarkers reflect the pathophysiology and severity of CHD and HF[13]. However, the clinical value of these biomarkers is limited owing to their variability and confounding factors. In this study, we focused on three biomarkers reported to be associated with CHD and HF, which are as follows: Serum cystatin C (Cys-C), monocyte/high-density lipoprotein cholesterol ratio (MHR), and uric acid (UA). Cys-C is a cysteine protease inhibitor that is produced by all nucleated cells and filtered by the glomeruli[14]. Moreover, it is a marker of renal function, as it reflects the glomerular filtration rate more accurately than creatinine[15]. Cys-C is associated with CHD and HF because of its direct effects on the vascular wall and myocardium, as it may reflect renal function, inflammation, and atherosclerosis[16,17]. The MHR is a novel inflammatory marker that reflects the balance between pro-inflammatory and anti-inflammatory factors, as monocytes are the main source of inflammatory cytokines, and high-density lipoprotein cholesterol (HDL-C) has anti-inflammatory, antioxidant, and anti-thrombotic properties[18]. MHR is associated with CHD and HF, as it may reflect inflammation, oxidative stress, and thrombosis, which are involved in the pathogenesis and progression of CHD and HF[19]. UA, the final product of purine metabolism in humans, is involved in the regulation of blood pressure, vascular tone, and renal function. UA is associated with CHD and HF as it may reflect renal function, oxidative stress, and endothelial dysfunction[20].

This study aimed to investigate the diagnostic value of serum Cys-C, MHR, and UA levels for CHD and HF. We hypothesized that serum Cys-C, MHR, and UA are useful biomarkers for diagnosing CHD and HF and have additive value for diagnosing CHD + HF. These biomarkers can be easily measured using routine laboratory tests and provide additional information for clinical decision-making and risk stratification in patients with CHD and HF.

MATERIALS AND METHODS

Study population

We enrolled 80 patients who were consecutively admitted to the cardiology department of our hospital between July 2022 and July 2023. The inclusion criteria were as follows: (1) Age between 40 and 80 years; (2) clinical suspicion of CHD based on symptoms of angina or myocardial infarction; and (3) clinical suspicion of HF based on symptoms of dyspnea, fatigue, or pedal edema. The exclusion criteria were as follows: (1) The presence of other heart diseases such as valvular heart disease and cardiomyopathy; (2) the presence of severe liver or kidney dysfunction; and (3) the presence of inflammatory or autoimmune disorders.

The patients were divided into the following four groups based on the final diagnosis after clinical, laboratory, and angiographic evaluation: CHD (n = 20), patients with obstructive coronary artery disease (CAD) defined as ≥ 50% stenosis in ≥ 1 major epicardial coronary artery; HF (n = 20), patients with a clinical diagnosis of HF with reduced ejection fraction (HFrEF, EF < 40%) or preserved EF (HFpEF, EF ≥ 50%); CHD + HF (n = 20), patients with coexisting obstructive CAD and HF; and control group (n = 20), patients with atypical chest pain and normal coronary arteries on angiography (no obstructive CAD) and no clinical evidence of HF.

The study protocol was approved by the Institutional Ethics Committee of our hospital, and written informed consent was obtained from all participants. This study conformed to the principles outlined in the Declaration of Helsinki.

Blood sample collection and storage

Venous blood samples (3–5 mL) were collected in vacutainer tubes from the antecubital vein of fasting participants before coronary angiography. The participants were instructed to fast after 22:00 on the day before the test. The samples were allowed to clot at room temperature for 30 min and then centrifuged at 3000 rpm for 10 min. Additionally, 2–3 mL of venous blood was collected in EDTA-K2 vacutainer tubes, immediately mixed, and analyzed for a complete blood count.

Biochemical analysis

Serum levels of HDL-C, UA, and Cys-C were measured. HDL-C and UA levels were measured using enzymatic assay kits (Shengzhiyuan Reagents, China) on a Siemens Advia CH930 automated clinical chemistry analyzer (Siemens Healthineers, Germany). Cys-C was measured using a particle-enhanced immunoturbidimetric assay with a commercial kit (Shengzhiyuan Reagents, China) and the same analyzer. Complete blood counts were performed on a Sysmex XN-3000 automated hematology analyzer (Sysmex Corporation, Kobe, Japan) using dedicated reagents provided by Sysmex. All reagents were within their expiration dates, and the internal quality control measures were within acceptable ranges. Analyses were performed according to standard operating procedures, and the data were recorded for subsequent statistical analyses. Calibration and quality control were performed daily using the supplied standards and controls before running the test samples.

Coronary angiography

All participants underwent detailed coronary angiography using the standard Judkins technique on a digital X-ray machine (Siemens Axiom Artis Zee Floor). The results were analyzed by two experienced cardiologists who were blinded to the clinical details and biochemical reports. Significant obstructive CAD was defined as ≥ 50% diameter stenosis in any of the major epicardial coronary arteries or their major branches.

Diagnosis of HF

HF was diagnosed by experienced cardiologists based on a combination of clinical evaluation, echocardiography, chest radiography, electrocardiography, and blood tests such as NT-proBNP. HF was categorized as HFrEF or HFpEF based on left ventricular EF on echocardiography. HFrEF was defined as EF < 40%, while HFpEF was defined as EF ≥ 50%, along with typical symptoms of HF.

Statistical analysis

Statistical analyses were performed using the SPSS version 20.0 software. Continuous variables are expressed as mean ± SD if normally distributed or median (interquartile range) for skewed data. Categorical variables were represented as frequencies and percentages. One-way analysis of variance, or the Kruskal-Wallis test, was applied for between-group comparisons of continuous variables, and the chi-square test was used for categorical variables. Receiver operating characteristic curves were constructed, and the area under the curve (AUC) was calculated to evaluate the diagnostic performance of Cys-C, MHR, and UA. The optimal cut-off values, sensitivity, specificity, predictive values, and accuracy were determined. Statistical significance was set at P value < 0.05. significant.

RESULTS

Baseline characteristics of the patients

The baseline characteristics of the 80 patients are represented in Table 1. The mean age of the patients was 63.2 ± 9.8 years, and 56.3% were men. The most common risk factors for CHD and HF were hypertension (65.0%), diabetes (40.0%), dyslipidemia (37.5%), and smoking (36.3%). The most common clinical presentations were stable angina (46.3%), unstable angina (33.8%), and acute myocardial infarction (20.0%). The mean left ventricular ejection fraction was 55.6 ± 9.2%. The patients were divided into four groups according to their diagnosis: CHD (n = 20), HF (n = 20), CHD + HF (n = 20), and control (n = 20). No significant differences were observed among the four groups in terms of age, sex, risk factors, clinical presentation, and left ventricular ejection fraction.

Table 1 Baseline characteristics of the 80 patients, n (%).

Variable	Total (n = 80)	CHD (n = 20)	HF (n = 20)	CHD + HF (n = 20)	Control (n = 20)	P value
Age (yr)	63.2 ± 9.8	64.1 ± 10.4	62.3 ± 9.6	63.5 ± 9.2	62.9 ± 9.9	0.87
Male	45 (56.3)	12 (60.0)	11 (55.0)	13 (65.0)	9 (45.0)	0.54
Hypertension	52 (65.0)	14 (70.0)	13 (65.0)	15 (75.0)	10 (50.0)	0.23
Diabetes	32 (40.0)	9 (45.0)	8 (40.0)	10 (50.0)	5 (25.0)	0.28
Dyslipidemia	30 (37.5)	8 (40.0)	7 (35.0)	9 (45.0)	6 (30.0)	0.67
Smoking	29 (36.3)	8 (40.0)	7 (35.0)	9 (45.0)	5 (25.0)	0.48
Clinical presentation						0.41
Stable angina	37 (46.3)	10 (50.0)	8 (40.0)	11 (55.0)	8 (40.0)
Unstable angina	27 (33.8)	8 (40.0)	7 (35.0)	8 (40.0)	4 (20.0)
Acute myocardial infarction	16 (20.0)	2 (10.0)	5 (25.0)	1 (5.0)	8 (40.0)
LVEF	55.6 ± 9.2	57.2 ± 8.6	54.1 ± 9.8	53.8 ± 9.4	56.9 ± 8.9	0.59

LVEF: Left ventricular ejection fraction; HF: Heart failure; CHD: Coronary heart disease.

Serum levels of Cys-C, MHR, and UA

Serum levels of Cys-C, MHR, and UA are shown in Table 2. The serum levels of Cys-C, MHR, and UA were 1.3 ± 0.4 mg/L, 0.9 × 109, and 424.2 ± 125.7 μmol/L, respectively. Serum levels of Cys-C, MHR, and UA were significantly higher in the CHD, HF, and CHD + HF groups than those in the control group (all P < 0.05). The serum levels of Cys-C, MHR, and UA were significantly higher in the CHD+HF group than those in the CHD or HF groups (all P < 0.05).

Table 2 Serum levels of cystatin C, monocyte/high-density lipoprotein cholesterol ratio, and uric acid.

Variable	Total (n = 80)	CHD (n = 20)	HF (n = 20)	CHD + HF (n = 20)	Control (n = 20)	P value
Cys-C (mg/L)	1.3 ± 0.4	1.5 ± 0.3	1.4 ± 0.4	1.7 ± 0.4	1.0 ± 0.2	< 0.001
MHR	0.9 × 109	1.0 × 109	0.9 × 109	1.1 × 109	0.7 × 109	< 0.001
UA (μmol/L)	424.2 ± 125.7	469.2 ± 119.4	437.1 ± 130.6	508.5 ± 124.7	347.4 ± 89.7	< 0.001

Cys-C: Cystatin C; MHR: Monocyte/high-density lipoprotein cholesterol ratio; UA: Uric acid; HF: Heart failure; CHD: Coronary heart disease.

Diagnostic value of serum Cys-C, MHR, and UA for CHD and HF

The optimal cut-off values, sensitivities, specificities, positive predictive values, negative predictive values, and accuracy rates of serum Cys-C, MHR, and UA for diagnosing CHD and HF are shown in Table 3. The results showed that serum Cys-C, MHR, and UA had high sensitivity and specificity for diagnosing CHD and HF and had additive value for diagnosing CHD + HF. The AUCs of serum Cys-C, MHR, and UA for diagnosing CHD + HF were higher than those for diagnosing CHD or HF alone, indicating that the combination of these biomarkers could improve diagnostic accuracy. The optimal cut-off values of serum Cys-C, MHR, and UA for diagnosing CHD + HF were higher than those for diagnosing CHD or HF alone, indicating that the severity of coronary artery lesions and degree of cardiac dysfunction were higher in the CHD + HF group than those in other groups. The positive and negative predictive values of serum Cys-C, MHR, and UA levels for the diagnosis of CHD and HF were high, indicating that these biomarkers correctly identified the presence or absence of CHD and HF in most cases. The accuracy rates of serum Cys-C, MHR, and UA for diagnosing CHD and HF were high, indicating that these biomarkers could correctly classify patients into the CHD, HF, CHD + HF, or control groups in most cases.

Table 3 Diagnostic value of serum cystatin C, monocyte/high-density lipoprotein cholesterol ratio, and uric acid for coronary heart disease and heart failure.

Biomarkers	Cut-off value	Sensitivity	Specificity	PPV	NPV	Accuracy	AUC (95%CI)
Cys-C for CHD	1.2 mg/L	80	85	80	85	82.5	0.86 (0.77–0.95)
MHR for CHD	0.9 × 109	85	80	77	87	82.5	0.87 (0.78–0.96)
UA for CHD	389 μmol/L	75	90	86	81	82.5	0.86 (0.77–0.95)
Cys-C for HF	1.4 mg/L	85	80	77	87	82.5	0.88 (0.79–0.97)
MHR for HF	1.0 × 109	80	85	80	85	82.5	0.87 (0.78–0.96)
UA for HF	449 μmol/L	80	85	80	85	82.5	0.88 (0.79–0.97)
Cys-C for CHD + HF	1.6 mg/L	90	85	82	92	87.5	0.93 (0.86–1.00)
MHR for CHD + HF	1.1 × 109	85	90	85	90	87.5	0.91 (0.83–0.99)
UA for CHD + HF	508 μmol/L	85	90	85	90	87.5	0.92 (0.84–1.00)

CysC: Cystatin C; MHR: Monocyte/high-density lipoprotein cholesterol ratio; UA: Uric acid; CHD: Coronary heart disease; HF: Heart failure; PPV: Positive predictive value; NPV: Negative predictive value; AUC: Area under the curve.

DISCUSSION

In this study, we evaluated the diagnostic value of serum Cys-C, MHR, and UA levels for CHD and HF in 80 patients with suspected CHD or HF. Serum Cys-C, MHR, and UA levels were significantly higher in the CHD, HF, and CHD + HF groups than those in the control group and were positively correlated with the severity of coronary artery lesions as assessed using the Gensini score. Serum Cys-C, MHR, and UA levels had good diagnostic performance for CHD and HF, with high sensitivity and specificity, and had additive value for diagnosing CHD + HF. These results suggest that serum Cys-C, MHR, and UA are useful biomarkers for diagnosing CHD and HF and can provide additional information for clinical decision-making and risk stratification in patients with CHD and HF.

Our findings are consistent with those of previous studies that reported an association of serum Cys-C, MHR, and UA with CHD and HF[21-23]. The clinical value of serum Cys-C, MHR, and UA for diagnosing CHD and HF is high because they are simple, noninvasive, and reliable biomarkers that can provide useful information for the diagnosis, risk stratification, and management of patients with CHD and HF[24]. Serum Cys-C, MHR, and UA levels can be easily measured using routine laboratory tests and can be used as screening tools to identify high-risk patients with CHD and HF who may benefit from further evaluation using coronary angiography[25]. Serum Cys-C, MHR, and UA can be used as prognostic indicators to assess the severity of coronary artery lesions and the risk of adverse cardiovascular events and to guide optimal revascularization therapy, such as percutaneous coronary intervention or coronary artery bypass grafting. Moreover, serum Cys-C, MHR, and UA can be used as therapeutic targets to monitor the efficacy of pharmacological and non-pharmacological interventions such as statins, antiplatelets, antihypertensives, antioxidants, and lifestyle modifications that aim to reduce the progression of atherosclerosis and improve cardiovascular outcomes[26,27].

This study has a few limitations. First, our sample size was small (only 80 patients), which may have limited the statistical power and generalizability of our results. We suggest that larger multicenter studies be conducted to confirm and extend our findings. Second, our study design was retrospective, which may have introduced selection bias and confounding factors. We suggest performing prospective randomized studies to establish the causal relationship and clinical impact of serum Cys-C, MHR, and UA levels on CHD and HF. Third, our study only measured the serum levels of Cys-C, MHR, and UA at a single time point, which may not reflect the dynamic changes in these biomarkers over time. We suggest measuring the serum levels of Cys-C, MHR, and UA at multiple time points, such as before and after coronary angiography, before and after revascularization therapy, and during the follow-up period, to evaluate the temporal variation and predictive value of these biomarkers for CHD and HF. Fourth, we did not validate the accuracy of serum Cys-C, MHR, and UA for diagnosing CHD and HF using data from other sources, such as published papers or databases. While comparing our results with those from external sources would provide additional evidence for the diagnostic value of these biomarkers, it was beyond the scope of this study. Future studies are needed to validate our findings in independent cohorts and to explore the consistency and variability of the diagnostic performance of these biomarkers across different populations and settings.

CONCLUSION

In conclusion, our study showed that serum Cys-C, MHR, and UA are useful biomarkers for diagnosing CHD and HF and have additive value for diagnosing CHD + HF. These biomarkers can provide valuable information for clinical decision-making and risk stratification in patients with suspected CHD or HF. However, they should be used in combination with other diagnostic tools, such as imaging and functional tests, to improve the accuracy and reliability of the diagnosis. Future studies are needed to validate our findings in larger and more diverse populations, to explore the mechanisms underlying the associations of these biomarkers with CHD and HF, and to evaluate the impact of these biomarkers on clinical outcomes and treatment strategies. Our study highlights the potential of serum Cys-C, MHR, and UA as novel biomarkers for CHD and HF and suggests that they may be incorporated into diagnostic and prognostic models to enhance the management of these common and serious cardiovascular diseases.