The aim of this study was to investigate the association between serum albumin to serum creatinine ratio (sACR) and the prognosis of heart failure (HF). In this single-center prospective cohort study, a total of 2625 patients with HF were enrolled between March 2012 and June 2017. All patients were divided into three groups according to the tertiles of sACR. Of 2625 patients, the mean age was 57.0 ± 14.3 years. During a median follow-up time of 23 months, 666 end point events occurred. Prognosis analysis indicated that the lowest sACR was significantly associated with higher mortality risk of HF (hazard ratio [HR] = 1.920, 95% confidence interval [CI] = 1.585–2.326, p < 0.001) when compared with the highest tertile. After adjusting for covariates including age, gender, diabetes, systolic blood pressure (SBP), diastolic blood pressure, heart rate, total cholesterol, triglycerides, HDL-C, LDL-C, white blood cell count, hemoglobin, glycosylated hemoglobin, and β-blocker use, the HRs for mortality risk of HF was 1.513 (95% CI = 1.070–2.139, p = 0.019). Subgroup analysis indicated that the mortality risk of HF statistically significantly reduced with the rise in sACR in patients with no β-blocker use, patients with serum creatine less than 97 μmol/L. However, stratification by age, sex, history of hypertension, diabetes, and smoking, level of glycosylated hemoglobin, and albumin have no obvious effect on the association between sACR and the prognosis of HF. Additionally, patients with lower sACR displayed reduced left ventricular ejection fraction and increased left ventricular end-diastolic diameter. The discriminant power of sACR alone and in combination with age, gender, SBP, heart rate, and glycosylated hemoglobin were excellent with C statistic of 0.655 and 0.889, respectively. Lower sACR was an independent risk factor for mortality risk of HF.
- WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC?
Both serum albumin and serum creatinine disorders are considered risk factors for cardiovascular mortality. However, the association between serum albumin to serum creatinine ratio (sACR) and mortality in heart failure (HF) was previously unknown.
- WHAT QUESTION DID THIS STUDY ADDRESS?
This study was to investigate the association between sACR and the prognosis of HF.
- WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE?
In this study, we found that a low sACR was associated with poor prognosis of HF, particularly in patients with no β-blocker use and normal kidney function (Scr < 97 μmol/L). Moreover, the sACR alone demonstrated excellent discrimination ability for mortality risk of HF, and the combined discrimination, including sACR, age, gender, systolic blood pressure, heart rate, and glycosylated hemoglobin could substantially improve the discrimination power.
- HOW MIGHT THIS CHANGE CLINICAL PHARMACOLOGY OR TRANSLATIONAL SCIENCE?
Our findings may promote the potential use of sACR in clinical practice as a preferable predictor for mortality risk of HF.
Heart failure (HF) is a multifaceted clinical syndrome characterized by significant morbidity and mortality.1 The diagnostic criteria for HF include objective evidence of pulmonary or systemic congestion, as well as elevated levels of natriuretic peptides. In recent years, the incidence and prevalence of HF have increased continuously due to the growing aging population and presence of HF-related risk factors, such as hypertension, obesity, and diabetes.2 Despite significant advances in HF therapy, the mortality and rehospitalization rates have remained high.3
Serum albumin, which is synthesized by the liver as preproalbumin and converted to proalbumin in the lumen of the endoplasmic reticulum of hepatocytes, is the most abundant circulating protein.4, 5 It is widely recognized that albumin reflects the nutritional status of the body.6 In addition, albumin possesses anti-inflammatory, antioxidant, anticoagulant, anti-platelet aggregation, colloid osmotic pressure maintenance, and transport of various endogenous and exogenous substances.7-10 Numerous studies have demonstrated a strong correlation between albumin and cardiovascular diseases (CVDs). In a large Viennese patient cohort, Grimm et al.11 found a significant inverse association between serum albumin levels and mortality. Low serum albumin in the remote phase after acute myocardial infarction was a valuable predictor of long-term adverse outcomes.12 Furthermore, in a cohort of 1726 patients with systolic HF, hypoalbuminemia was independently associated with an increased risk of death.13 Similarly, Liu et al. also identified hypoalbuminemia as the independent predictor for death in patients with HF with preserved ejection fraction.14 Moreover, a meta-analysis involving 16,763 patients with HR revealed hypoalbuminemia as an independent predictor of all-cause mortality in patients with acute or chronic HF.15
Serum creatinine (Scr), which was widely used as the marker of renal function, is the anhydride form of creatine and mainly come from muscle metabolism.16 The relationship between Scr and CVDs has been extensively investigated. In the Heart Outcomes and Prevention Evaluation (HOPE) study, Scr greater than or equal to 1.4 mg/dL significantly increased the risk for cardiovascular events when compared with Scr less than 1.4 mg/dL.17 Additionally, Linda et al. revealed that elevated creatinine level was associated with increased risk of mortality, CVDs, and chronic HF by analyzing data from the Cardiovascular Health Study.18 Similarly, in a prospective study with 9978 participants, Scr was found to be correlated with various traditional cardiovascular risk factors and the 10-year cardiovascular risk in patients with hypertension.19
In fact, the level of serum albumin is influenced by renal function, as damaged kidneys can secrete albumin.20 Renal function-normalized albumin (the serum albumin to serum creatinine ratio [sACR]) has emerged as a new biomarker and may be a superior indicator compared to serum albumin alone. Additionally, the opposite associations of serum albumin and creatinine with CVDs suggest that sACR may have a more powerful predictive ability for CVDs. Indeed, Erdem et al. found that sACR is inversely associated with short-term clinical outcomes in patients with ST-elevation myocardial infarction (STEMI) after percutaneous coronary intervention (PCI) in a cohort of 3057 patients.21 However, the association of sACR with the prognosis of HF has not been previously reported. Therefore, our aim is to investigate the association between sACR and the prognosis of HF in the Chinese population.
We prospectively recruited 3043 patients with HF from Cardiology Division of Panzhihua Central Hospital between March 2013 and December 2017. HF was diagnosed according to the American College of Cardiology/American Hospital Association guideline, using physical examination, laboratory tests, and echocardiography22: (1) age greater than 18 years old, (2) brain natriuretic peptide levels greater than or equal to 100 pg/mL at admission, (3) presence of clinical signs and symptoms of HF, including dyspnea, gasping, weakness, and fluid retention, and (4) abnormal cardiac structure and function observed through echocardiography. The exclusion criteria included: severe valvular heart disease as the primary etiology of HF; life-threatening complications, such as severe liver dysfunction, renal dysfunction, or a history of malignancy, and life expectancy less than 1 year; second- or third-degree atrioventricular block, unless a pacemaker was present; acute myocardial infarction or unstable angina within 1 month before admission; or reject to participate in the follow-up. A total of 2625 patients were included for association analysis at the end of the study. The selection process is illustrated in Figure 1. This study was approved by the Review Board of Panzhihua Central Hospital and the written informed consents were obtained from all participants. The investigation conformed to the principles of the Declaration of Helsinki.
Clinical follow-up and data collection
In this study, we conducted follow-up every 3 months through outpatient interview or by telephone contact. The primary end points were defined as cardiovascular deaths or cardiac transplantation. Baseline demographic data included age, gender, hypertension, diabetes, and smoking were obtained from the patients' records at the time of enrollment. All patients underwent blood sampling and laboratory tests in the early morning after admission on an empty stomach. Hypertension was defined as systolic blood pressure (SBP) greater than 140 mmHg, diastolic blood pressure (DBP) greater than 90 mmHg, or currently treated with antihypertensive drugs. Diabetes was assessed by a fasting glucose level of greater than 7.8 mmol/L and/or a glucose level of greater than 11.1 mmol/L at 2 h after oral glucose challenge. A history of smoking greater than two pack-years and/or smoking within the preceding 1 year was defined as smoking. β-blocker treatment was at the discretion of the treating physician, defined as used for greater than 6 months.
Statistical analyses were conducted using SPSS version 13.0 (SPSS, Inc.) for Windows (Microsoft Corp.). Abnormally distributed quantitative data were presented as median (interquartile range), normally distributed quantitative data were presented as mean ± standard deviation (SD), and categorical variables were presented as numbers (percentages). Patients were divided into the following three groups according to sACR level tertile: T1 (sACR < 0.379), T2 (0.379 ≤ sACR < 0.508), and T3 (sACR ≥ 0.508). Differences among groups were examined using chi-squared, one-way analysis of variance, or Kruskal–Wallis tests. Cox proportional hazard regression models was used to calculate the hazard ratio (HR) and 95% confidence intervals (95% CIs) for the associations between variables and the prognosis of HF. Due to the fact that sACR was calculated from the original serum albumin and creatinine value, serum albumin and creatinine values were not included in the multivariate Cox regression model to avoid excess collinearity and potential spurious results. Furthermore, forest plots were constructed to identify whether the HRs of increased sACR differed significantly between subgroups based on baseline characteristics, glycosylated hemoglobin, serum albumin, Scr, or β-blocker use. Statistical significance was defined as p < 0.05 using two-tailed tests.
In the final analysis, a total of 2625 patients with HF were eligible for inclusion. Among all these patients, the average age was 57.0 ± 14.3 years and 68% were men. The percentage of patients with hypertension, diabetes, smoking, and β-blocker use were 38.1%, 17.7%, 38.3%, and 83.7%, respectively. The median sACR was 0.44 (0.34–0.55), with a median serum albumin level of 39.3 (36.1–42.4) g/L and a median creatinine level of 88 (73–109) μmol/L. Table 1 displays the baseline demographic and clinical characteristics of patients with HF according to tertiles of sACR. Patients with higher sACR were found to be younger and have higher level of total cholesterol, triglycerides, HDL-C, LDL-C, hemoglobin, sodium, serum albumin, and higher percentage of β-blocker use. Conversely, they had lower levels of white blood cell count, potassium, and creatinine (p < 0.05) compared to patients with low sACRs. No significant differences were observed among groups in terms of gender, hypertension, diabetes, smoking, blood pressure, heart rate, and glycosylated hemoglobin (p > 0.05).
|Characteristic||Total (n = 2625)||Serum albumin/creatine ratio tertile||p value|
|T1 (<0.379) (n = 875)||T2 (0.379–0.508) (n = 875)||T3 (≥0.508) (n = 875)|
|Age, year||57.0 ± 14.3||59.2 ± 14.2||58.0 ± 14.3||53.8 ± 13.9||<0.001|
|Male, n (%)||1776 (68)||595 (68)||612 (70)||569 (65)||0.09|
|Hypertension, n (%)||999 (38.1)||336 (38.4)||323 (36.9)||340 (38.9)||0.68|
|Diabetes, n (%)||464 (17.7)||151 (17.3)||150 (17.1)||163 (18.6)||0.66|
|Smoking, n (%)||1005 (38.3)||342 (39.1)||319 (36.5)||348 (39.8)||0.32|
|β-blocker use||2196 (83.7)||656 (75.0)||761 (87.0)||779 (89.0)||<0.001|
|SBP, mmHg||127.2 ± 24.2||127.1 ± 23.9||127.4 ± 25.3||127.2 ± 23.5||0.97|
|DBP, mmHg||80.5 ± 17.1||80.4 ± 17.1||80.5 ± 17.5||80.7 ± 16.5||0.95|
|Heart rate, beats/min||86.0 ± 19.6||85.6 ± 19.7||85.9 ± 19.7||86.4 ± 19.3||0.7|
|Total cholesterol, mg/dL||3.9 ± 1.0||3.8 ± 1.0||3.8 ± 1.0||4.1 ± 0.9||<0.001|
|Triglycerides, mg/dL||1.1 (0.8–1.6)||1.1 (0.8–1.6)||1.2 (0.8–1.6)||1.2 (0.9–1.7)||<0.001|
|HDL-C, mg/dL||0.9 (0.8–1.1)||0.9 (0.7–1.1)||0.9 (0.7–1.1)||1.0 (0.8–1.2)||<0.001|
|LDL-C, mg/dL||2.4 ± 0.9||2.4 ± 0.8||2.4 ± 0.9||2.5 ± 0.8||0.01|
|White blood cell count (×109/L)||6.2 (4.8–7.9)||6.8 (5.0–8.3)||6.2 (4.9–7.8)||5.9 (4.6–7.6)||<0.001|
|Hemoglobin, g/L||133 ± 22||129 ± 26||138 ± 18||132 ± 20||<0.001|
|Potassium, mmol/L||4.2 ± 0.5||4.3 ± 0.6||4.2 ± 0.4||4.0 ± 0.4||<0.001|
|Sodium, mmol/L||139.4 ± 3.6||138.7 ± 3.6||139.6 ± 3.9||140.0 ± 3.1||<0.001|
|Glycosylated hemoglobin, %||5.6 (1.4–6.2)||5.6 (1.2–6.2)||5.6 (1.5–6.2)||5.6 (1.5–6.1)||0.77|
|Albumin, g/L||39.3 (36.1–42.4)||37.1 (33.5–40.1)||38.7 (36.3–41.7)||42.1 (38.8–44.5)||<0.001|
|Creatinine, μmol/L||88 (73–109)||124 (107–149)||88 (82–95)||68.3 (61–76)||<0.001|
- Abbreviations: DBP, diastolic pressure; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; SBP, systolic pressure.
- p < 0.05 is considered to be statistically significant.
sACR and prognosis of HF
In our study, 21 patients were diagnosed with malignancy, 45 patients were under 18 years old, 42 patients had severe valvular heart disease, and 39 and 41 patients had severe liver and kidney dysfunction, respectively. Furthermore, 52 patients declined to participate in the follow-up, and 178 patients were lost to follow-up (Figure 1). After exclusion of 418 subjects in each category, the follow-up time for included patients was 23 (14–36) months, and during the follow-up period a total of 666 (25.4%) end points occurred. The number of end point events in the lowest, middle, and highest tertile groups were 289 (33%), 213 (24.3%), and 164 (18.7%), respectively.
First, we conducted Cox proportional hazard regression model to assess the predictive value of all variables for the mortality risk of HF. As shown in Table 2, univariate Cox proportional hazard regression model analysis demonstrated significant associations between several variables and the prognosis of HF. These variables included age (HR = 1.031, 95% CI = 1.024–1.037, p < 0.001), gender (HR = 1.254, 95% CI = 1.062–1.482, p = 0.008), diabetes (HR = 1.449, 95% CI = 1.210–1.737, p < 0.001), β-blocker use (HR = 5.885, 95% CI = 4.695–7.377, p < 0.001), SBP (HR = 0.985, 95% CI = 0.981–0.988, p < 0.001), DBP (HR = 0.982, 95% CI = 0.977–0.986, p < 0.001), heart rate (HR = 1.005, 95% CI = 1.001–1.009, p = 0.011), total cholesterol (HR = 0.781, 95% CI = 0.714–0.855, p < 0.001), triglycerides (HR = 0.675, 95% CI = 0.591–0.770, p < 0.001), HDL-C (HR = 0.634, 95% CI = 0.481–0.836, p = 0.001), LDL-C (HR = 0.889, 95% CI = 0.802–0.985, p = 0.025), white blood cell count (HR = 1.036, 95% CI = 1.009–1.063, p = 0.008), hemoglobin (HR = 0.992, 95% CI = 0.988–0.995, p < 0.001), glycosylated hemoglobin (HR = 1.055, 95% CI = 1.015–1.096, p = 0.007), and sACR (HR = 0.719, 95% CI = 0.654–0.791, p < 0.001). Subsequently, these variables with statistical significance (p < 0.05) in the univariate model were included in the multivariate model. The results revealed that only age, gender, β-blocker use, SBP, heart rate, glycosylated hemoglobin, and sACR remained statistically significant (Table 2).
|HR||95% CI||p value||HR||95% CI||p value|
|Male, n (%)||1.254||1.062–1.482||0.008||1.881||1.435–2.466||<0.001|
|Hypertension, n (%)||1.106||0.944–1.295||0.213|
|Diabetes, n (%)||1.449||1.210–1.737||<0.001|
|Smoking, n (%)||0.905||0.775–1.057||0.209|
|Heart rate, beats/min||1.005||1.001–1.009||0.011||1.007||1.001–1.014||0.022|
|Total cholesterol, mg/dL||0.781||0.714–0.855||<0.001|
|White blood cell count (×109/L)||1.036||1.009–1.063||0.008|
|Glycosylated hemoglobin, %||1.055||1.015–1.096||0.007||1.078||1.021–1.139||0.007|
- Abbreviations: CI, confidence interval; DBP, diastolic pressure; HDL-C, high-density lipoprotein cholesterol; HF, heart failure; HR, hazard ratio; LDL-C, low-density lipoprotein cholesterol; sACR, serum albumin to serum creatinine ratio; SBP, systolic pressure.
- P < 0.05 is considered to be statistically significant.
The cumulative survival rates were analyzed using Kaplan–Meier survival curve. As shown in Figure 2, the cumulative survival rates at the end of 1, 2, 3, 4, and 5 years were 80.0%, 69.9%, 64.0%, 60.5%, and 45.0% in the lowest tertile (T1), 86.5%, 76.1%, 73.5%, 68.5%, and 60.3% in the middle tertile (T2), and 90.9%, 83.6%, 78.3%, 73.4%, and 48.9% in the highest tertile (T3), respectively. The prognosis was significantly better in the highest sACR compared to the lower sACR (log rank = 48.5, p < 0.001). Additionally, a prognosis analysis was conducted by adjusting for variables associated with the prognosis of HF in the univariate Cox proportional hazard regression model analysis. As shown in Table S1, the mortality risk in T1 and T2 groups increased significantly compared to the T3 group, even after adjusting for variables in different models. To evaluate the prognostic value of the sACR in different HF phenotypes, the population was divided into two groups based on left ventricular ejection fraction (LVEF), namely HF with reduced LVEF (HFrEF; LVEF < 50%) and HF with preserved LVEF (HFpEF; LVEF ≥ 50%). As shown in Table S2, the mortality risk in the T1 group increased significantly compared to the T3 group in the HFrEF population, regardless of whether variables in different models were adjusted for. Whereas the statistical significance disappeared in the HFpEF population, which was probably due to the limited number of patients. Additionally, we split the data into training and test datasets and found that the mortality risk in the T1 group increased significantly compared to the T3 group both in training and test sets, regardless of adjustment or not (Table S3).
Subsequently, we performed a subgroup analysis to further assess the associations between sACR and the prognosis of HF in different clinically relevant subgroups. As shown in Figure 3, the mortality risk of HF statistically significantly decreased with an increase in sACR in patients who did not use β-blockers and those with Scr less than 97 μmol/L. However, stratification by age, sex, history of hypertension, diabetes, and smoking, level of glycosylated hemoglobin, and albumin did not noticeably affect the association between sACR and the prognosis of HF.
Association between sACR and clinical characteristics
Furthermore, we conducted stratified analysis to detect the association between sACR and various clinical characteristics. As shown in Figure 4, patients in the T1 and T2 groups exhibited a decreased LVEF and an enlarged left ventricular end-diastolic diameter (LVEDD) in comparison to those in the T3 group (p < 0.001). Additionally, the interventricular septal thickness at diastole (IVSD) and left ventricular posterior wall diastolic thickness (LVPWD) were notably thicker in the T1 group than in the T3 group (p < 0.001). However, the differences in IVSD and LVPWD between the T2 and T3 groups were not statistically significant.
Discriminative power analysis
We assessed the predictive power of sACR, both independently and in combination with age, gender, SBP, heart rate, and glycosylated hemoglobin, for determining the mortality risk of HF. As shown in Figure 5, the average area under the curves AUCs for sACR alone and combined discrimination were 0.655 (95% CI = 0.580–0.731) and 0.889 (95% CI = 0.855–0.924), respectively. Their true prediction rate reached up to 84.4% and 86.8%, respectively. The discrepancy between sACR alone and combined discrimination was statistically significant (p < 0.001). Additionally, the average AUCs for sACR alone and combined discrimination were 0.641 (95% CI = 0.553–0.729) and 0.872 (95% CI = 0.826–0.918) in the training set, and 0.667 (95% CI = 0.584–0.750) and 0.898 (95% CI = 0.871–0.925) in the test sets.
In this study, we examined the correlation between sACR and the prognosis of HF in the Chinese population. We found that patients with higher sACR exhibited a reduced risk of HF mortality compared to those with a lower sACR, even after adjusting for multiple risk factors. Additionally, subgroup analysis revealed the strong association between the elevated sACR and reduced mortality in patients with no β-blocker use and patients with Scr less than 97 μmol/L. However, stratification by age, sex, history of hypertension, diabetes, and smoking, level of glycosylated hemoglobin, and albumin did not significantly affect the association between sACR and the prognosis of HF. Notably, patients with lower sACR showed decreased LVEF and increased LVEDD. Moreover, the predictive power of sACR alone for mortality risk of HF was 0.655, and the combined predictive power, including sACR, age, gender, SBP, heart rate, and glycosylated hemoglobin, reached up to 0.889.
HF is the final pathway of numerous CVDs, including coronary heart disease, hypertension, valvular heart disease, and idiopathic dilated cardiomyopathy.23 The relationship between serum albumin and CVDs has been a subject of research for several years. Takao et al. found that an increase in albumin during index hospitalization was associated with a lower 1-year risk for a composite of all-cause death and hospitalization in patients with acute HF.24 In a prospective study with 5779 patients with HF, low albumin was an independent predictor of increased mortality.25 Additionally, a meta-analysis involving 16,763 HF participants revealed that patients with hypoalbuminemia had a 3.5-fold (95% CI = 1.29–9.73) higher risk for long-term all-cause mortality.15 Notably, several animal studies have also demonstrated the protective role of human serum albumin, including the anti-inflammatory effects and protective role in ischemia–reperfusion injury.26, 27
The level of serum albumin is influenced by renal function, as reflected by Scr, due to the potential secretion of albumin by damaged kidneys.20 Renal function-normalized albumin (sACR) has emerged as a novel biomarker and potentially superior to serum albumin alone as an indicator. Additionally, numerous studies have established a link between Scr and CVDs. For instance, Chen et al.19 found that Scr was associated with several traditional cardiovascular risk factors and the 10-year cardiovascular risk in patients with hypertension. In a cohort of 697 participants, elevated creatinine level was independently associated with coronary heart disease.28 Given the opposite roles of serum albumin and creatinine in CVDs, we hypothesize that sACR could be a reliable predictor for HF mortality risk, which was confirmed by our study. Previous studies have reported that sACR was inversely associated with short-term clinical outcomes in patients with STEMI after PCI,21 decreased sACR was an independent predictor of all-cause mortality and cardiac mortality.29 However, the association between the sACR and the prognosis of HF remains underexplored. To address this knowledge gap, we conducted the present study. Our results showed that reduced sACR was associated with increased mortality risk of HF.
The precise mechanism underlying the increased risk of sACR on mortality in patients with HF remains unclear. Previous studies have reported a positive association between Scr and pro-inflammatory markers such as lipoprotein (a) and high sensitive C-reactive protein.30, 31 Conversely, albumin has been identified as possessing antioxidant and anti-inflammatory properties.26, 32 It is widely recognized that inflammation and oxidative stress have played an important role in the cardiovascular events.33, 34 Elevated levels of pro-inflammatory and inflammatory indices have been associated with adverse outcomes in patients with HF.35, 36 Based on the aforementioned discussion, we hypothesize that inflammation and oxidative stress could be the biological mechanisms linking sACR to the mortality risk of HF. In summary, a reduced sACR indicates increased inflammation and oxidative stress, contributing to the poor prognosis of HF. In addition, activation of β-adrenergic receptor (β-AR) could increase the level of inflammatory factors and lead to pathological cardiac remodeling.37 Our subgroup analysis indicates that β-blocker use absolutely abolished the effect of sACR on the prognosis of HF. This suggests that the relationship between sACR and HF could be partially mediated by the β-AR signaling pathway, underscoring the significance of inflammation in this association. Furthermore, it has been demonstrated that oxidized albumin was increased with the deterioration of renal function.38 In a cohort of 248 patients undergoing dialysis with normal serum albumin levels, Lim et al.39 discovered a correlation between higher oxidized albumin levels and an increase in mortality due to cardiovascular complications. In patients with HF with impaired renal function (Scr ≥ 97 μmol/L), oxidized albumin levels increased, possibly leading to a higher mortality risk. This could counterbalance the protective effect of serum albumin on HF, which may explain why the association between sACR and the prognosis of HF is not observed in the group with Scr greater than or equal to 97 μmol/L.
Previous studies have investigated the relationship between urinary albumin-to-creatinine ratio (uACR) and HF. Liu et al.40 found that higher uACR was associated with higher all-cause mortality and HF hospitalization in a retrospective cohort study. In addition, a large cohort study involving 24,433 participants revealed that higher uACR was associated with greater risk of incident HF hospitalization in community-dwelling Black and White adults.41 There was a negative correlation between the levels of sACR and uACR. Both lower sACR and higher uACR were associated with an increase in cardiovascular events. Both indices are of importance in predicting adverse cardiovascular outcomes.
The strengths of our study include a large sample size and long follow-up time. However, there are still some limitations that need to be acknowledged. First, this is a single-center study. The conclusions drawn from this study should be validated in other centers. Second, there is a possibility of unmeasured or residual confounding effects in our observational study. Third, this study only focused on the baseline serum albumin and Scr levels, ignoring the dynamic changes in sACR, which may have provided more valuable information for understanding the underlying mechanism. Fourth, it is worth mentioning that HF is an umbrella term that encompasses different phenotypes, such as left HF, right HF, and whole HF. Each of these phenotypes requires different treatment modalities in clinical practice. Unfortunately, we did not assess the association between sACR and HF in these different phenotypes. In fact, the majority of patients in our study had left HF, and no cases of right HF were included. Therefore, the results of our study are only applicable to left HF and whole HF. Finally, further prospective studies conducted among different populations are needed to confirm and extend our findings.
In conclusion, we found that a low sACR was associated with poor prognosis of HF, especially in patients with no β-blocker use and normal kidney function (Scr < 97 μmol/L). Moreover, the sACR alone displayed excellent discrimination ability for mortality risk of HF, and the combined discrimination including sACR, age, gender, SBP, heart rate, and glycosylated hemoglobin could substantially improve the discrimination power. Our findings may promote the potential use of sACR in clinical practice as a preferable predictor for mortality risk of HF.
Xs.X. and Jj.L wrote the manuscript. Sy.L. designed the research. Sh.W., Xb.Z., and Xs.X. performed the research. Sh.W. and Xb.Z. analyzed the data.
The authors thank the staffs of the Division of Cardiology and follow-up nurses for their help in recruitment and follow-up of patients with heart failure.
This work was supported by the project from Panzhihua Science and Technology Bureau (No. 2020ZD-S-26), the Sichuan Medical Association Program (No. S20020), the Sichuan Province key clinical specialty construction Project, and Joint project of local universities in Yunnan Province (202101BA070001-104).
CONFLICT OF INTEREST STATEMENT
The authors declared no competing interests for this work.
INFORMED CONSENT STATEMENT
Informed consent was obtained from all subjects involved in the study.
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