Abstract
Background and objective
Prevention of atherosclerotic cardiovascular diseases (ASCVD) is possible with early recognition of individuals at risk. Salusin alpha is an endogenous bioactive peptide with anti-atherogenic properties. We aimed to reveal the relationship between salusin alpha levels and cardiovascular (CV) risk using the Systematic COronary Risk Estimation 2 (SCORE2) and Pooled Cohort Equation (PCE) algorithms.
Methods
A total of 137 asymptomatic outpatients were included in the study. The participants were divided into four quartile groups according to the distribution of salusin alpha levels: Q1 (n = 34), Q2 (n = 35), Q3 (n = 34), and Q4 (n = 34). The relationships of salusin alpha with cardiovascular risk scores and groups were investigated.
Results
The means of SCORE2 and PCE risk scores (11.24 ± 1.24 and 13.30 ± 1.71, respectively; p < 0.001 for both), baseline scores in the presence of SCORE2 and PCE optimal risk factors (4.82 ± 0.71 (p = 0.034); 4.20 ± 0.77 (p = 0.010)), and means of Δ SCORE2 and Δ PCE risk score (6.41 ± 0.67 and 9.10 ± 1.10, respectively; p < 0.001 for both) were significantly higher in the Q1 group. The SCORE2 “very high” cardiovascular (CV) risk group was significantly represented in the Q1 group (n = 17 (50%)), while the “low-moderate” risk group was more heavily represented in the Q4 group (n = 15 (44.1%)) (p < 0.001). The PCE “high” CV risk group was significantly represented in the Q1 group (n = 9 (26.5%)), while the “low” risk group was more intensely represented in the Q4 group (n = 20 (58.8%)) (p < 0.001). Salusin alpha had a significantly negative moderate correlation with SCORE2 and PCE risk scores. As a result of receiver operating characteristic (ROC) analysis, it was found that salusin alpha had significant diagnostic power in the prediction of CV risk groups determined by SCORE2 and PCE risk scores. Salusin alpha was observed to be a risk-reducing factor in SCORE2 and PCE CV risk groups (odds ratio (OR) (95% confidence interval (CI)): 0.989 (0.982-0.997) (p = 0.002) and OR (95% CI): 0.988 (0.978-0.998) (p = 0.009) respectively).
Conclusion
Salusin alpha has a negative correlation with SCORE2 and PCE risk scores. It has significant diagnostic power in the prediction of CV risk groups and is an independent variable that has a risk-reducing effect in the distribution of risk groups.
Keywords: pce, score2, ischemic heart disease, salusin alpha, cardiovascular risk
Introduction
Cardiovascular diseases (CVD), especially ischemic heart disease, are the leading causes of death worldwide [1]. Ischemia often develops as a result of occlusive atherosclerotic vascular disease. Although the incidence of atherosclerotic cardiovascular disease (ASCVD) has decreased in many European countries in recent years, it is still an important cause of mortality and morbidity [2]. The prevention of CVD before it causes serious symptoms and complications can only be possible with early recognition of individuals at risk. Various algorithms have been described so far to determine the risk of ASCVD. Among them, the algorithm known as Systematic COronary Risk Estimation 2 (SCORE2) was defined and put into use by the European Society of Cardiology (ESC) in 2021 for the primary prevention of CVD in all European countries. The SCORE2 algorithm predicts fatal and nonfatal (myocardial infarction and stroke) cardiovascular (CV) events that may develop within the next 10 years in apparently healthy individuals over 40 years of age without serious comorbidities and ASCVD. In this algorithm, cardiovascular risk is calculated using data such as age, gender, smoking habit, systolic blood pressure, and non-high-density lipoprotein (non-HDL) cholesterol level. The routine use of any biochemical parameter other than non-HDL cholesterol in the algorithm is not recommended by the ESC [2]. The Pooled Cohort Equation (PCE) was created by the American College of Cardiology and the American Heart Association in 2013 using various patient cohorts. This risk model was developed in the United States with large white and Afro-American communities. It is calculated by variables of age, gender, ethnicity, systolic and diastolic blood pressures, total cholesterol, high-density lipoprotein (HDL) and low-density lipoprotein (LDL) cholesterol, smoking, history of diabetes, and hypertension treatment [3,4].
Salusins are endogenous bioactive peptides that are synthesized in tissues such as human vascular endothelial cells, kidneys, and brains and have two forms (alpha and beta), first described by Shichiri et al. [5] in 2003. The first clinical study showing the relationship between these peptides and atherosclerosis was conducted by Watanabe et al. [6] and included patients with angiographically proven coronary artery disease (CAD) and a control group. In this study, salusin alpha level was found to be significantly lower in the group with CAD. It was shown that salusin alpha has anti-atherogenic properties, while salusin beta has pro-atherogenic effects. This opposite effect was also observed in an animal experiment by Nagashima et al. [7], in which salusins were administered exogenously. Evidence suggests a negative association between atherosclerosis and plasma salusin alpha level. In the case of atherosclerosis, while the plasma salusin alpha level decreases, on the contrary, when its plasma levels increase, it is understood that atherosclerosis is suppressed.
In this study, we tried to evaluate the relationship between cardiovascular risk level and plasma salusin alpha level in asymptomatic and apparently healthy individuals who were not previously diagnosed with ASCVD by clinical or imaging methods and did not have diabetes and serious comorbid diseases.
Materials and methods
Study design
Asymptomatic participants over the age of 40 who applied to the cardiology outpatient clinic between February 2020 and June 2021 were included in the study. The exclusion criteria were CVD (coronary artery disease, stroke, history of transient ischemic attack, and peripheral artery disease), diabetes mellitus, familial hypercholesterolemia, estimated glomerular filtration rate (eGFR) < 60 mL/minute/1.73 m², severe liver disease, malignancy, heart failure, severe heart valve disease, and active infection. A total of 137 participants were included in the study. Age, gender, smoking habit, laboratory results, and echocardiography measurements were obtained at the time of the first application. The height and weight of the participants were measured, and body mass index (BMI) was calculated by dividing the body weight by the square of the height (kg/m²). In all participants, arterial blood pressure was measured from the upper arm in the outpatient clinic with a standard cuff size aneroid sphygmomanometer (ERKA Perfect Aneroid, ERKA, Bad Tölz, Germany). The risk of ASCVD in all participants was determined as recommended by guidelines on cardiovascular disease prevention published by the ESC in 2021 [2]. SCORE2 scores corresponding to age, gender, smoking, systolic blood pressure, and non-HDL cholesterol level parameters were calculated. For this, the SCORE2 risk table was used for participants aged 40-69, and the SCORE2-Older Persons (SCORE2-OP) risk table for participants aged over 70, as recommended by the guideline. In addition, PCE risk score calculations were made from the study participants. Optimal risk scores were calculated by assuming that participants’ other risk factors were optimal based on non-modifiable risk factors. These scores were the optimal scores of the relevant age and gender without additional risk factors and were named “SCORE2 optimal” and “PCE optimal.” Deviation from optimal risk scores due to non-modifiable risk factors such as age, gender, and ethnicity was calculated using the following formulas: Δ SCORE2 = SCORE2 - “SCORE2 optimal” and Δ PCE = PCE - “PCE optimal.”
The participants were divided into four groups in 25% quartiles according to the distribution of salusin alpha levels. The group with the lowest salusin alpha level was expressed with Q1 and the highest group with Q4. Echocardiographic evaluations of the participants were performed in the left lateral position using the standard approach. Left ventricular end-diastole (LVED), left ventricular end-systole (LVES), and left atrium (LA) diameters were measured from the parasternal long axis window using the m-mode method. The ejection fraction (EF) was determined using the modified Simpson method.
This study was approved by the local ethics committee (Clinical Research Ethics Committee of Giresun University) (approval date - number: 04/09/2018 - 01/06); all patients were informed about the goals of the study, and informed consent was obtained.
Laboratory procedures
Venous blood samples were taken from all patients after 12 hours of fasting, and serum creatinine, glycated hemoglobin (HbA1c), aspartate aminotransferase (AST), alanine aminotransferase (ALT), total cholesterol (TC), triglyceride (TG), HDL, LDL, white blood cell (WBC), hemoglobin (HGB), and platelet (PLT) levels were determined. Non-HDL level was found by subtracting HDL from TC. eGFR was calculated using the Modified Diet in Renal Disease (MDRD) formula [8]. Plasma salusin alpha levels were analyzed using an enzyme-linked immunosorbent assay reagent kit (Abbkine, Inc., Wuhan, China). The detection range of the kit was 20-320 pg/mL, and the plasma samples were studied at a 1:5 dilution ratio. Plasma salusin alpha levels were expressed as pg/mL.
Statistical analysis
Analysis was performed using the Statistical Package for the Social Sciences version 21 software (IBM SPSS Statistics, Armonk, NY, USA). The normal distribution of continuous variables was evaluated using the Kolmogorov-Smirnov test. Continuous variables with normal distribution were given as mean ± standard deviation, non-normally distributed variables were given as median (25th-75th quartiles), and categorical variables were given as percent (%). The chi-square test was used for categorical variables. One-way analysis of variance (ANOVA) or the Kruskal-Wallis test was used for statistical analysis of clinical data between groups. The Pearson correlation coefficient was used for correlations, and Tukey’s test was used for post hoc analyses. The correlation between salusin alpha with SCORE2 and PCE risk scores was evaluated with a scatterplot. The optimal cutoff value of salusin alpha for predicting CV risk groups was evaluated using receiver operating characteristic (ROC) analysis. The factors affecting the distribution of CV risk groups were investigated using logistic regression analysis. Variables with a p-value of <0.05 in univariate analyzes were included in the multivariate analysis model. A p-value of <0.05 was considered statistically significant.
Results
The participants included in our study were evaluated by dividing them into four quarter groups of 25% according to the distribution of salusin alpha levels. The number of participants and salusin alpha levels of the groups were as follows: 34 participants in the Q1 group, with a mean salusin alpha level of 274.52 ± 16.44 pg/mL; 35 participants in the Q2 group, with a mean salusin alpha level of 319.73 ± 12.21 pg/mL; 34 participants in the Q3 group, with a mean salusin alpha level of 371.96 ± 16.88 pg/mL; and 34 participants in the Q4 group, with a mean salusin alpha level of 498.38 ± 64.31 pg/mL (p < 0.001). The groups were similar in terms of age, gender, and BMI. Smoking (n = 18 (52.9%)), SCORE2 mean risk score (11.24 ± 1.24), baseline score in the presence of SCORE2 optimal risk factors (4.82 ± 0.71), and mean Δ SCORE2 (6.41 ± 0.67) were significantly higher in the Q1 group (p = 0.004, p < 0.001, p = 0.034, and p < 0.001, respectively). In the distribution of SCORE2 CV risk groups, it was observed that the “very high” risk group was significantly represented in the Q1 group (n = 17 (50%)), and the “low-moderate” risk group was more intensely represented in the Q4 group (n = 15 (44.1%)) (p < 0.001). The mean PCE risk score (13.30 ± 1.71), the baseline score in the presence of PCE optimal risk factors (4.20 ± 0.77), and the mean Δ PCE (9.10 ± 1.10) were significantly higher in the Q1 group (p < 0.001, p = 0.010, and p < 0.001, respectively). In the distribution of PCE CV risk groups, it was observed that the “high” risk group was significantly represented in the Q1 group (n = 9 (26.5%)) and the “low” risk group was more intensely represented in the Q4 group (n = 20 (58.8%)) (p < 0.001). Lipid profiles were generally similar between groups. However, the numerical difference between the Q1 and Q4 groups was found to be significant in total cholesterol (Q1 and Q2 versus Q4), triglyceride, LDL (Q1 and Q2 versus Q4), and HDL and non-HDL cholesterol levels (Q1 and Q2 versus Q4). There was no significant difference between the groups in blood pressure, echocardiography, and other laboratory measurements. Details of demographic characteristics, echocardiography, and laboratory measurements are presented in Table 1.
Table 1. Demographic characteristics, echocardiography, and laboratory data of salusin alpha quartile groups.
Normally distributed values are presented as mean ± standard deviation, non-normally distributed values as median (25th-75th quartiles), and categorical values as number (percentages).
HT, hypertension; SCORE2, Systematic COronary Risk Evaluation 2; CV, cardiovascular; PCE, Pooled Cohort Equation; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HbA1c, glycated hemoglobin; eGFR, estimated glomerular filtration rate; ALT, alanine aminotransferase; AST, aspartate aminotransferase; TC, total cholesterol; TG, triglycerides; LDL, low-density lipoprotein cholesterol; HDL, high-density lipoprotein cholesterol; WBC, white blood cell; HGB, hemoglobin; PLT, platelet; LVED, left ventricular end-diastole; LVES, left ventricular end-systole; LA, left atrium; EF, ejection fraction
| Parameters | Q1 (≤298.08) (n = 34) | Q2 (298.08-338.72) (n = 35) | Q3 (338.72-402.37) (n = 34) | Q4 (>402.37) (n = 34) | p-value |
| Age (year) | 56.29 ± 10.47 | 53.97 ± 8.27 | 54.47 ± 6.72 | 52.41 ± 8.01 | 0.235 |
| Sex (male), n (%) | 20 (58.8%) | 17 (48.6%) | 16 (47.1%) | 10 (29.4%) | 0.106 |
| Current smoker, n (%) | 18 (52.9%) | 11 (31.4%) | 6 (17.6%) | 6 (17.6%) | 0.004 |
| HT, n (%) | 22 (64.7%) | 21 (60%) | 26 (76.5%) | 17 (50%) | 0.153 |
| SCORE2 | 11.24 ± 1.24 | 7.26 ± 0.78 | 7.06 ± 0.73 | 5.15 ± 0.57 | <0.001 |
| SCORE2 optimal | 4.82 ± 0.71 | 3.49 ± 0.38 | 3.32 ± 0.35 | 2.97 ± 0.34 | 0.034 |
| Δ SCORE2 | 6.41 ± 0.67 | 3.77 ± 0.55 | 3.74 ± 0.52 | 2.18 ± 0.39 | <0.001 |
| SCORE2 CV risk groups | <0.001 | ||||
| Low-moderate, n (%) | 1 (2.9%) | 11 (31.4%) | 9 (26.5%) | 15 (44.1%) | |
| High, n (%) | 16 (47.1%) | 11 (31.4%) | 16 (47.1%) | 17 (50%) | |
| Very high, n (%) | 17 (50%) | 13 (37.1%) | 9 (26.5%) | 2 (5.9%) | |
| PCE | 13.30 ± 1.71 | 8.13 ± 0.99 | 8.24 ± 1.10 | 4.91 ± 0.63 | <0.001 |
| PCE optimal | 4.20 ± 0.77 | 2.46 ± 0.28 | 2.86 ± 0.42 | 2.01 ± 0.29 | 0.010 |
| Δ PCE | 9.10 ± 1.10 | 5.66 ± 0.82 | 5.37 ± 0.83 | 2.89 ± 0.48 | <0.001 |
| PCE CV risk groups | <0.001 | ||||
| Low, n (%) | 6 (17.6%) | 16 (45.7%) | 13 (38.2%) | 20 (58.8%) | |
| Borderline, n (%) | 10 (29.4%) | 3 (8.6%) | 3 (8.8%) | 9 (26.5%) | |
| Intermediate, n (%) | 9 (26.5%) | 16 (45.7%) | 16 (47.1%) | 5 (14.7%) | |
| High, n (%) | 9 (26.5%) | 0 | 2 (5.9%) | 0 | |
| Salusin alpha (pg/mL) | 274.52 ± 16.44 | 319.73 ± 12.21 | 371.96 ± 16.88 | 498.38 ± 64.31 | <0.001 |
| BMI (kg/m2) | 28.61 ± 3.69 | 28.36 ± 4.66 | 29.10 ± 3.90 | 28.46 ± 4.22 | 0.878 |
| SBP (mmHg) | 143.74 ± 17.15 | 140.09 ± 18.35 | 147.38 ± 16.92 | 139.09 ± 16.93 | 0.202 |
| DBP (mmHg) | 84.35 ± 12.55 | 85.43 ± 12.84 | 86.35 ± 11.89 | 82.41 ± 12.23 | 0.554 |
| HbA1c (%) | 5.47 ± 0.47 | 5.59 ± 0.34 | 5.63 ± 0.38 | 5.49 ± 0.42 | 0.626 |
| Creatine (mg/dL) | 0.81 ± 0.15 | 0.79 ± 0.13 | 0.78 ± 0.19 | 0.74 ± 0.14 | 0.254 |
| eGFR(mL/minute/1.73 m2) | 93.31 ± 15.03 | 94.76 ± 13.05 | 93.98 ± 14.35 | 96.20 ± 12.97 | 0.826 |
| ALT (IU/L) | 22.68 (17.66-27.70) | 21.69 (17.84-25.53) | 23.32 (16.65-30) | 22.53 (18.43-26.62) | 0.992 |
| AST (IU/L) | 19.91 ± 5.93 | 21.63 ± 6.73 | 21.88 ± 12.43 | 21.26 ± 6.79 | 0.767 |
| TC (mg/dL) | 207.03 ± 32.72 | 207.66 ± 33.28 | 201.71 ± 49.14 | 190.24 ± 36.86 | 0.006 |
| TG (mg/dL) | 171.88 (131.93-225.81) | 168.86 (128.21-214.52) | 164.35 (121.09-219.43) | 161.44 (123.23-215.12) | 0.002 |
| LDL (mg/dL) | 123.18 ± 30.36 | 123.80 ± 28.70 | 117.76 ± 40.60 | 114.97 ± 34.95 | 0.004 |
| HDL (mg/dL) | 44.76 ± 11.64 | 46.49 ± 9.87 | 48.12 ± 12.16 | 50.76 ± 15 | 0.076 |
| Non-HDL (mg/dL) | 162.26 ± 32.36 | 161.17 ± 32.07 | 152.68 ± 46.12 | 139.47 ± 35.52 | 0.024 |
| WBC (103/mm3) | 7.27 (6.34-8.20) | 7.05 (6.31-7.79) | 6.78 (6.27-7.28) | 6.82 (6.15-7.50) | 0.767 |
| HGB (g/dL) | 14.48 ± 1.61 | 14.44 ± 1.47 | 13.98 ± 1.81 | 14.05 ± 1.46 | 0.566 |
| PLT (103/mm3) | 236.02 ± 47.06 | 241.73 ± 60.39 | 252.08 ± 61.06 | 247.35 ± 58.83 | 0.654 |
| LVED diameter (mm) | 47.38 ± 4.39 | 47.34 ± 4.02 | 47.12 ± 4.21 | 45.21 ± 3.16 | 0.114 |
| LVES diameter (mm) | 27.71 ± 3.60 | 27.54 ± 3.92 | 27.41 ± 4.25 | 27.06 ± 3.82 | 0.903 |
| LA diameter (mm) | 34.74 ± 5.16 | 35.06 ± 4.54 | 33.09 ± 5.01 | 33.82 ± 5.34 | 0.366 |
| EF (%) | 64.71 ± 1.19 | 64.71 ± 1.17 | 64.71 ± 1.19 | 64.85 ± 0.85 | 0.948 |
Salusin alpha was found to have a significant negative correlation with age (r: -0.217; p = 0.011), smoking (r: -0272; p = 0.001), and non-HDL cholesterol (r: -0.176; p = 0.040). It was found that SCORE2 and PCE risk scores were strongly correlated (r: 0.917; p < 0.001) and had a similarly significant correlation with optimal baseline score measurements, Δ measurements, and risk groups. Salusin alpha had a moderately significant negative correlation with SCORE2 and PCE risk scores. It was found that this relationship was similarly significant in optimal baseline measurements, Δ measurements, and risk groups. Correlation analysis results are presented in Table 2.
Table 2. Correlation of salusin alpha, and SCORE2 and PCE risk scores with other variables.
SCORE2, Systematic COronary Risk Evaluation 2; PCE, Pooled Cohort Equation; HT, hypertension; CV, cardiovascular; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HbA1c, glycated hemoglobin; TC, total cholesterol; TG, triglycerides; LDL, low-density lipoprotein cholesterol; HDL, high-density lipoprotein cholesterol
| Parameters | Salusin alpha | SCORE2 | PCE | |||
| r | p-value | r | p-value | r | p-value | |
| Age (year) | -0.217 | 0.011 | 0.770 | <0001 | 0.638 | <0.001 |
| Sex (male), n (%) | 0.163 | 0.057 | -0.077 | 0.373 | -0.264 | 0.002 |
| Current smoker, n (%) | -0.272 | 0.001 | 0.289 | 0.001 | 0.328 | <0.001 |
| HT, n (%) | -0.138 | 0.107 | 0.342 | <0.001 | 0.375 | <0.001 |
| SCORE2 | -0.395 | <0001 | - | - | 0.917 | <0.001 |
| SCORE2 optimal | -0.245 | 0004 | 0.832 | <0.001 | 0.720 | <0.001 |
| Δ SCORE2 | -0.420 | <0.001 | 0.894 | <0.001 | 0.854 | <0.001 |
| SCORE2 CV risk groups | -0.441 | <0.001 | 0.826 | <0.001 | 0.791 | <0.001 |
| PCE | -0.374 | <0.001 | 0.917 | <0.001 | - | - |
| PCE optimal | -0.237 | 0.005 | 0.804 | <0.001 | 0.811 | <0.001 |
| Δ PCE | -0.391 | <0.001 | 0.837 | <0.001 | 0.948 | <0.001 |
| PCE CV risk groups | -0.363 | <0.001 | 0.844 | <0.001 | 0.878 | <0.001 |
| BMI (kg/m2) | 0.009 | 0.915 | 0.088 | 0.306 | 0.072 | 0.401 |
| SBP (mmHg) | -0.095 | 0.269 | 0.351 | <0.001 | 0.328 | <0.001 |
| DBP (mmHg) | -0.075 | 0.386 | 0.016 | 0.856 | 0.044 | 0.606 |
| HbA1c (%) | -0.057 | 0.507 | 0.049 | 0.569 | 0.042 | 0.623 |
| Creatine (mg/dL) | -0.100 | 0.247 | 0.217 | 0.011 | 0.355 | <0.001 |
| TC (mg/dL) | -0.137 | 0.111 | 0.127 | 0.138 | 0.098 | 0.255 |
| TG (mg/dL) | -0.142 | 0.097 | 0.031 | 0.715 | 0.129 | 0.134 |
| LDL (mg/dL) | -0.067 | 0.435 | 0.116 | 0.177 | 0.097 | 0.261 |
| HDL (mg/dL) | 0.119 | 0.165 | -0.008 | 0.925 | -0.207 | 0.015 |
| Non-HDL (mg/dL) | -0.176 | 0.040 | 0.136 | 0.112 | 0.170 | 0.047 |
Δ SCORE2 and Δ PCE scores obtained by the difference of the risk scores obtained with the participants’ current risk factors (SCORE2 and PCE) and the risk scores obtained by assuming other risk factors based on non-modifiable risk factors at optimal limits (SCORE2 optimal and PCE optimal) were compared with salusin alpha. It was observed that there was a higher level of negative correlation. Relevant correlation relationships are presented in Figure 1 with a scatterplot.
Figure 1. Scatterplot of the relationship between salusin alpha and SCORE2 and PCE risk scores.
(A) SCORE2-salusin alpha relationship, (B) Δ SCORE2-salusin alpha relationship, (C) PCE-salusin alpha relationship, and (D) Δ PCE-salusin alpha relationship.
SCORE2, Systematic COronary Risk Evaluation 2; PCE, Pooled Cohort Equation
As a result of ROC analysis, it was found that salusin alpha had significant diagnostic power in the prediction of CV risk groups determined by SCORE2 and PCE risk scores. The strongest diagnostic power was found to be predictive of PCE high CV risk group with a cutoff value of <296.41 with 81% sensitivity and 81% specificity (area under the curve (AUC) (95% confidence interval (CI)): 0.822 (0.711-0.933); p < 0.001). ROC analysis details are presented in Table 3.
Table 3. Diagnostic importance of salusin alpha in the prediction of risk groups obtained by risk scores (ROC analysis details).
ROC, receiver operating characteristic; AUC, area under the curve; CI, confidence interval; PCE, Pooled Cohort Equation; CV, cardiovascular; SCORE2, Systematic COronary Risk Evaluation 2
| AUC | 95% CI | Sensitivity | Specificity | Cutoff | p-value | |
| PCE high CV risk | 0.822 | 0.711-0.933 | 81% | 81% | <296.41 | <0.001 |
| PCE low CV risk | 0.667 | 0.574-0.759 | 85% | 45% | >313.20 | 0.001 |
| SCORE2 very high risk | 0.732 | 0.646-0.817 | 70% | 65% | <332.79 | <0.001 |
| SCORE2 low-moderate risk | 0.719 | 0.624-0.813 | 91% | 41% | >313.20 | <0.001 |
According to the results of the multivariate logistic regression analysis, salusin alpha, systolic blood pressure, smoking, gender, and age were determined as factors affecting SCORE2 CV risk group distribution. Salusin alpha was observed to be a risk-reducing factor in the SCORE2 CV risk grouping (odds ratio (OR) (95% CI): 0.989 (0.982-0.997); p = 0.002). Logistic regression analysis details for factors affecting the SCORE2 CV risk group distribution are presented in Table 4.
Table 4. Logistic regression analysis results of factors affecting CV risk groups determined by SCORE2.
CV, cardiovascular; SCORE2, Systematic COronary Risk Evaluation 2; OR, odds ratio; CI, confidence interval; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; TG, triglycerides; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol; BMI, body mass index
| SCORE2: low-moderate versus high-very high | ||||
| Univariate | Multivariate | |||
| OR (95% CI) | p-value | OR (95% CI) | p-value | |
| Salusin alpha | 0.991 (0.986-0.995) | <0.001 | 0.989 (0.982-0.997) | 0.002 |
| SBP | 1.060 (1.031-1.090) | <0.001 | 1.145 (1.072-1.224) | <0.001 |
| DBP | 1.034 (1.001-1.068) | 0.083 | ||
| TC | 1.004 (0.994-1.014) | 0.488 | ||
| HDL | 0.987 (0.957-1.017) | 0.378 | ||
| LDL | 1.004 (0.992-1.015) | 0.532 | ||
| Non-HDL | 1.006 (0.996-1.016) | 0.272 | ||
| Current smoke | 10.69 (2.431-47.03) | 0.002 | 12.90 (2.169-76.83) | 0.001 |
| Gender | 0.415 (0.185-0.931) | 0.033 | 0.026 (0.003-0.214) | 0.001 |
| Age | 1.126 (1.063-1.193) | <0.001 | 1.412 (1.205-1.655) | <0.001 |
| BMI | 1.006 (0.917-1.105) | 0.896 | ||
According to the results of the multivariate logistic regression analysis, salusin alpha, systolic blood pressure, HDL cholesterol level, smoking, gender, and age were determined as factors affecting PCE CV risk group distribution. Salusin alpha was observed to be a risk-reducing factor in PCE CV risk grouping (OR (95% CI): 0.988 (0.978-0.998); p = 0.009). Logistic regression analysis details for the factors affecting the PCE CV risk group distribution are presented in Table 5.
Table 5. Logistic regression analysis results of factors affecting CV risk groups determined by PCE.
CV, cardiovascular; PCE, Pooled Cohort Equation; OR, odds ratio; CI, confidence interval; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; TG, triglycerides; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol; BMI, body mass index
| PCE: low-borderline versus intermediate-high | ||||
| Univariate | Multivariate | |||
| OR (95% CI) | p-value | OR (95% CI) | p-value | |
| Salusin alpha | 0.991 (0.987-0.996) | 0.001 | 0.988 (0.978-0.998) | 0.009 |
| SBP | 1.049 (1.025-1.074) | <0.001 | 1.172 (1.084-1.267) | <0.001 |
| DBP | 1.015 (0.987-1.043) | 0.302 | ||
| TC | 1.003 (0.994-1.012) | 0.496 | ||
| HDL | 0.971 (0.942-0.998) | 0.033 | 0.864 (0.774-0.964) | 0.009 |
| LDL | 1.002 (0.992-1.013) | 0.638 | ||
| Non-HDL | 1.006 (0.997-1.015) | 0.170 | ||
| Current smoke | 3.634 (1.689-7.819) | 0.001 | 7. 170 (3.248-15.82) | <0.001 |
| Gender | 0.436 (0.218-0.873) | 0.019 | 0.009 (0.001-0.107) | <0.001 |
| Age | 1.206 (1.129-1.289) | <0.001 | 1.761 (1.391-2.230) | <0.001 |
| BMI | 1.034 (0.951-1.123) | 0.432 | ||
Discussion
Our aim in our study was to determine alternative methods and variables to increase the effectiveness and diagnostic power of conventional CV risk classification systems in the healthy population. The results we have obtained can bring a different perspective to the subject and prepare the ground for other studies that can be applied in the future. In our study, we found a significant negative correlation between plasma salusin alpha level and SCORE2 and PCE scores. Salusin alpha has significant diagnostic power in the prediction of CV risk groups. It is an independent variable that has a risk-reducing effect in the distribution of CV risk groups. Salusin alpha is associated not only with CV risk scores but also with participants’ scores for deviation from baseline risk status (Δ SCORE2 and Δ PCE) based on age, sex, and ethnicity. This negative correlation has a stronger correlation than standard scores.
There are many studies in the literature showing that salusins are associated with atherosclerosis. In a study involving patients who underwent coronary angiography for chronic coronary syndrome, plasma salusin alpha level was found to be negatively correlated with the presence and severity of CAD. In this study, the authors emphasized that salusin alpha may be a potential biomarker in determining the risk of future CAD [9]. In two other studies including patients with ST-elevation myocardial infarction [10] and patients with carotid atherosclerosis [11], it was shown that plasma salusin alpha levels decreased in these patient populations. In animal experiments, there are studies showing that atherosclerosis is suppressed in animals treated with exogenous salusin alpha infusion [7,12,13]. These studies showing the relationship of salusins with atherosclerosis include patient groups with obvious clinical findings related to atherosclerosis. There are insufficient data to evaluate the role of salusin alpha in CV risk determination in healthy individuals without clinical signs of atherosclerosis.
There are also studies showing the relationship between plasma salusin levels and atherosclerotic risk factors. In a study conducted on patients undergoing hemodialysis due to end-stage renal disease, it was found that a lipid-lowering diet, increase in physical activity, and atorvastatin treatment caused a decrease in LDL cholesterol and an increase in HDL/LDL ratio by increasing the level of salusin alpha in the plasma [14]. In a study involving type 2 diabetes patients, a negative correlation was found between plasma salusin alpha level and LDL cholesterol level [15]. Similar effects have been demonstrated in animal studies. Salusin alpha administered to mice with apolipoprotein E deficiency for eight weeks caused a serious decrease in total cholesterol serum level and an increase in HDL cholesterol level [7]. In another study, it was found that salusin alpha given in high doses for 12 weeks caused a decrease in total cholesterol, triglyceride, and LDL cholesterol levels and an increase in HDL cholesterol levels [12]. In both studies, it was observed that salusin alpha prevented the progression of atherosclerosis by causing an improvement in the serum lipid profile. It has been shown that the suppression of atherosclerosis results from the inhibition of foam cell formation from macrophages through the downregulation of the acetyl-coenzyme A acetyltransferase-1 (ACAT-1) enzyme [6]. The result of all these studies is that the increase in plasma salusin alpha level positively affects the lipid profile. In our study, in parallel with the previous literature, we found that participants with high plasma salusin alpha levels had low total cholesterol, triglyceride, and LDL and non-HDL levels. Non-HDL levels indicate all atherogenic lipoproteins with apo-B content in plasma [16]. The role of LDL cholesterol and other apo-B-containing lipoproteins in atherosclerotic processes is well known [17]. The absolute decrease in LDL cholesterol, independent of drugs, leads to a decrease in the risk of cardiovascular disease [18].
In our study, there was also a statistically significant correlation between smoking and serum salusin alpha levels. Salusin alpha levels were found to be significantly lower in smokers. We did not find any data on this relationship in the literature before. In the study by Nakayama et al. [19], it was found that Jak-2 (Janus kinases), which can be upregulated with stimuli such as angiotensin II, inflammatory cytokines, and atherogenic growth factors, suppresses the expression of salusin alpha. It has been emphasized that this mechanism is the possible reason for the decrease in serum salusin alpha levels in atherosclerosis. It has been previously shown that the Jak-2 V617F gene mutation is more pronounced and frequent in smokers compared to nonsmokers [20]. This mutation disrupts the autoinhibition of Jak-2, leading to the constitutive activation of the Jak-2 signaling pathway. The decrease in salusin levels seen in smoking participants may be due to the Jak-2 V617F gene mutation that occurs with smoking.
There are no studies evaluating the relationship between CVD risk and salusins in previously asymptomatic patients. To the best of our knowledge, our study is the first to evaluate plasma salusin alpha levels in asymptomatic individuals whose cardiovascular risk is determined using the SCORE2 and PCE algorithms. In our study, we found that SCORE2, PCE scores, and CV risk groups had a negative significant correlation with plasma salusin alpha levels. The relationship of this peptide, which has anti-atherogenic activity, with CV risk scores would contribute to the production of modified score systems that could be evaluated with clinical outcomes in the future. Salusin alpha is associated not only with CV risk scores but also with participants’ scores for deviation from baseline risk status (Δ SCORE2 and Δ PCE) based on age, sex, and ethnicity. This negative correlation has a stronger correlation than standard scores. This result can be interpreted that salusin alpha can also be used in the follow-up of CV risk reduction with lifestyle changes or in determining the severity of the calculated CV risk.
Current guidelines do not recommend the use of any biochemical parameter other than non-HDL cholesterol for use in the SCORE2 and PCE algorithm [2,3]. There are previous studies conducted on patients with some biochemical parameters and SCORE risk groups. In a study including non-diabetic asymptomatic patients, it was found that HbA1c indicates the prevalence of subclinical atherosclerosis in patients in the low-intermediate risk group according to the SCORE index [21]. In another study, it was shown that serum nitric oxide (NO) level has a significant negative correlation with systolic and diastolic blood pressure in asymptomatic patients who are in the low cardiovascular risk group according to the SCORE score. It was emphasized that the increase in serum NO in this group indicates a decrease in blood pressure. In the study, while NO levels did not show a significant difference in the group with and without CAD, endothelin levels were found to be significantly higher in men with CAD than in those without CAD [22]. In another study involving liver transplant candidates, asymmetric dimethylarginine (ADMA) and NO levels were classified according to the SCORE index, and it was shown that they were significantly higher in the group with high CV risk. In the study, it was emphasized that the increase in ADMA and NO levels was independently associated with the increase in CV risk [23]. The authors, who integrated the PCE CV risk scoring system with isolated post-challenge hyperglycemia in postmenopausal women, created the opportunity to perform a more sensitive risk analysis on the participants with the modified assessment system they obtained [24]. Our study provides valuable information to express the diagnostic effects of biomarkers in CV risk classification. The decrease in salusin alpha plasma levels appears to lead to an increased risk of CVD. The fact that exogenous salusin alpha suppressed atherosclerosis in animal experiments also shows how important our findings are. In the coming years, it is clear that more comprehensive studies are needed to comment on whether salusin alpha can be an effective biochemical marker in CV risk assessment processes and whether exogenous administration, among other risk measures, will reduce the risk in patients with a high risk of CV event development.
Limitations
The most obvious limitation is that the study was single-centered with single ethnicity, and the number of participants was relatively low. In addition, our study was carried out in an ethnicity considered a high-risk group in the SCORE2 algorithm. Whether there is underlying subclinical atherosclerosis in the patient population has not been revealed by imaging methods. The study includes hypertensive patients, and some of them use antihypertensive drugs. The cardiovascular risk level is lower in patients whose blood pressure is under control. It is not clear whether antihypertensive drugs have a direct effect on salusin alpha levels in the low CV risk group.
Conclusions
In conclusion, in our study, we found that salusin alpha has a negative correlation with SCORE2 and PCE risk scores. It has significant diagnostic power in the prediction of CV risk groups. Salusin alpha is an independent variable that has a risk-reducing effect in the distribution of risk groups. It was also found that salusin alpha had a negative and significant relationship with the severity and prevalence of CV risk factors.
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Funding Statement
This research was supported by the Coordinatorship of Scientific Research Projects of Giresun University (project number: SAĞ-BAP-A-150219-46).
The authors have declared that no competing interests exist.
Human Ethics
Consent was obtained or waived by all participants in this study. The Clinical Research Ethics Committee of Giresun University issued approval 04/09/2018 - 01/06
Animal Ethics
Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue.
References
- 1.Heart disease and stroke statistics-2022 update: a report from the American Heart Association. Tsao CW, Aday AW, Almarzooq ZI, et al. Circulation. 2022;145:0–639. doi: 10.1161/CIR.0000000000001052. [DOI] [PubMed] [Google Scholar]
- 2.2021 ESC Guidelines on cardiovascular disease prevention in clinical practice. Visseren FL, Mach F, Smulders YM, et al. Eur Heart J. 2021;42:3227–3337. doi: 10.1093/eurheartj/ehab484. [DOI] [PubMed] [Google Scholar]
- 3.2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Goff DC Jr, Lloyd-Jones DM, Bennett G, et al. J Am Coll Cardiol. 2014;63:2935–2959. doi: 10.1016/j.jacc.2013.11.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.2019 ACC/AHA guideline on the primary prevention of cardiovascular disease: executive summary: a report of the American College of Cardiology/American Heart Association Task Force on clinical practice guidelines. Arnett DK, Blumenthal RS, Albert MA, et al. J Am Coll Cardiol. 2019;74:1376–1414. doi: 10.1016/j.jacc.2019.03.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Salusins: newly identified bioactive peptides with hemodynamic and mitogenic activities. Shichiri M, Ishimaru S, Ota T, Nishikawa T, Isogai T, Hirata Y. Nat Med. 2003;9:1166–1172. doi: 10.1038/nm913. [DOI] [PubMed] [Google Scholar]
- 6.Impact of salusin-alpha and -beta on human macrophage foam cell formation and coronary atherosclerosis. Watanabe T, Nishio K, Kanome T, et al. Circulation. 2008;117:638–648. doi: 10.1161/CIRCULATIONAHA.107.712539. [DOI] [PubMed] [Google Scholar]
- 7.Chronic infusion of salusin-alpha and -beta exerts opposite effects on atherosclerotic lesion development in apolipoprotein E-deficient mice. Nagashima M, Watanabe T, Shiraishi Y, et al. Atherosclerosis. 2010;212:70–77. doi: 10.1016/j.atherosclerosis.2010.04.027. [DOI] [PubMed] [Google Scholar]
- 8.Assessing kidney function--measured and estimated glomerular filtration rate. Stevens LA, Coresh J, Greene T, Levey AS. N Engl J Med. 2006;354:2473–2483. doi: 10.1056/NEJMra054415. [DOI] [PubMed] [Google Scholar]
- 9.Serum salusin-α levels are inversely correlated with the presence and severity of coronary artery disease. Du SL, Wang WJ, Wan J, Wang YG, Wang ZK, Zhang Z. Scand J Clin Lab Invest. 2013;73:339–343. doi: 10.3109/00365513.2013.783227. [DOI] [PubMed] [Google Scholar]
- 10.Serum salusin levels in ST-segment elevation myocardial infarction. Yilmaz M, Atescelik M, Ardic S, et al. Clin Lab. 2016;62:1717–1723. doi: 10.7754/Clin.Lab.2016.160136. [DOI] [PubMed] [Google Scholar]
- 11.Serum salusin-alpha levels are decreased and correlated negatively with carotid atherosclerosis in essential hypertensive patients. Watanabe T, Suguro T, Sato K, et al. Hypertens Res. 2008;31:463–468. doi: 10.1291/hypres.31.463. [DOI] [PubMed] [Google Scholar]
- 12.Salusin-α attenuates hepatic steatosis and atherosclerosis in high fat diet-fed low density lipoprotein receptor deficient mice. Tang K, Wang F, Zeng Y, Chen X, Xu X. Eur J Pharmacol. 2018;830:76–86. doi: 10.1016/j.ejphar.2018.04.026. [DOI] [PubMed] [Google Scholar]
- 13.Salusin-α inhibits proliferation and migration of vascular smooth muscle cell via AKT/mTOR signaling. Gao S, Xu L, Zhang Y, et al. Cell Physiol Biochem. 2018;50:1740–1753. doi: 10.1159/000494792. [DOI] [PubMed] [Google Scholar]
- 14.Effect of lifestyle changes and atorvastatin administration on dyslipidemia in hemodialysis patients: a prospective study. Grzegorzewska AE, Niepolski L, Sikora J, Janków M, Jagodziński PP, Sowińska A. Pol Arch Med Wewn. 2014;124:443–451. doi: 10.20452/pamw.2401. [DOI] [PubMed] [Google Scholar]
- 15.Evaluation of salusin-α and salusin-β levels in patients with type 2 diabetes mellitus and determination of the impact of severity of hyperglycemia on salusin levels. Argun D, Argun F, Borku Uysal B. Ir J Med Sci. 2021;190:1403–1411. doi: 10.1007/s11845-021-02674-4. [DOI] [PubMed] [Google Scholar]
- 16.Trajectories of non-HDL cholesterol across midlife: implications for cardiovascular prevention. Pencina KM, Thanassoulis G, Wilkins JT, et al. J Am Coll Cardiol. 2019;74:70–79. doi: 10.1016/j.jacc.2019.04.047. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement from the European Atherosclerosis Society Consensus Panel. Ference BA, Ginsberg HN, Graham I, et al. Eur Heart J. 2017;38:2459–2472. doi: 10.1093/eurheartj/ehx144. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Efficacy and safety of more intensive lowering of LDL cholesterol: a meta-analysis of data from 170,000 participants in 26 randomised trials. Baigent C, Blackwell L, Emberson J, et al. Lancet. 2010;376:1670–1681. doi: 10.1016/S0140-6736(10)61350-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Expression of prosalusin in human neuroblastoma cells. Nakayama C, Shichiri M, Sato K, Hirata Y. Peptides. 2009;30:1362–1367. doi: 10.1016/j.peptides.2009.03.021. [DOI] [PubMed] [Google Scholar]
- 20.Janus kinase V617F mutation in cigarette smokers. Weinberg I, Borohovitz A, Krichevsky S, Perlman R, Ben-Yehuda A, Ben-Yehuda D. Am J Hematol. 2012;87:5–8. doi: 10.1002/ajh.22180. [DOI] [PubMed] [Google Scholar]
- 21.Glycated hemoglobin and subclinical atherosclerosis in people without diabetes. Rossello X, Raposeiras-Roubin S, Oliva B, et al. J Am Coll Cardiol. 2021;77:2777–2791. doi: 10.1016/j.jacc.2021.03.335. [DOI] [PubMed] [Google Scholar]
- 22.Associations of endothelial biomarkers, nitric oxide metabolites and endothelin, with blood pressure and coronary lesions depend on cardiovascular risk and sex to mark endothelial dysfunction on the SCORE scale. Gumanova NG, Gorshkov AU, Klimushina MV, Kots AY. Horm Mol Biol Clin Investig. 2020;41 doi: 10.1515/hmbci-2020-0024. [DOI] [PubMed] [Google Scholar]
- 23.Association of asymmetric dimethylarginine and nitric oxide with cardiovascular risk in patients with end-stage liver disease. Dragičević M, Košuta I, Kruezi E, Lovrenčić MV, Mrzljak A. Medicina (Kaunas) 2020;56 doi: 10.3390/medicina56110622. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Ten-year atherosclerotic cardiovascular disease risk prediction for postmenopausal women: Impacts of isolated postchallenge hyperglycemia. Wu TH, Lin YC, Hwu CM. Medicine (Baltimore) 2022;101:0. doi: 10.1097/MD.0000000000030352. [DOI] [PMC free article] [PubMed] [Google Scholar]