Abstract
Background
Endothelial Activation and Stress Index (EASIX), derived from measurements of platelet count, creatinine, and lactate dehydrogenase, can serve as an indicator for systemic inflammation and endothelial dysfunction. However, the association between EASIX and the prevalence of cardiovascular disease (CVD) in the general American adults has yet to be explored. This study aims to explore this potential association.
Methods
In this cross-sectional study, participants from NHANES 1999–2018 were analyzed. To examine the association, multivariable logistic regression analyses and restricted cubic spline methods were conducted, supplemented by subgroup and interaction analyses. Statistical analyses were performed with R software (Version 4.1.3).
Results
This study comprised 38,794 subjects, with a prevalence of CVD at 10.4%. The median age was 49 years (range, 34–64), and 19866 (51.2%) were female. After adjusting for all covariates, elevated EASIX levels were associated with an increased prevalence of CVD [adjusted odds ratios (aOR) = 1.33, 95% CI: 1.26, 1.41], myocardial infarction (aOR = 1.16, 95% CI: 1.10, 1.23), angina pectoris (aOR = 1.15, 95% CI: 1.07, 1.23), congestive heart failure (aOR = 1.29, 95% CI: 1.22, 1.37), coronary heart disease (aOR = 1.23, 95% CI: 1.16, 1.31), and stroke (aOR = 1.16, 95% CI: 1.09, 1.23). A notable J-shaped association was observed, with a significant inflection point at 82.5, which was more pronounced in older subjects and those with hyperlipidemia. Furthermore, EASIX demonstrates the superior predictive capability for CVD, compared to other individual indices (AUC = 0.700, 95% CI: 0.691, 0.709).
Conclusion
An elevated EASIX is associated with a greater prevalence of CVD in American adults. EASIX exhibited better performance in evaluating the associations compared to other individual indices. It is expected that EASIX can become a more effective metric for identifying populations at an early risk of CVD.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12872-025-05131-7.
Keywords: EASIX, CVD, Inflammation, Creatinine, Albumin
Introduction
Cardiovascular diseases (CVD) persist as a leading cause of mortality worldwide, posing significant threats to healthy life expectancies (defined as years lived free of disability or disease) and escalating healthcare costs, thereby constituting a major public health challenge [1]. The mortality and prevalence rates associated with CVD have shown a consistent upward trend [2]. Specifically, CVD-related deaths increased from 12.1 million in 1990 to 18.6 million in 2019, while the number of individuals affected by CVD rising from 271 million in 1990 to 523 million in 2019 [2]. In light of these rising figures, it is imperative to explore the underlying causes of CVD and to develop preventive strategies aiming at delaying its onset and reducing its incidence.
Recently, there has been a growing focus on the contribution of microvascular endothelial injury to CVD [3]. Endothelial cells are crucial to the pathophysiology of CVD, as they are central in preserving vascular homeostasis [4]. Dysfunction in these cells can result in impaired vasodilation, thrombosis, and inflammation, potentially exacerbating endothelial damage [5]. Endothelial dysfunction reduces nitric oxide bioavailability, provoking vasoconstriction and platelet activation. Up-regulated ICAM-1, VCAM-1, and E-selectin promote leukocyte adhesion and transmigration, amplifying inflammation. Concurrently, tissue-factor exposure and cytokine release (TNF-α, IL-6) trigger thrombosis and oxidative stress, establishing a vicious cycle of further endothelial injury and cardiovascular risk [6, 7].
To quantify systemic endothelial impairment conveniently, EASIX, a non-invasive quantitative biomarker calculated from readily available serum measurements of creatinine, lactate dehydrogenase (LDH), and platelet count (PLT)—was originally developed to assess the severity of endotheliopathy in patients undergoing stem-cell transplantation [8]. Prior research has demonstrated that elevated EASIX is associated with a heightened risk of mortality in patients following allogeneic transplantation of stem cells, a condition associated with thrombotic microangiopathy due to endothelial dysfunction [9, 10]. LDH, a constituent of EASIX, is released to platelets, leukocytes, and endothelial cells, when vascular endothelial integrity is compromised, resulting in increased levels [11]. In patients exhibiting elevated EASIX levels, indicative of severe endothelial damage, increased creatinine levels may suggest an association between renal function impairment and endothelial damage. Concurrently, reduced platelet levels may result from complement activation and endothelial damage [12]. The exposure to collagen, along with elevated levels of tissue factors and von Willebrand factor due to endothelial injury, facilitates platelet over- aggregation and activation [13]. Recent studies have increasingly recognized EASIX as a potential biomarker for patients with severe liver diseases [14], sepsis [15], traumatic brain injury [16], or acute pancreatitis [17]. This development has prompted researchers to suggest that EASIX can function as a comprehensive indicator of endothelial cell dysfunction. Importantly, empirical studies have revealed a significant association between increased EASIX levels and adverse outcomes in individuals with coronary artery disease [18], including chronic heart failure (CHF) [19], and acute myocardial infarction [20].
Nonetheless, the association between EASIX, as an economical biomarker, and the prevalence of CVD in general adults has yet to be explored. Considering this context, this study aims to explore the association between EASIX and the risk of CVD. It is anticipated that the findings of this study will aid in the early identification of high-risk populations and offer valuable insights for the management of CVD.
Methods
Data sources
Cross-sectional data were provided by National Health and Nutrition Examination Survey (NHANES) administered by National Center for Health Statistics (NCHS) to evaluate the physical and nutritional health of the non-institutionalized American population [21, 22]. Although the data from NHANES can be updated continuously within its biennial cycle, the study was designed through a stratified, multi-stage probabilistic sampling method to ensure a relatively high degree of representativeness among enrolled participants. All survey methodologies and protocols employed in NHANES have received approval from the NCHS ethical review committee.
Research subjects
Continuous data were extracted from NHANES 1999–2018, collected between March 1, 1999, and December 31, 2018, to form initial samples. The datasets generated and analyzed in this study can be readily accessible on the official NHANES website at https://www.cdc.gov/nchs/nhanes/index.html. These survey cycles furnished the comprehensive information on EASIX and various CVD conditions, including CVD, CHF, coronary heart disease (CHD), heart attack, stroke, and angina. Initially, the study cohort comprised 101,316 subjects. Following exclusions for individuals under the age of 18 years old (n = 42,112) and those with missing data on EASIX (n = 6,882), incomplete CVD evaluation data (n = 4,100), and missing covariates data (n = 9,428), the final analysis included 38,794 subjects (see Fig. 1).
Fig. 1.
Flowchart of the sample selection from the 1999–2018 NHANES
Definition of EASIX and CVD
EASIX was detected with the formula: Creatinine (mg/dL) × LDH (U/L)/PLT (10^9/L) [23].
The definition of CVD was derived from responses to the questionnaire completed by subjects. CVD was identified if a participant reported experiencing myocardial infarction, angina pectoris, CHD, stroke, or CHF [24]. Therefore, any affirmative response to these specific criteria was considered indicative of the presence of CVD [25].
Selection of covariates
In this analysis, a range of demographic variables was accounted for, including age, race, gender, poverty income ratio (PIR), marital status, and education level. Furthermore, several anthropometric and laboratory variables were incorporated and these include: alcohol abuse, creatinine, smoking status, c, albumin, aspartate aminotransferase (AST), uric acid, and alanine aminotransferase (ALT). Additionally, health status disparities, including diabetes, hyperlipidemia, and hypertension, were considered as potential confounders in this study.
Statistical analysis
Continuous variables were initially assessed for normality. Normally distributed data are presented as mean ± standard deviation (SD), while non-normally distributed data are expressed as median and interquartile range (IQR). Subjects were stratified according to EASIX tertiles (T1: < 0.6; T2: 0.36–0.54; T3: ≥ 0.54), and intergroup differences were assessed with either chi-square test or t-test. To explore the association between EASIX and CVD, three separate multivariable logistic regression models were employed. Model 1 was not adjusted for any covariates. Model 2 included adjustments for gender, age, and race. In Model 3, a comprehensive set of covariates was considered, including age, gender, race, BMI, physical activities, diabetes, education level, smoking status, PIR, hyperlipidemia, marital status, ALT, albumin, hypertension, alcohol abuse, AST, creatinine, and uric acid levels.
To address the non-linear association, restricted cubic spline (RCS) analysis was employed. The association was further examined through subgroup analyses with stratified multivariable logistic regression models. Furthermore, the predictive capabilities of EASIX and other individual markers, including LDH, creatinine, and PLT, for CVD, angina pectoris, CHF, myocardial infarction, CHD, and stroke, were evaluated through ROC curves by comparing the AUC values. Statistical analyses were performed through R version (Version 4.1.3, https://www.r-project.org/), with statistical significance determined at a two-tailed p-value of less than 0.05.
Results
Baseline features based on EASIX groups
Table 1 delineates the clinical characteristics of 38,794 subjects, stratified into tertiles according to the EASIX levels. The mean age was 49.4 ± 18.1 years old, with a female representation of 51.2%. The overall prevalence of CVD was 10.4%, with a notable increase in prevalence corresponding to higher EASIX tertiles (T1: 5.2%, T2: 7.8%, T3: 18.2%). Subjects in the highest EASIX tertile demonstrated an elevated risk of stroke, CHF, CHD, myocardial infarction, and angina relative, to those in the lowest tertile. Furthermore, those in the highest tertile were generally older, with higher levels of AST, BMI, creatinine, ALT, and uric acid. They were also more frequently male, widowed or divorced, and engaged in the use of alcohol and tobacco, while exhibiting higher income levels. Additionally, this group had a greater prevalence of hypertension, hyperlipidemia, and diabetes (all P < 0.001).
Table 1.
Clinical characteristics of participants by EASIX groups
| Characteristics | Total (n = 38794) | T1 (n = 12931) | T2 (n = 12931) | T3 (n = 12932) | P-value |
|---|---|---|---|---|---|
| Age, year | 49.4 ± 18.1 | 42.8 ± 15.9 | 48.8 ± 17.4 | 56.5 ± 18.1 | < 0.001 |
| Gender, n (%) | < 0.001 | ||||
| Male | 18928 (48.8) | 2693 (20.8) | 6969 (53.9) | 9266 (71.7) | |
| Female | 19866 (51.2) | 10238 (79.2) | 5962 (46.1) | 3666 (28.3) | |
| PIR | 2.20 (1.16, 4.17) | 2.06 (1.06, 3.98) | 2.26 (1.18, 4.25) | 2.29 (1.21, 4.25) | < 0.001 |
| BMI, kg/m2 | 28.9 ± 6.7 | 28.9 ± 7.1 | 28.7 ± 6.6 | 29.1 ± 6.4 | < 0.001 |
| Race, n (%) | < 0.001 | ||||
| Mexican American | 6969 (18.0) | 3286 (25.4) | 2211 (17.1) | 1472 (11.4) | |
| Other Hispanic | 3138 (8.1) | 1302 (10.1) | 1052 (8.1) | 784 (6.1) | |
| Non-Hispanic White | 18117 (46.7) | 5353 (41.4) | 6330 (49) | 6434 (49.8) | |
| Non-Hispanic Black | 7651 (19.7) | 1874 (14.5) | 2347 (18.2) | 3430 (26.5) | |
| Other Race | 2919 (7.5) | 1116 (8.6) | 991 (7.7) | 812 (6.3) | |
| Physical activity, n (%) | 0.231 | ||||
| Low | 32238 (83.1) | 10978 (84.9) | 10681 (82.6) | 10579 (82.2) | |
| High | 6556 (16.9) | 1953 (15.1) | 2250 (17.4) | 2353 (17.8) | |
| Marital group, n (%) | < 0.001 | ||||
| Married or living with partner | 25008 (64.5) | 8352 (64.6) | 8311 (64.3) | 8345 (64.5) | |
| Widowed or divorced | 7159 (18.5) | 2022 (15.6) | 2333 (18) | 2804 (21.7) | |
| Never married | 6627 (17.1) | 2557 (19.8) | 2287 (17.7) | 1783 (13.8) | |
| Education level, n (%) | < 0.001 | ||||
| Less than high school | 10345 (26.7) | 3598 (27.8) | 3333 (25.8) | 3414 (26.4) | |
| High school or above | 28449 (73.3) | 9333 (72.2) | 9598 (74.2) | 9518 (73.6) | |
| Smoking status, n (%) | < 0.001 | ||||
| Current or ever | 17998 (46.4) | 5399 (41.8) | 6185 (47.8) | 6414 (49.6) | |
| Never | 20796 (53.6) | 7532 (58.2) | 6746 (52.2) | 6518 (50.4) | |
| Drinking status, n (%) | < 0.001 | ||||
| Current or ever | 27344 (70.5) | 8498 (65.7) | 9399 (72.7) | 9447 (73.1) | |
| Never | 11450 (29.5) | 4433 (34.3) | 3532 (27.3) | 3485 (26.9) | |
| Diabetes, n (%) | 5226 (13.5) | 1413 (10.9) | 1487 (11.5) | 2326 (18) | < 0.001 |
| Hypertension, n (%) | 13287 (34.3) | 3270 (25.3) | 4100 (31.7) | 5917 (45.8) | < 0.001 |
| Hyperlipidemia, n (%) | 28116 (72.5) | 9329 (72.1) | 9316 (72) | 9471 (73.2) | 0.003 |
| ALT, U/L | 21.0 (16.0, 28.0) | 18.0 (15.0, 25.0) | 21.0 (17.0, 29.0) | 23.0 (18.0, 31.0) | < 0.001 |
| AST, U/L | 23.0 (19.0, 27.8) | 21.0 (18.0, 24.0) | 23.0 (20.0, 27.0) | 25.0 (21.0, 30.0) | < 0.001 |
| Albumin, g/L | 42.5 ± 3.6 | 41.7 ± 3.9 | 43.0 ± 3.4 | 42.7 ± 3.4 | < 0.001 |
| Creatinine, mg/dl | 0.84 (0.70, 1.00) | 0.70 (0.60, 0.80) | 0.85 (0.75, 0.96) | 1.02 (0.90, 1.20) | < 0.001 |
| Uric acid, mmol/L | 315.2 (261.7, 374.7) | 273.6 (226.0, 327.1) | 321.2 (267.7, 374.7) | 350.9 (303.3, 410.4) | < 0.001 |
| CHD, n (%) | 1561 (4.0) | 198 (1.5) | 328 (2.5) | 1035 (8.0) | < 0.001 |
| Angina pectoris, n (%) | 1094 (2.8) | 189 (1.5) | 295 (2.3) | 610 (4.7) | < 0.001 |
| CHF, n (%) | 1165 (3.0) | 137 (1.1) | 244 (1.9) | 784 (6.1) | < 0.001 |
| Heart attack, n (%) | 1569 (4.0) | 226 (1.7) | 369 (2.9) | 974 (7.5) | < 0.001 |
| Stroke, n (%) | 1312 (3.4) | 225 (1.7) | 357 (2.8) | 730 (5.6) | < 0.001 |
| CVD, n (%) | 4034 (10.4) | 667 (5.2) | 1015 (7.8) | 2352 (18.2) | < 0.001 |
Values are mean ± SD, median (IQR) or number (%)
BMI Body mass index, PIR Poverty-income ratio, CHD Coronary heart disease
Association between EASIX and the risk of CVD
Table 2 illustrates the association between EASIX and the risk of CVD. In the fully adjusted models, a significant positive association was identified between EASIX and the probability of CVD after comparing to the reference category (Tertile 1), with aOR of 1.33 and 95% CI of 1.26–1.41. Furthermore, subjects in the third tertile of EASIX exhibited a risk of CVD increased by 48%, as indicated by aOR of 1.48 and 95% CI of 1.32–1.65.
Table 2.
Association between EASIX and risk of CVD
| Subgroups | Model 1 | Model 2 | Model 3 | |||
|---|---|---|---|---|---|---|
| OR (95% CI) | P-value | OR (95% CI) | P-value | OR (95% CI) | P-value | |
| CVD | ||||||
| EASIX (continuous) | 2.86 (2.64 ~ 3.09) | < 0.001 | 1.5 (1.41 ~ 1.6) | < 0.001 | 1.33 (1.26 ~ 1.41) | < 0.001 |
| EASIX (tertile) | ||||||
| T1 | 1 (Ref) | 1 (Ref) | 1 (Ref) | |||
| T2 | 1.57 (1.42 ~ 1.73) | < 0.001 | 0.96 (0.86 ~ 1.07) | 0.438 | 0.98 (0.88 ~ 1.1) | 0.721 |
| T3 | 4.09 (3.74 ~ 4.47) | < 0.001 | 1.59 (1.43 ~ 1.76) | < 0.001 | 1.48 (1.32 ~ 1.65) | < 0.001 |
| P for trend | < 0.001 | < 0.001 | < 0.001 | |||
| CHD | ||||||
| EASIX (continuous) | 1.67 (1.56 ~ 1.79) | < 0.001 | 1.31 (1.23 ~ 1.39) | < 0.001 | 1.23 (1.16 ~ 1.31) | < 0.001 |
| EASIX (tertile) | ||||||
| T1 | 1 (Ref) | 1 (Ref) | 1 (Ref) | |||
| T2 | 1.67 (1.40 ~ 2.00) | < 0.001 | 0.91 (0.75 ~ 1.09) | 0.303 | 0.92 (0.76 ~ 1.11) | 0.405 |
| T3 | 5.59 (4.8 ~ 6.53) | < 0.001 | 1.78 (1.5 ~ 2.11) | < 0.001 | 1.64 (1.37 ~ 1.96) | < 0.001 |
| P for trend | < 0.001 | < 0.001 | < 0.001 | |||
| Angina pectoris | ||||||
| EASIX (continuous) | 1.35 (1.27 ~ 1.44) | < 0.001 | 1.2 (1.13 ~ 1.28) | < 0.001 | 1.15 (1.07 ~ 1.23) | < 0.001 |
| EASIX (tertile) | ||||||
| T1 | 1 (Ref) | 1 (Ref) | 1 (Ref) | |||
| T2 | 1.57 (1.31 ~ 1.89) | < 0.001 | 1.06 (0.87 ~ 1.28) | 0.553 | 1.07 (0.88 ~ 1.3) | 0.489 |
| T3 | 3.34 (2.83 ~ 3.94) | < 0.001 | 1.5 (1.24 ~ 1.81) | < 0.001 | 1.37 (1.13 ~ 1.66) | 0.002 |
| P for trend | < 0.001 | < 0.001 | < 0.001 | |||
| Congestive heart failure | ||||||
| EASIX (continuous) | 1.78 (1.66 ~ 1.91) | < 0.001 | 1.44 (1.35 ~ 1.54) | < 0.001 | 1.29 (1.22 ~ 1.37) | < 0.001 |
| EASIX (tertile) | ||||||
| T1 | 1 (Ref) | 1 (Ref) | 1 (Ref) | |||
| T2 | 1.8 (1.45 ~ 2.22) | < 0.001 | 1.19 (0.96 ~ 1.48) | 0.109 | 1.16 (0.93 ~ 1.45) | 0.176 |
| T3 | 6.03 (5.02 ~ 7.24) | < 0.001 | 2.62 (2.14 ~ 3.21) | < 0.001 | 2.05 (1.66 ~ 2.53) | < 0.001 |
| P for trend | < 0.001 | < 0.001 | < 0.001 | |||
| Heart attack | ||||||
| EASIX (continuous) | 1.57 (1.47 ~ 1.67) | < 0.001 | 1.23 (1.17 ~ 1.31) | < 0.001 | 1.16 (1.10 ~ 1.23) | < 0.001 |
| EASIX (tertile) | ||||||
| T1 | 1 (Ref) | 1 (Ref) | 1 (Ref) | |||
| T2 | 1.65 (1.4 ~ 1.95) | < 0.001 | 0.92 (0.77 ~ 1.09) | 0.333 | 0.95 (0.79 ~ 1.13) | 0.534 |
| T3 | 4.58 (3.95 ~ 5.3) | < 0.001 | 1.49 (1.26 ~ 1.76) | < 0.001 | 1.39 (1.17 ~ 1.65) | < 0.001 |
| P for trend | < 0.001 | < 0.001 | < 0.001 | |||
| Stroke | ||||||
| EASIX (continuous) | 1.44 (1.35 ~ 1.53) | < 0.001 | 1.22 (1.15 ~ 1.29) | < 0.001 | 1.16 (1.09 ~ 1.23) | < 0.001 |
| EASIX (tertile) | ||||||
| T1 | 1 (Ref) | 1 (Ref) | 1 (Ref) | |||
| T2 | 1.6 (1.35 ~ 1.9) | < 0.001 | 1.09 (0.91 ~ 1.3) | 0.346 | 1.14 (0.95 ~ 1.36) | 0.165 |
| T3 | 3.38 (2.9 ~ 3.93) | < 0.001 | 1.5 (1.26 ~ 1.78) | < 0.001 | 1.43 (1.2 ~ 1.71) | < 0.001 |
| P for trend | < 0.001 | < 0.001 | < 0.001 | |||
Model 1: None covariates were adjusted
Model 2: gender, race and age were adjusted
Model 3, age, gender, race, BMI, physical activities, diabetes, education level, smoking status, PIR, hyperlipidemia, marital status, ALT, albumin, hypertension, alcohol abuse, AST, creatinine, and uric acid were adjusted
A positive association was identified between EASIX and the risk of CHD (aOR = 1.23, 95% CI: 1.16, 1.31), angina pectoris (aOR = 1.15, 95% CI: 1.07, 1.23), CHF (aOR = 1.29, 95% CI: 1.22, 1.37), myocardial infarction (aOR = 1.16, 95% CI: 1.10, 1.23), and stroke (aOR = 1.16, 95% CI: 1.09, 1.23) (refer to Table 2) after EASIX was analyzed as a continuous variable. Furthermore, in the fully adjusted Model 3, subjects in the highest tertile of EASIX exhibited an increased risk for CHD, angina pectoris, CHF, myocardial infarction, and stroke, by 64%, 37%, 105%, 39%, and 43%, respectively.
Nonlinear association between EASIX and the risk of CVD
An RCS analysis was conducted to explore the association between EASIX and the risk of CVD. As illustrated in Fig. 2, the RCS analysis demonstrates a significant association between EASIX and the risk of CVD, characterized by a J-shaped trend. Specifically, when EASIX exceeded 0.3677, a significant effect value of 2.58 was observed. Conversely, when EASIX was below 0.3677, the effect value did not reach statistical significance (Table 3).
Fig. 2.
Restricted cubic spline fitting for the association between EASIX and CVD risk. The model was adjusted for age, gender, race, BMI, physical activities, diabetes, education level, smoking status, PIR, hyperlipidemia, marital status, ALT, albumin, hypertension, alcohol abuse, AST, creatinine, and uric acid. Knots were placed at the 5th, 35th, 65th, and 95th percentiles. The reference value was set at the 50th percentile
Table 3.
Threshold effect analysis of EASIX and CVD using the two-piecewise linear regression model
| Adjusted OR (95% CI) | P value | |
|---|---|---|
| Fitting by the standard linear model | 1.33 (1.26 ~ 1.41) | < 0.001 |
| Fitting by the two-piecewise linear model | ||
| Inflection point | 0.3677 | |
| EASIX < 0.3677 | 0.71 (0.17 ~ 3.03) | 0.648 |
| EASIX ≥ 0.3677 | 2.58 (2.25 ~ 2.96) | < 0.001 |
| Log likelihood ratio | 0.049 |
Age, gender, race, BMI, physical activities, diabetes, education level, smoking status, PIR, hyperlipidemia, marital status, ALT, albumin, hypertension, alcohol abuse, AST, creatinine, and uric acid were adjusted
Figure 2 further illustrates the positive nonlinear association between EASIX and the risks of CHD, CHF, and heart attack. Additionally, a positive linear association between EASIX and both angina pectoris and stroke were identified.
Subgroup analysis
Subgroup analyses and interaction tests were performed to examine the consistency of the association between EASIX and the risk of CVD across various population subgroups, including age, hypertension, gender, BMI, diabetes, alcohol abuse, smoking status, and hyperlipidemia. As illustrated in Fig. 3, the results indicated a significantly stronger positive association between EASIX and the risk of CVD among older subjects and those with hyperlipidemia (p for interaction < 0.05).
Fig. 3.
Association between EASIX and CVD risk in various subgroups. The model was adjusted for age, gender, race, BMI, physical activities, diabetes, education level, smoking status, PIR, hyperlipidemia, marital status, ALT, albumin, hypertension, alcohol abuse, AST, creatinine, and uric acid
ROC analysis
To evaluate the prognostic accuracy of EASIX for CVD, AUC was calculated and compared to that of other individual markers, namely creatinine, LDH, and PLT. The analysis demonstrates that EASIX achieved a significantly higher AUC than the other individual markers in predicting CVD, CHD, myocardial infarction angina pectoris, and CHF, with all comparisons yielding p-values less than 0.001 (see Fig. 4 and Table 4). These results indicate that EASIX possesses superior discriminative power and accuracy in predicting CVD events compared to the other individual markers.
Fig. 4.
ROC analysis of EASIX, creatinine, LDH, and PLT to (A) CVD, (B) CHD, (C) angina pectoris, (D) CHF, (E) heart attack, (F) stroke among American adults
Table 4.
The AUC for EASIX, creatinine, LDH, and PLT to discriminate CVD risk
| AUC | 95%CI | Cutoff value | Sensitivity | Specificity | P for difference in AUC | |
|---|---|---|---|---|---|---|
| CVD | ||||||
| EASIX | 0.700 | 0.691–0.709 | 0.602 | 0.513 | 0.790 | Reference |
| Creatinine | 0.676 | 0.667–0.685 | 0.955 | 0.548 | 0.710 | < 0.001 |
| LDH | 0.601 | 0.592–0.611 | 133.5 | 0.551 | 0.598 | < 0.001 |
| PLT | 0.589 | 0.579–0.599 | 211.5 | 0.403 | 0.743 | < 0.001 |
| CHD | ||||||
| EASIX | 0.729 | 0.715–0.742 | 0.613 | 0.580 | 0.777 | Reference |
| Creatinine | 0.700 | 0.686–0.714 | 0.955 | 0.610 | 0.696 | < 0.001 |
| LDH | 0.602 | 0.588–0.616 | 132.5 | 0.572 | 0.573 | < 0.001 |
| PLT | 0.639 | 0.624–0.654 | 214.5 | 0.507 | 0.718 | < 0.001 |
| Angina pectoris | ||||||
| EASIX | 0.658 | 0.641–0.675 | 0.492 | 0.644 | 0.600 | Reference |
| Creatinine | 0.637 | 0.620–0.655 | 0.895 | 0.636 | 0.562 | < 0.001 |
| LDH | 0.592 | 0.575–0.609 | 131.5 | 0.587 | 0.556 | < 0.001 |
| PLT | 0.575 | 0.557–0.593 | 210.3 | 0.389 | 0.739 | < 0.001 |
| CHF | ||||||
| EASIX | 0.734 | 0.718–0.750 | 0.614 | 0.584 | 0.773 | Reference |
| Creatinine | 0.716 | 0.700–0.732 | 0.955 | 0.628 | 0.693 | < 0.001 |
| LDH | 0.629 | 0.612–0.646 | 136.5 | 0.557 | 0.631 | < 0.001 |
| PLT | 0.611 | 0.594–0.629 | 211.5 | 0.453 | 0.734 | < 0.001 |
| Heart attack | ||||||
| EASIX | 0.704 | 0.691–0.718 | 0.576 | 0.575 | 0.732 | Reference |
| Creatinine | 0.692 | 0.678–0.706 | 0.975 | 0.577 | 0.710 | < 0.001 |
| LDH | 0.596 | 0.581–0.610 | 132.5 | 0.564 | 0.573 | < 0.001 |
| PLT | 0.605 | 0.589–0.620 | 221.5 | 0.500 | 0.671 | < 0.001 |
| Stroke | ||||||
| EASIX | 0.661 | 0.645–0.676 | 0.572 | 0.514 | 0.722 | Reference |
| Creatinine | 0.665 | 0.649–0.681 | 1.045 | 0.428 | 0.812 | 0.102 |
| LDH | 0.585 | 0.569–0.601 | 133.5 | 0.549 | 0.587 | < 0.001 |
| PLT | 0.555 | 0.538–0.571 | 238.2 | 0.536 | 0.552 | < 0.001 |
Discussion
This study provides the first evidence establishing EASIX as a predictive biomarker for CVD within a large, nationally representative cohort. Elevated EASIX levels are significantly associated with increased odds of CVD, encompassing CHD, angina pectoris, CHF, myocardial infarction, and stroke. Subgroup analyses confirmed the robustness of these conclusions.
EASIX, a biomarker gaining recognition for its prognostic utility, especially in disorders marked by endothelial dysfunction, derived from measurements of LDH, creatinine, and PLT [8]. This index functions as a marker for the extent of endothelial activation and stress, both of which are crucial in the pathogenesis of various diseases [26]. A substantial body of research has demonstrated a significant correlation between EASIX and levels of endothelial activation markers, such as CXCL8, interleukin-18, CXCL9, tumor inhibitory factor-2, insulin-like growth factor-1, soluble thrombomodulin, and angiopoietin-2 [8, 10, 27, 28]. Consequently, the prognostic relevance of EASIX has been investigated and utilized in various diseases, including low-risk myelodysplastic syndromes, multiple myeloma, neococcal pneumonia, diffuse large B-cell lymphoma, small-cell lung cancer, sepsis, and in critically ill patients with advanced liver disease [15, 29–32]. Moreover, EASIX has been examined in cardiac conditions, including myocardial infarction and CHD, where it has been validated as a potential biomarker for predicting mortality [20]. Building on these findings, this study is the first to investigate the association between EASIX and the risk of CVD in American adults. The systemic endothelial health status, as indicated by EASIX, has been shown to be a critical factor in evaluating CVD risk.
Furthermore, the present study identified a more pronounced positive association between EASIX and the risk of CVD among older adults and individuals with hyperlipidemia. Prior research underscores the heightened prevalence of endothelial dysfunction within these groups [33, 34]. For example, one study reported that older adults with atherosclerosis exhibited increased serum levels of adhesion molecules, such as soluble vascular cell adhesion molecule-1, soluble intercellular adhesion molecule-1, and E-selectin, comparing to younger control, indicating that aging significantly exacerbates endothelial impairment [35]. Additionally, older individuals and those with hyperlipidemia are at an increased risk of hypertension, metabolic syndrome, and diabetes [21, 36, 37]. These conditions can detrimentally impact vascular endothelial function and promote atherosclerosis, potentially amplifying the association between elevated EASIX levels and the risk of CVD.
This study is the inaugural investigation to delineate a non-linear association between EASIX and the risk of CVD, identifying a critical threshold value of 82.5. Beyond this threshold, there is a significant escalation in the prevalence of CVD, underscoring the imperative for early intervention and diagnosis in the management for CVD. Moreover, the study reveals that AUC for EASIX was significantly superior to that of other individual markers in predicting CVD, angina pectoris, myocardial infarction, CHF, and CHD. Therefore, EASIX is anticipated to become a reliable and valuable indicator for predicting CVD.
The potential mechanism underlying this association may be attributed to multiple factors. Firstly, EASIX serves as an indicator of endothelial cell activation and damage [38]. Endothelial dysfunction is pivotal in the pathogenesis of CVD, as it facilitates the development of inflammation, atherosclerosis, and thrombosis [39]. Secondly, creatinine, a constituent of EASIX score, serves as an indicator of renal function [40]. Serum creatinine serves as an indicator of a pro-inflammatory state, and inflammation-induced endothelial dysfunction has been associated with the occurrence of CVD in females with compromised renal functions [41]. Furthermore, elevated serum creatinine levels are frequently associated with a reduced glomerular filtration rate, which can lead to water-sodium retention, thereby increasing cardiac load and cardiovascular risk [42, 43]. Thirdly, reduced platelet counts, a component of EASIX score, may indicate heightened platelet destruction or consumption, commonly observed in cases of severe systemic inflammation or disseminated intravascular coagulation [44], which is associated with an elevated risk of CVD [45]. Furthermore, during inflammatory and oxidative stress events that mediate endothelial cell necrosis or apoptosis [46], LDH is released from leukocytes, endothelial cells, and platelets, resulting in elevated plasma LDH levels [47]. Elevated LDH levels are associated with an increased risk of atherosclerotic cardiovascular disease, serving as an indicator of underlying inflammatory processes and vascular damage [48].
This study theoretically contributes novel evidence supporting EASIX as a biomarker for CVD, thereby enriching the etiological investigation of CVD and advancing the understanding of endothelial dysfunction in the pathogenesis of CVD. Practically, these findings implied that EASIX can be integrated into risk assessment and screening protocols. Specifically, it offers potential as a new diagnostic and predictive clinical tool, aiding physicians in more accurately evaluating the probability of cardiovascular events in patients and facilitating the development of personalized treatment and prevention strategies.
This study acknowledges several limitations. Firstly, the determination of causality was not possible due to the cross-sectional nature of the data from NHANES. Secondly, the study did not account for all potential confounding variables, leaving several unaddressed confounders that may impact the results. Thirdly, the findings are based on American adults, which may limit the generalizability of the results to populations of different racial or regional backgrounds due to potential selection bias. Lastly, the study was constrained by the availability of only single EASIX values, precluding an analysis of dynamic changes over time. Future research involving larger sample sizes, longer observation durations, and randomized controlled trials is necessary to confirm and build upon these findings.
Conclusion
The findings of this study suggest that EASIX serves as a significant tool for predicting the development of CVD in general American adults, characterized by a nonlinear association between EASIX and the probability of CVD. Moreover, this study holds considerable implications for potentially reducing the costs associated with screening processes.
Electronic Supplementary Material
Below is the link to the electronic supplementary material.
Acknowledgements
We would like to thank the NHANES database for providing the data source for this study.
Authors’ contributions
YZJ designed the study; XX, and MYZ collected biochemical data; WCL drafted the manuscript. All authors read and approved the final manuscript.
Funding
Not applicable.
Data availability
YZJ designed the study; XX, and MYZ collected biochemical data; WCL drafted the manuscript. All authors read and approved the final manuscript.
Declarations
Ethics approval and consent to participate
The study was approved by the National Centre for Health Statistics Research Ethics Review Board, and every participant signed informed consent. The written informed consent of all subjects was obtained following the Declaration of Helsinki.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Diseases GBD, Injuries C. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global burden of cardiovascular diseases and risk factors, 1990–2019: update from the GBD 2019 study. J Am Coll Cardiol. 2020;76(25):2982–3021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Yan J, Zhong X, Zhao Y, Wang X. Role and mechanism of miRNA in cardiac microvascular endothelial cells in cardiovascular diseases. Front Cardiovasc Med. 2024;11:1356152. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Di Febo R, Saeed Z, Serafini F, Brocco D, D’Ascanio F, Pizzi AD, et al. Diagnostic and prognostic roles of endothelial- and platelet-derived extracellular vesicles in cardiovascular diseases. J Transl Med. 2025;23(1):553. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Xu S, Ilyas I, Little PJ, Li H, Kamato D, Zheng X, et al. Endothelial dysfunction in atherosclerotic cardiovascular diseases and beyond: from mechanism to pharmacotherapies. Pharmacol Rev. 2021;73(3):924–67. [DOI] [PubMed] [Google Scholar]
- 6.Vanhoutte PM, Shimokawa H, Feletou M, Tang EH. Endothelial dysfunction and vascular disease - a 30th anniversary update. Acta Physiol (Oxf). 2017;219(1):22–96. [DOI] [PubMed] [Google Scholar]
- 7.Libby P, Ridker PM, Hansson GK. Progress and challenges in translating the biology of atherosclerosis. Nature. 2011;473(7347):317–25. [DOI] [PubMed] [Google Scholar]
- 8.Luft T, Benner A, Terzer T, Jodele S, Dandoy CE, Storb R, et al. EASIX and mortality after allogeneic stem cell transplantation. Bone Marrow Transplant. 2020;55(3):553–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Penack O, Luft T, Peczynski C, Benner A, Sica S, Arat M, Itala-Remes M, Corral LL, Schaap NPM, Karas M, et al. Endothelial Activation and Stress Index (EASIX) to predict mortality after allogeneic stem cell transplantation: a prospective study. J Immunother Cancer. 2024;12(1):e007635. [DOI] [PMC free article] [PubMed]
- 10.Kordelas L, Terzer T, Gooley T, Davis C, Sandmaier BM, Sorror M, et al. EASIX-1year and late mortality after allogeneic stem cell transplantation. Blood Adv. 2023;7(18):5374–81. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Parra-Bonilla G, Alvarez DF, Alexeyev M, Vasauskas A, Stevens T. Lactate dehydrogenase a expression is necessary to sustain rapid angiogenesis of pulmonary microvascular endothelium. PLoS One. 2013;8(9):e75984. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Cofiell R, Kukreja A, Bedard K, Yan Y, Mickle AP, Ogawa M, et al. Eculizumab reduces complement activation, inflammation, endothelial damage, thrombosis, and renal injury markers in aHUS. Blood. 2015;125(21):3253–62. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Carrizzo A, Izzo C, Oliveti M, Alfano A, Virtuoso N, Capunzo M, Di Pietro P, Calabrese M, De Simone E, Sciarretta S, et al. The main determinants of diabetes mellitus vascular complications: endothelial dysfunction and platelet hyperaggregation. Int J Mol Sci. 2018;19(10):2968. [DOI] [PMC free article] [PubMed]
- 14.Schult D, Rasch S, Schmid RM, Lahmer T, Mayr U. EASIX is an accurate and easily available prognostic score in critically ill patients with advanced liver disease. J Clin Med. 2023;12(7):2553. [DOI] [PMC free article] [PubMed]
- 15.Xu HB, Ye Y, Xue F, Wu J, Suo Z, Zhang H. Association between endothelial activation and stress index and 28-day mortality in septic ICU patients: a retrospective cohort study. Int J Med Sci. 2023;20(9):1165–73. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Wang R, Jiang Y, He M, Xu J. EASIX is an effective marker in predicting mortality of traumatic brain injury patients. Eur J Med Res. 2024;29(1):300. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Wang J, Chen X, Qin C, Zeng X, Du X, Wang D. The endothelial activation and stress index is a potential prognostic indicator for patients with acute pancreatitis managed in the intensive care unit: a retrospective study. Front Med (Lausanne). 2024;11:1498148. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Finke D, Hund H, Frey N, Luft T, Lehmann LH. EASIX (endothelial activation and stress index) predicts mortality in patients with coronary artery disease. Clin Res Cardiol. 2025;114(8):1008–18. [DOI] [PMC free article] [PubMed]
- 19.Yin F, Wang K. Independent prognostic importance of endothelial activation and stress index (EASIX) in critically ill patients with heart failure: modulating role of inflammation. Front Med (Lausanne). 2025;12:1560947. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Sang M, Ma X, Zhu F, Zhu C, Ying Z. Association between endothelial activation and stress index and 30-day mortality risk in acute myocardial infarction patients: a study based on the medical information mart for intensive care-IV database. BMC Cardiovasc Disord. 2024;24(1):699. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Zhou Y, Lin H, Weng X, Dai H, Xu J. Correlation between hs-CRP-triglyceride glucose index and NAFLD and liver fibrosis. BMC Gastroenterol. 2025;25(1):252. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Xie C, Chen X, Zhang J, Jiang X, Xu J, Lin H. Metabolic score for visceral fat is correlated with all-cause and cardiovascular mortality among individuals with non-alcoholic fatty liver disease. BMC Gastroenterol. 2025;25(1):238. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Yin J, Chen J, Chen Y. Mediation of endothelial activation and stress index in the association between vitamin B(6) turnover rate and diabetic retinopathy: an analysis of the National Health and Nutrition Examination Survey. Front Nutr. 2024;11:1490340. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Zhang Q, Xiao S, Jiao X, Shen Y. The triglyceride-glucose index is a predictor for cardiovascular and all-cause mortality in CVD patients with diabetes or pre-diabetes: evidence from NHANES 2001–2018. Cardiovasc Diabetol. 2023;22(1):279. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Zipf G, Chiappa M, Porter KS, Ostchega Y, Lewis BG, Dostal J. National health and nutrition examination survey: plan and operations, 1999–2010. Vital Health Stat 1. 2013;56:1–37. [PubMed] [Google Scholar]
- 26.Luft T, Benner A, Jodele S, Dandoy CE, Storb R, Gooley T, et al. EASIX in patients with acute graft-versus-host disease: a retrospective cohort analysis. Lancet Haematol. 2017;4(9):e414–23. [DOI] [PubMed] [Google Scholar]
- 27.Luft T, Wendtner CM, Kosely F, Radujkovic A, Benner A, Korell F, et al. EASIX for prediction of outcome in hospitalized SARS-CoV-2 infected patients. Front Immunol. 2021;12:634416. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Dai H, Penack O, Radujkovic A, Schult D, Majer-Lauterbach J, Blau IW, et al. Early bilirubinemia after allogeneic stem cell transplantation-an endothelial complication. Bone Marrow Transplant. 2021;56(7):1573–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Song GY, Jung SH, Kim K, Kim SJ, Yoon SE, Lee HS, et al. Endothelial activation and stress index (EASIX) is a reliable predictor for overall survival in patients with multiple myeloma. BMC Cancer. 2020;20(1):803. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Perez-Garcia F, Bailen R, Torres-Macho J, Fernandez-Rodriguez A, Jimenez-Sousa MA, Jimenez E, et al. Age-adjusted endothelial activation and stress index for Coronavirus disease 2019 at admission is a reliable predictor for 28-day mortality in hospitalized patients with Coronavirus disease 2019. Front Med. 2021;8:736028. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Park S, Go SI, Lee GW. The endothelial activation and stress index (EASIX) score is an independent prognostic factor in patients with diffuse large B-cell lymphoma. BMC Cancer. 2022;22(1):816. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Go SI, Park S, Kang MH, Kim HG, Kang JH, Kim JH, et al. Endothelial activation and stress index (EASIX) as a predictive biomarker in small cell lung cancer. Cancer Biomark. 2022;35(2):217–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Ungvari Z, Tarantini S, Kiss T, Wren JD, Giles CB, Griffin CT, et al. Endothelial dysfunction and angiogenesis impairment in the ageing vasculature. Nat Rev Cardiol. 2018;15(9):555–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Ansar S, Koska J, Reaven PD. Postprandial hyperlipidemia, endothelial dysfunction and cardiovascular risk: focus on incretins. Cardiovasc Diabetol. 2011;10:61. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Rajagopalan S, Brook R, Mehta RH, Supiano M, Pitt B. Effect of losartan in aging-related endothelial impairment. Am J Cardiol. 2002;89(5):562–6. [DOI] [PubMed] [Google Scholar]
- 36.Bonomini F, Rodella LF, Rezzani R. Metabolic syndrome, aging and involvement of oxidative stress. Aging Dis. 2015;6(2):109–20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Zhou X, Weng X, Xu J, Wang W. Correlation between remnant cholesterol and hyperuricemia in American adults. Lipids Health Dis. 2024;23(1):176. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Pedraza A, Salas MQ, Rodriguez-Lobato LG, Escribano-Serrat S, Suarez-Lledo M, Martinez-Cebrian N, et al. Easix score correlates with endothelial dysfunction biomarkers and predicts risk of acute graft-versus-host disease after allogeneic transplantation. Transplant Cell Ther. 2024;30(2):187 e181-187 e112. [DOI] [PubMed] [Google Scholar]
- 39.Gimbrone MA Jr., Garcia-Cardena G. Endothelial cell dysfunction and the pathobiology of atherosclerosis. Circ Res. 2016;118(4):620–36. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Gounden V, Bhatt H, Jialal I. Renal Function Tests. In: StatPearls. edn. Treasure Island (FL) ineligible companies. Disclosure: Harshil Bhatt declares no relevant financial relationships with ineligible companies. Disclosure: Ishwarlal Jialal declares no relevant financial relationships with ineligible companies. 2025.
- 41.Chen J, Yan L, Hu L, Xiao S, Liao Y, Yao X, et al. Association between the serum creatinine to cystatin C ratio and cardiovascular disease in middle-aged and older adults in China: a nationwide cohort study. J Am Heart Assoc. 2025;14(9):e040050. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Rye KA, Barter PJ. Formation and metabolism of prebeta-migrating, lipid-poor apolipoprotein A-I. Arterioscler Thromb Vasc Biol. 2004;24(3):421–8. [DOI] [PubMed] [Google Scholar]
- 43.Rasouli M, Trischuk TC, Lehner R. Calmodulin antagonist W-7 inhibits de novo synthesis of cholesterol and suppresses secretion of de novo synthesized and preformed lipids from cultured hepatocytes. Biochim Biophys Acta. 2004;1682(1–3):92–101. [DOI] [PubMed] [Google Scholar]
- 44.Gauer RL, Whitaker DJ. Thrombocytopenia: evaluation and management. Am Fam Physician. 2022;106(3):288–98. [PubMed] [Google Scholar]
- 45.Lee DH, Yao C, Bhan A, Schlaeger T, Keefe J, Rodriguez BAT, et al. Integrative genomic analysis reveals four protein biomarkers for platelet traits. Circ Res. 2020;127(9):1182–94. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Munzel T, Sinning C, Post F, Warnholtz A, Schulz E. Pathophysiology, diagnosis and prognostic implications of endothelial dysfunction. Ann Med. 2008;40(3):180–96. [DOI] [PubMed] [Google Scholar]
- 47.Guo H, Sun J, Li D, Hu Y, Yu X, Hua H, et al. Shikonin attenuates acetaminophen-induced acute liver injury via inhibition of oxidative stress and inflammation. Biomed Pharmacother. 2019;112:108704. [DOI] [PubMed] [Google Scholar]
- 48.Parsanathan R, Jain SK. Novel invasive and noninvasive cardiac-specific biomarkers in obesity and cardiovascular diseases. Metab Syndr Relat Disord. 2020;18(1):10–30. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
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Supplementary Materials
Data Availability Statement
YZJ designed the study; XX, and MYZ collected biochemical data; WCL drafted the manuscript. All authors read and approved the final manuscript.