Endothelial activation and stress index links to cardiovascular disease in postmenopausal women mediated by arterial stiffness: A cross-sectional study of National Health and Nutrition Examination Survey 1999

Abstract

Objective

To investigate the association between the endothelial activation and stress index and cardiovascular disease prevalence among postmenopausal women and to evaluate whether arterial stiffness, assessed by estimated pulse wave velocity, statistically accounts for part of this association.

Methods

We conducted a cross-sectional analysis of 9877 postmenopausal women from the National Health and Nutrition Examination Survey (1999–2018). Endothelial activation and stress index was log-transformed (LnEASIX), and arterial stiffness was assessed using estimated pulse wave velocity. Logistic regression was used to evaluate associations with cardiovascular disease after adjusting for demographic, lifestyle, and clinical factors. Nonlinear trends were explored using restricted cubic splines, and mediation analysis were performed to assess the contribution of estimated pulse wave velocity.

Results

Higher LnEASIX was associated with greater cardiovascular disease prevalence (adjusted odds ratio per unit = 1.62, 95% confidence interval: 1.35–1.95). Risk increased sharply above an LnEASIX threshold of −0.97 (endothelial activation and stress index of approximately 0.38; odds ratio = 2.16, 95% confidence interval: 1.71–2.72). Estimated pulse wave velocity statistically accounted for 4.82% of this association. Associations were stronger in women with diabetes.

Conclusion

Elevated endothelial activation and stress index is associated with increased cardiovascular disease risk in postmenopausal women, with arterial stiffness partially mediating this relationship. These findings highlight the potential utility of endothelial activation and stress index as a practical biomarker for cardiovascular risk assessment and prevention.

Keywords

Endothelial activation and stress index endothelial malfunction cardiovascular disease estimated pulse wave velocity National Health and Nutrition Examination Survey

Introduction

Cardiovascular disease (CVD) remains the leading cause of mortality among women, with the risk increasing significantly after menopause.¹ Postmenopausal women experience a twofold to sixfold higher incidence of CVD than premenopausal women of the same age.² This increased risk is likely attributable to the reduced protective effects of estrogen on the cardiovascular system.³ Understanding the factors contributing to CVD in postmenopausal women is essential for improving management strategies and developing targeted interventions.

Endothelial dysfunction is recognized as an early vascular abnormality that precedes and contributes to the development of cardiovascular risk factors and clinical CVD.⁴ Recently, a novel tool known as the endothelial activation and stress index (EASIX) has gained attention. This index combines three routine laboratory parameters: lactate dehydrogenase (LDH), creatinine, and platelet count.⁵ In postmenopausal women, estrogen deficiency is associated with increased oxidative stress and chronic low-grade inflammation, both of which contribute to endothelial dysfunction.⁶ This dysfunction results in a vasculopathic phenotype characterized by end-organ injury, renal impairment, and a prothrombotic milieu.^7,8 These features are captured by the three components of EASIX: LDH as a marker of tissue injury, creatinine reflecting renal dysfunction, and platelet count indicating thrombotic propensity. EASIX has emerged as an accessible and cost-effective metric of microvascular injury and endothelial perturbation.⁹ Previous studies have shown that elevated EASIX levels are independently associated with increased cardiovascular mortality in patients with hypertension and diabetes as well as with higher mortality in patients with coronary artery disease.^10–12 Collectively, these findings support the hypothesis that EASIX serves as a surrogate indicator of endothelial status in postmenopausal women and may be associated with CVD prevalence in this population.

In addition, arterial stiffness is an important risk factor that increases the likelihood of CVD.¹³ By reducing nitric oxide (NO), endothelial dysfunction induces vasoconstriction, inflammation, and smooth muscle proliferation, all of which contribute to arterial stiffening.¹⁴ Estimated pulse wave velocity (ePWV) is a noninvasive parameter derived from age and blood pressure that serves as an indicator of arterial stiffness.¹⁵ Furthermore, endothelial function has been shown to inversely correlate with aortic pulse wave velocity (PWV).¹⁶ However, few studies have examined the potential contribution of ePWV to CVD risk associated with endothelial dysfunction in postmenopausal women.

Therefore, this study used data from the National Health and Nutrition Examination Survey (NHANES) database to investigate the association between EASIX and CVD risk in postmenopausal women and to determine whether ePWV partially mediates this relationship.

Methods

Data collection

The NHANES, managed by the National Center for Health Statistics (NCHS) under the Centers for Disease Control and Prevention (CDC), is an ongoing nationwide program designed to assess the health and nutritional status of the civilian, noninstitutionalized US population. Using advanced statistical sampling techniques, the program recruits approximately 5000 individuals annually through a representative probability-based approach. Ethical approval for the survey protocol is granted by the NCHS Research Ethics Review Board, and all participants provide written informed consent prior to participation.¹⁷ Data collection involves structured home interviews to obtain detailed information on sociodemographic characteristics, financial status, health behaviors, and medical history, followed by biennial cross-sectional surveys. All participant information has been anonymized. For this cross-sectional analysis, 20 years of data (1999–2018) spanning 10 consecutive NHANES cycles were used. All data, along with accompanying documentation and analytical guidance, are publicly available on the NHANES website (https://wwwn.cdc.gov/nchs/nhanes/Default.aspx). This study was conducted in accordance with the Declaration of Helsinki (1975, as revised in 2024). The reporting of this study conforms to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.¹⁸

Definition of postmenopausal women

Menopausal status was determined using self-reported reproductive health questionnaires. Women who answered “No” to the question, “Have you had at least one period in the past 12 months?” (2003–2006) or “Have you had regular menstruation in the past 12 months?” (1999–2002, 2007–2018) were asked a follow-up question: “What are the reasons for your lack of menstruation/regular menstruation in the past 12 months?” Women who responded “hysterectomy” or “menopause/life changes” were considered postmenopausal.¹⁹ The self-reported reproductive health questionnaire (Questionnaire Data – Continuous NHANES) is detailed in the RHQ module on the NHANES.

Assessment of EASIX and ePWV

EASIX was calculated using the formula: $LDH (U / L) \times creatinine (m g / d L) / platelet count (10^{9} / L)$ .²⁰ ePWV was calculated from age and mean blood pressure (MBP), with MBP derived from systolic blood pressure (SBP) and diastolic blood pressure (DBP) as follows: $ePWV = 9.587 - 0.402 \times age + 4.560 \times 10^{- 3} \times {age}^{2} - 2.621 \times 10^{- 5} \times {age}^{2} \times MBP + 3.176 \times 10^{- 3} \times age \times MBP - 1.832 \times 10^{- 2} \times MBP$ ; $MBP = DBP + 0.4 (SBP - DBP)$ .²¹

Definition of CVD

The diagnosis of CVD was based on self-reported physician diagnoses obtained during individual interviews using a standardized medical conditions questionnaire. Participants were asked, “Has a doctor or other health expert ever informed you that you have congestive heart failure (CHF)/coronary heart disease (CHD)/angina pectoris/myocardial infarction (MI)/stroke?” Participants were classified as having CVD if they answered “yes” to any of these conditions.

Other variables

Demographic information included participants’ age, race (Mexican American, Non-Hispanic Black, Non-Hispanic White, Other Hispanic, or Other Race), education (less than high school, high school or equivalent, or greater than high school), and marital status (having a partner or without a partner). Physical examination measurements included body mass index (BMI), SBP, and DBP. At the NHANES mobile examination center, trained personnel measured blood pressure following standardized procedures. Participants were required to rest quietly in a seated position for at least 5 min before undergoing 3 consecutive brachial artery blood pressure measurements at approximately 1-min intervals using an appropriately sized upper-arm cuff. The right arm was preferred; however, when unavailable or contraindicated, the left arm was used. NHANES does not require participants to fast or abstain from caffeine, smoking, or medication particularly for blood pressure measurement, which is conducted under routine examination conditions. Between 1999 and 2018, all measurements were obtained using the auscultation method with a mercury sphygmomanometer.²² NHANES provides standardized training and assessment for blood pressure observers and conducts equipment calibration and quality control to ensure consistency and reliability of data across different periods. The average of available readings was used to calculate SBP and DBP for analysis. Lifestyle factors included smoking status (current, former, or never) and drinking status (no or yes). Health conditions assessed included diabetes and hypertension. Diabetes was identified using multiple criteria: self-reported physician diagnosis, current use of diabetes medications (insulin or oral agents), glycated hemoglobin A1c (HbA1c) ≥6.5%, or fasting glucose ≥7 mmol/L.²³ Hypertension was defined as a prior physician-diagnosed condition, current use of antihypertensive medication, or a blood pressure reading of SBP ≥140 mmHg or DBP ≥90 mmHg.²⁴ Blood samples for laboratory testing were collected at the NHANES mobile examination center following standardized procedures. Baseline laboratory variables included in Table 1 were measured in nonfasting samples and were not restricted to fasting blood collection, consistent with routine NHANES procedures. Laboratory measurements included high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), albumin, alkaline phosphatase (ALP), aspartate aminotransferase (AST), alanine aminotransferase (ALT), LDH, total bilirubin, serum urea nitrogen, serum uric acid (UA), creatinine, and HbA1c. Additionally, estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation.²⁵

Table 1.

Baseline characteristics of participants stratified by LnEASIX tertiles.

Characteristic	Overall, N = 9877 (100%)^a	T1 (−3.37 to 1.00), N = 3293 (34%)^a	T2 (−1.00 to 0.63), N = 3292 (34%)^a	T3 (−0.63 to 2.71), N = 3292 (32%)^a	p-value^b
Age, years	62.00 (55.00, 71.00)	58.00 (52.00, 66.00)	62.00 (55.00, 70.00)	67.00 (59.00, 77.00)	<0.001
Race, n (%)					<0.001
Mexican American	1505 (4.70%)	751 (7.04%)	443 (3.98%)	311 (3.02%)
Non-Hispanic Black	1984 (9.83%)	473 (7.63%)	650 (9.40%)	861 (12.60%)
Non-Hispanic White	4768 (75.94%)	1433 (73.97%)	1661 (77.70%)	1674 (76.12%)
Other Hispanic	916 (4.25%)	401 (5.73%)	299 (4.11%)	216 (2.83%)
Other Race	704 (5.28%)	235 (5.63%)	239 (4.81%)	230 (5.43%)
Education, n (%)					0.004
Greater than high school	4481 (54.48%)	1421 (53.80%)	1580 (56.59%)	1480 (52.91%)
High school or equivalent	2478 (27.26%)	778 (25.83%)	815 (27.61%)	885 (28.40%)
Less than high school	2918 (18.26%)	1094 (20.38%)	897 (15.80%)	927 (18.68%)
Marital status, n (%)					<0.001
Having a partner	4952 (58.50%)	1819 (62.51%)	1673 (58.91%)	1460 (53.82%)
Without a partner	4925 (41.50%)	1474 (37.49%)	1619 (41.09%)	1832 (46.18%)
BMI, kg/m²	28.27 (24.45, 33.20)	28.10 (24.38, 33.22)	28.30 (24.36, 32.94)	28.40 (24.50, 33.37)	0.6
Systolic pressure, mmHg	127.33 (115.33, 141.33)	125.33 (114.67, 138.00)	126.00 (115.33, 139.33)	130.00 (116.67, 146.67)	<0.001
Diastolic pressure, mmHg	70.00 (63.00, 77.33)	71.33 (64.00, 78.00)	70.67 (64.00, 78.00)	68.67 (60.67, 76.67)	<0.001
CVD, n (%)	1609 (13.99%)	393 (10.35%)	427 (10.81%)	789 (21.26%)	<0.001
Hypertension, n (%)					<0.001
Yes	6506 (59.43%)	1954 (54.90%)	2080 (56.03%)	2472 (67.88%)
No	3371 (40.57%)	1339 (45.10%)	1212 (43.97%)	820 (32.12%)
Diabetes, n (%)					<0.001
Yes	2474 (18.68%)	845 (19.15%)	694 (15.07%)	935 (22.09%)
No	7403 (81.32%)	2448 (80.85%)	2598 (84.93%)	2357 (77.91%)
Smoking status, n (%)					<0.001
Current	1446 (15.62%)	629 (21.69%)	459 (14.20%)	358 (10.75%)
Former	2523 (27.82%)	777 (27.23%)	836 (27.93%)	910 (28.32%)
Never	5908 (56.56%)	1887 (51.08%)	1997 (57.88%)	2024 (60.93%)
Drinking status, n (%)					<0.001
Yes	7391 (80.61%)	2516 (83.26%)	2523 (82.49%)	2352 (75.78%)
No	2486 (19.39%)	777 (16.74%)	769 (17.51%)	940 (24.22%)
HDL-C, mmol/L	1.50 (1.22, 1.81)	1.42 (1.19, 1.76)	1.53 (1.24, 1.84)	1.50 (1.24, 1.84)	<0.001
TC, mmol/L	5.38 (4.68, 6.08)	5.46 (4.84, 6.13)	5.40 (4.78, 6.10)	5.20 (4.50, 5.95)	<0.001
Albumin, g/L	42.00 (40.00, 44.00)	42.00 (40.00, 44.00)	42.00 (40.00, 44.00)	42.00 (40.00, 44.00)	0.001
ALP, IU/L	72.00 (59.00, 87.00)	73.00 (60.00, 89.00)	71.00 (59.00, 86.00)	72.00 (59.00, 87.00)	0.045
AST, U/L	22.00 (19.00, 27.00)	21.00 (18.00, 25.00)	22.00 (19.00, 27.00)	23.00 (20.00, 28.00)	<0.001
ALT, U/L	19.00 (16.00, 24.00)	19.00 (15.00, 24.00)	19.00 (16.00, 25.00)	19.00 (15.00, 25.00)	0.059
LDH, U/L	139.00 (123.00, 159.00)	125.00 (112.00, 139.00)	140.00 (126.00, 155.00)	157.00 (140.00, 179.00)	<0.001
Total bilirubin, μmol/L	10.26 (6.84, 11.97)	8.60 (6.84, 11.97)	10.26 (6.84, 11.97)	10.26 (6.84, 11.97)	<0.001
Serum urea nitrogen, mmol/L	5.00 (3.93, 6.40)	4.60 (3.57, 5.40)	5.00 (3.93, 6.07)	6.07 (4.64, 7.50)	<0.001
Serum uric acid, μmol/L	297.40 (249.80, 350.90)	279.60 (232.00, 327.10)	291.50 (249.80, 339.00)	321.20 (267.70, 380.70)	<0.001
Creatinine, mg/dL	0.79 (0.68, 0.90)	0.69 (0.60, 0.77)	0.79 (0.70, 0.89)	0.92 (0.80, 1.10)	<0.001
HbA1c, %	5.60 (5.30, 5.90)	5.60 (5.30, 5.90)	5.60 (5.30, 5.90)	5.60 (5.40, 6.00)	<0.001
eGFR, mL/min/1.73m²	83.26 (67.64, 96.13)	95.46 (85.10, 104.06)	83.11 (71.12, 94.26)	65.91 (52.22, 80.03)	<0.001

median (IQR) for continuous; n (%) for categorical.

Design-based Kruskal–Wallis test; Pearson’s X²: Rao & Scott adjustment.

Bolded values denote p-values <0.05, indicating statistical significance.

LnEASIX: log-transformed endothelial activation and stress index; BMI: body mass index; CVD: cardiovascular diseases; TC: total cholesterol; HDL-C: high-density lipoprotein cholesterol; HbA1c: glycated hemoglobin; ALP: alkaline phosphatase; ALT: alanine aminotransferase; AST: aspartate aminotransferase; eGFR: estimated glomerular filtration rate; IQR: interquartile range.

Statistical analysis

Statistical analyses were conducted in the R statistical environment (version 4.4.1), using the ‘survey’ package to account for NHANES’s complex survey design, which incorporates sampling weights, stratification variables, and cluster designations, following CDC recommendations. To handle missing values in covariates, we used multiple imputation by chained equations (MICE), to ensure robust and reliable analyses. Baseline characteristics were initially summarized and compared across EASIX groups categorized into tertiles. The distribution of continuous variables was assessed using the Kolmogorov–Smirnov test. As most variables demonstrated significant deviations from normality, they were summarized as medians (interquartile range), and differences across EASIX tertiles were compared using the Kruskal–Wallis test. Detailed results of the Kolmogorov–Smirnov normality tests are presented in Table S1. Categorical variables were analyzed using weighted chi-square tests and were presented as counts and percentages. Additionally, we applied a logarithmic transformation to EASIX to approximate a normal distribution. Logistic regression was then applied to evaluate the association between log-transformed EASIX (LnEASIX) and CVD. Three models were constructed: Model 1 was unadjusted; Model 2 was adjusted for age and race; and Model 3 was further adjusted for marital status, education, BMI, smoking status, drinking status, diabetes, hypertension, HbA1c, AST, ALT, ALP, HDL-C, TC, albumin, serum UA, and serum urea nitrogen. To explore potential nonlinear relationships between LnEASIX and CVD, we performed restricted cubic spline (RCS) analysis. The threshold effect was subsequently identified by determining inflection points within a predefined range to optimize model likelihood. A two-piecewise logistic model was subsequently constructed to assess associations between LnEASIX and CVD on either side of the identified inflection point. Furthermore, we performed an exploratory mediation analysis using the ‘mediation’ package in R. Logistic regression models were fitted for the mediator (ePWV) and the outcome (CVD), with both models adjusted for race, education, marital status, drinking status, smoking status, BMI, hypertension, diabetes, HbA1c, HDL-C, TC, AST, ALT, ALP, albumin, serum UA, and serum urea nitrogen. Indirect, direct, and total effects as well as the proportion mediated were calculated. Statistical inference was based on nonparametric bootstrapping with 1000 resamples. We also carried out subgroup analyses across predefined subgroups, including age, race, BMI, education, marital status, smoking status, drinking status, diabetes, and hypertension, to investigate potential effect modifications. Finally, sensitivity analysis was conducted by excluding participants with missing covariates.

All statistical tests were two-tailed, and a significance threshold of p < 0.05 was applied.

Results

Study population

From an initial cohort of 55,081 individuals aged ≥20 years drawn from NHANES 1999–2018, 43,004 nonmenopausal participants were excluded, leaving 12,077 individuals. Subsequently, 73 participants lacking CVD data, 1837 participants with missing EASIX data, and 290 participants without ePWV data were excluded. This resulted in a final analytical sample of 9877 participants. The flow of participant selection is shown in Figure 1.

Figure 1.

Flowchart of participant selection for the present study.

Baseline characteristics

In this study, 9877 participants with a mean age of 62.00 years were included. Participants in the highest EASIX tertile (T3) were older and had higher SBP along with a greater prevalence of CVD, hypertension, and diabetes than those in the lower tertiles. Laboratory findings indicated that participants in T3 had lower TC and eGFR, whereas they had higher levels of LDH, serum urea nitrogen, serum UA, and creatinine. Table 1 provides a detailed summary of baseline characteristics by EASIX tertiles.

Association between EASIX and CVD

Results of the logistic regression analysis examining EASIX and CVD are presented in Table 2. In the fully adjusted model (Model 3), a one-unit increase in LnEASIX was significantly associated with higher odds of CVD (odds ratio (OR) = 1.62, 95% confidence interval (CI): 1.35–1.95). When stratified by tertiles, participants in the highest EASIX tertile (T3) had significantly greater odds of CVD than those in the lowest tertile (T1) (OR = 1.43, 95% CI: 1.17–1.74), whereas no significant association was observed for the middle tertile (T2). A significant dose–response trend was evident across ascending EASIX tertiles (P for trend <0.001). RCS analysis illustrated a nonlinear relationship between LnEASIX and CVD (P for nonlinearity <0.05; Figure 2). Using a two-piecewise logistic regression model, an inflection point was identified at LnEASIXof −0.97 (corresponding to 0.38 for EASIX) (Table 3). Below this threshold, LnEASIX was not significantly associated with CVD (OR = 0.71, 95% CI: 0.45–1.12). However, above the inflection point, each one-unit increase in LnEASIX was associated with a 2.16-fold increase in the odds of CVD (OR = 2.16, 95% CI: 1.71–2.72). The log-likelihood ratio test confirmed that the two-piecewise logistic model provided a significantly better fit than a standard linear model (P for log-likelihood ratio < 0.001).

Table 2.

Association of LnEASIX with CVD in menopause female.

	Model 1			Model 2			Model 3
Characteristic	OR	95% CI	p-value	OR	95% CI	p-value	OR	95% CI	p-value
Per unit	2.73	2.36, 3.14	<0.001	1.88	1.61, 2.19	<0.001	1.62	1.35, 1.95	<0.001
Group LnEASIX
T1 (−3.37 to 1.00)	Ref.	—	—	Ref.	—	—	Ref.	—	—
T2 (−1.00 to 0.63)	1.05	0.87, 1.27	0.611	0.87	0.72, 1.06	0.162	0.97	0.79, 1.18	0.738
T3 (−0.63 to 2.71)	2.34	1.97, 2.77	<0.001	1.53	1.27, 1.84	<0.001	1.43	1.17, 1.74	<0.001
p for trend			<0.001			<0.001			<0.001

Model 1: unadjusted.

Model 2: adjusted for age and race.

Model 3: adjusted for age, race, marital status, education, BMI, smoking status, drinking status, diabetes, hypertension, HbA1c, AST, ALT, ALP, HDL-C, TC, albumin, serum uric acid, and serum urea nitrogen.

Bolded values denote p-values <0.05, indicating statistical significance.

ALP: alkaline phosphatase; ALT: alanine aminotransferase; AST: aspartate aminotransferase; BMI: body mass index; CI: confidence interval; CVD: cardiovascular diseases; LnEASIX: log-transformed endothelial activation and stress index; HbA1c: glycated hemoglobin; HDL-C: high-density lipoprotein cholesterol; OR: odds ratio; TC: total cholesterol; Ref: reference.

Figure 2.

Association between EASIX and CVD risk modeled using RCS, adjusted for all covariates.

Table 3.

Threshold effect analysis of LnEASIX in menopause female.

	Adjusted OR, (95% CI), p-value
Fitting by the standard linear model	1.62 (1.35, 1.95) <0.001
Fitting by the two-piecewise linear model
Infection point	−0.97
LnEASIX ≤ −0.97	0.71 (0.45, 1.12) 0.138
LnEASIX > −0.97	2.16 (1.71, 2.72) <0.001
p for log-likelihood ratio	<0.001

Adjusted for age, race, marital status, education, BMI, smoking status, drinking status, diabetes, hypertension, HbA1c, AST, ALT, ALP, HDL-C, TC, albumin, serum uric acid, and serum urea nitrogen.

Bolded values denote p-values <0.05, indicating statistical significance.

ALP: alkaline phosphatase; ALT: alanine aminotransferase; AST: aspartate aminotransferase; BMI: body mass index; CI: confidence interval; LnEASIX: log-transformed endothelial activation and stress index; HbA1c: glycated hemoglobin; HDL-C: high-density lipoprotein cholesterol; OR: odds ratio; TC: total cholesterol.

Mediation analysis

Exploratory mediation analysis indicated that ePWV accounted for 4.82% of the association between EASIX and CVD (Figure 3).

Figure 3.

Mediation analysis depicting the role of ePWV in the association between EASIX and CVD risk.

Subgroup analysis

Subgroup analyses were conducted to assess the association between LnEASIX and CVD across various populations (Figure 4). A significant interaction was observed for diabetes (P for interaction < 0.05), with a stronger association among participants with diabetes than those without diabetes. No significant interactions were observed for other subgroups, and the association between LnEASIX and CVD remained generally consistent across strata.

Figure 4.

Subgroup analyses of the association between EASIX and CVD risk. CVD: cardiovascular disease; EASIX: endothelial activation and stress index.

Sensitivity analysis

After excluding participants with missing covariates, we found that the positive association between LnEASIX and CVD remained significant (Table S2).

Discussion

This cross-sectional study included 9877 participants representing the US civilian population and analyzed NHANES data from 1999 to 2018 to examine the association between EASIX and CVD prevalence in postmenopausal women. We found that higher EASIX values were significantly associated with increased CVD prevalence. RCS analysis indicated a nonlinear relationship, with CVD risk rising sharply when EASIX exceeded the threshold. This positive association was partially mediated by ePWV, which accounted for 4.82% of the observed relationship. The association remained robust across subgroups, including participants with diabetes, hypertension, or unhealthy lifestyle factors such as alcohol consumption.

EASIX comprising three routinely measured laboratory parameters, including LDH, creatinine, and platelets, was originally developed to assess endothelial injury following hematopoietic stem cell transplantation.^20,26 Although its role in onco-hematology is well-established, our study broadens its application to population-level cardiovascular health.^27,28 Endothelial dysfunction is a recognized early pathological basis for cardiovascular diseases, including atherosclerosis and coronary heart disease.²⁹ Estrogen exerts vasodilatory, anti-inflammatory, and antioxidant effects, thereby protecting the vascular endothelium.^30,31 Following menopause, the decline in estrogen levels reduces endothelial protection, contributing to the increased CVD risk.^32,33 Mechanistically, endothelial cell injury and death are associated with elevated LDH levels, a well-known marker of cellular damage,³⁴ while microthrombus formation consumes platelets, lowering platelet counts. Concurrently, microvascular thrombosis in the kidney reduces glomerular filtration and increases creatinine retention. Collectively, these changes increase EASIX and reflect endothelial dysfunction, thereby linking it to CVD risk.

The decline in estrogen levels may elevate EASIX through several pathways. First, the loss of antioxidant effects, such as reduced upregulation of superoxide dismutase (SOD) and glutathione peroxidase (GPx), results in excessive accumulation of reactive oxygen species (ROS).³⁵ These molecules damage endothelial membranes, proteins, and DNA, inducing apoptosis or necrosis and releasing LDH. Second, diminished upregulation of NO and prostaglandin 12 (PGI2),³⁶ combined with increased endothelial expression of tissue factor (TF), shifts the hemostatic balance toward a procoagulant state, resulting in platelet consumption. This observation is consistent with evidence from Denorme et al.³⁷ and aligns with studies demonstrating a hypercoagulable state in postmenopausal women.³⁸ Third, systemic coagulation and endothelial activation particularly affect the glomeruli, where microthrombi obstruct capillaries, resulting in impaired renal filtration and elevated creatinine. Collectively, these mechanisms explain the increase in EASIX after menopause.

To further elucidate the mechanism linking EASIX to cardiovascular disease, we incorporated ePWV, a widely used indicator of arterial stiffness. This approach is biologically plausible because endothelial activation and stress, as captured by EASIX, contribute to chronic inflammation, oxidative stress, and microvascular dysfunction. These processes disrupt vascular elastic fibers, promote fibrosis, and accelerate stiffening, ultimately manifesting as increased ePWV.^39–41 Accordingly, EASIX may be considered an upstream driver of arteriosclerosis. Prior studies have consistently demonstrated that ePWV is a strong, independent predictor of cardiovascular events and mortality.^42–44 Arterial stiffness elevates SBP, increases cardiac afterload, and impairs coronary perfusion, thereby promoting heart failure, MI, and stroke.³¹ Our mediation analysis indicated that ePWV accounted for a statistically significant proportion of the association between EASIX and CVD risk, suggesting that arterial stiffness represents a relevant component of this relationship. Although not implying strict causal mediation, these findings indicate that endothelial cell–level stress reflected by EASIX and arterial stiffening may represent interconnected vascular processes contributing to cardiovascular disease.

This study has several strengths. First, we analyzed data from the large-scale, nationally representative NHANES database, which provided strong statistical power and ensured broad generalizability. Second, comprehensive adjustments were made for potential confounding factors, including demographic, clinical, and laboratory parameters, thereby enhancing the validity of our results. Third, detailed subgroup analyses demonstrated a consistent association across different population strata, supporting the robustness of EASIX as a prognostic marker. Fourth, we explored potential mechanisms linking EASIX to CVD and demonstrated partial mediation through ePWV. Finally, we employed a variety of analytical methods, including RCS analysis, to detect important nonlinear relationships that may be overlooked by conventional linear models.

This study also has several limitations. First, the mediation analyses were based on cross-sectional data; therefore, observed mediation should be interpreted as statistical rather than causal. Second, despite adjusting for numerous demographic, clinical, and laboratory factors, residual confounding from unmeasured variables may remain. Third, CVD and menopausal status were based on self-reported physician diagnoses rather than adjudicated events, which may have introduced recall bias and misclassification. Fourth, the two main variables were not gold-standard measurements: EASIX was derived from routine laboratory markers rather than direct endothelial function tests, and arterial stiffness was assessed using ePWV rather than carotid–femoral PWV. Fifth, EASIX and other laboratory indices were measured at a single time point and may not reflect long-term vascular changes. Additionally, NHANES lacks detailed data on CVD progression and severity; prospective studies using clinically adjudicated outcomes and gold-standard vascular assessments are warranted to confirm our findings.

Finally, this study specifically focused on postmenopausal women, a population at uniquely elevated cardiovascular risk. Future research is warranted to directly compare EASIX–CVD associations across sex and menopausal status to further clarify potential sex- and hormone-related differences.

Conclusion

This study demonstrates that higher EASIX is significantly associated with increased CVD risk in postmenopausal women, with ePWV statistically accounting for a portion of this association. These findings highlight EASIX as a practical marker for CVD risk stratification. Prospective studies are needed to validate these results and to explore interventions targeting endothelial health to mitigate cardiovascular risk.

Supplemental Material

sj-pdf-1-imr-10.1177_03000605261434398 - Supplemental material for Endothelial activation and stress index links to cardiovascular disease in postmenopausal women mediated by arterial stiffness: A cross-sectional study of National Health and Nutrition Examination Survey 1999–2018

Supplemental material, sj-pdf-1-imr-10.1177_03000605261434398 for Endothelial activation and stress index links to cardiovascular disease in postmenopausal women mediated by arterial stiffness: A cross-sectional study of National Health and Nutrition Examination Survey 1999–2018 by Huidong Long, Ruoyue Guo, Jingyu Zhou, Guancheng Liu, Hanrui Chen and Yunen Lin in Journal of International Medical Research

Footnotes

Acknowledgments

We acknowledge the NHANES database for providing the platform and contributors for uploading valuable datasets. We also thank all participants included in this study.

Authorship contribution statement

Huidong Long: Conceptualization, Methodology, Software, Writing-Original draft preparation. Ruoyue Guo: Data curation, Writing- Original draft preparation. Jingyu Zhou: Visualization, Investigation. Guancheng Liu: Data curation, Visualization. Hanrui Chen: Software, Validation. Yunen Lin: Supervision.

Availability of data and materials

The dataset supporting the conclusions of this article is available from the NHANES repository: .

Clinical trial number

Not applicable.

Consent for publication

Not applicable.

Declaration of conflicting interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Ethics approval and consent to participate

Data collection for NHANES was approved by the NCHS Research Ethics Review Board (ERB). An individual investigator utilizing the publicly available NHANES data do not need to file the Institutional Review Board (IRB).

Funding

This work was supported by the Traditional Chinese Medicine Bureau Research Project of Guangdong Province, China (No. 20232116); the Science and Technology Program of the Guangdong Health Commission (No. A2023226); and the Guangdong Medical Association Clinical Research Special Fund Project (No. A202301018).

Supplemental material

Supplemental material for this article is available online.

ORCID iD

Yunen Lin

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