Association between the monocyte-to-high-density lipoprotein cholesterol ratio and abdominal aortic calcification in adults: Insights from the National Health and Nutrition Examination Survey (2013

Abstract

Background

This cross-sectional study aimed to investigate the association between the monocyte-to-high-density lipoprotein cholesterol ratio and abdominal aortic calcification in adults aged ≥40 years in the United States using data from the 2013–2014 National Health and Nutrition Examination Survey.

Method

Multivariable logistic regression and restricted cubic splines models were used to assess the association between the monocyte-to-high-density lipoprotein cholesterol ratio and abdominal aortic calcification.

Results

Among 2665 participants, 800 were diagnosed with abdominal aortic calcification. Multivariable logistic regression analysis revealed a positive association between the monocyte-to-high-density lipoprotein cholesterol ratio and abdominal aortic calcification (odds ratio: 1.33; 95% confidence interval: 1.13–1.57), and a nonlinear dose–response relationship was observed. Subgroup analysis suggested that the relationship between the monocyte-to-high-density lipoprotein cholesterol ratio and abdominal aortic calcification was more pronounced in females and nonsmokers.

Conclusions

The monocyte-to-high-density lipoprotein cholesterol ratio was positively associated with the prevalence of abdominal aortic calcification among individuals aged ≥40 years. A persistently elevated monocyte-to-high-density lipoprotein cholesterol ratio may contribute to an increased burden of abdominal aortic calcification.

Keywords

Monocyte-to-high-density lipoprotein cholesterol ratio abdominal aortic calcification National Health and Nutrition Examination Survey cardiovascular disease oxidative stress

Introduction

Abdominal aortic calcification (AAC) is a prevalent vascular condition characterized by ectopic calcification within the abdominal aortic wall, representing a significant marker of atherosclerotic disease.¹ AAC is intricately linked to the incidence and progression of cardiovascular diseases and serves as an independent predictor of cardiovascular events and overall mortality, highlighting its importance in assessing the atherosclerotic burden.² Evidence suggests a robust correlation between AAC and various metabolic disorders, including diabetes and hypertension, underscoring its role in systemic metabolic dysregulation.^3,4

Inflammatory and oxidative stress pathways are pivotal in the etiology and progression of atherosclerosis and AAC.⁵ Inflammatory cells, especially monocytes and macrophages, are central to the formation, evolution, and rupture of atherosclerotic plaques. Monocytes migrate to the arterial intima, differentiate into macrophages, and engulf oxidized low-density lipoprotein, leading to foam cell formation and plaque instability.⁶ Oxidative stress exacerbates atherosclerosis by impairing endothelial function, promoting smooth muscle cell proliferation and migration, and inducing apoptosis while also activating inflammatory cascades.⁷ This dual role of oxidative stress in direct vascular damage and inflammation amplification is well-established.⁸

Studies have shown that the monocyte-to-high-density lipoprotein cholesterol ratio (MHR), an emerging biomarker, is correlated with inflammatory and oxidative stress responses.^9,10 High-density lipoprotein cholesterol (HDL-C) exhibits anti-inflammatory and antioxidant properties and facilitates reverse cholesterol transport, thereby inhibiting monocyte migration and the release of inflammatory mediators.¹¹ Under inflammation and oxidative stress, monocyte counts tend to escalate, and HDL-C functionality is compromised, resulting in an increased MHR. The potential of MHR as a risk assessment tool for atherosclerosis and cardiovascular diseases is gaining recognition, with associations being established with coronary artery disease and chronic heart failure.¹²

Research elucidating the relationship between MHR and AAC is limited, with existing studies focusing on specific populations or small cohorts, and there is a dearth of large-scale, nationally representative data. The National Health and Nutrition Examination Survey (NHANES), with its comprehensive health and nutrition indicators, offers a valuable dataset for investigating the association between MHR and AAC. Although a recent study investigated the sex-specific association between MHR and extensive AAC, it was based on a smaller, non-nationally representative cohort and did not account for nonlinear associations or interaction effects beyond sex. This study leverages the 2013–2014 NHANES data to explore the association between MHR and AAC, aiming to provide novel insights for early detection and intervention strategies. Understanding the relationship between MHR and AAC is crucial for elucidating the pathophysiological mechanisms of inflammation and oxidative stress in atherosclerosis and may yield new biomarkers and therapeutic targets for cardiovascular disease prevention and management, thereby holding significant clinical and research implications.

Materials and methods

Study population

The NHANES is an extensive research initiative conducted annually to evaluate the health and nutritional status of a representative sample of approximately 5000 adults living in the United States. This survey collects a diverse array of data, including demographic information, dietary habits, results from physical examinations, laboratory analysis data, and responses to questionnaires. Importantly, all NHANES data are freely accessible to the public, thereby promoting transparency and facilitating their use for research and public health policy development.

Based on the availability of AAC data from physical examinations, this study utilized the 2013–2024 NHANES data to investigate the relationship between MHR and AAC. Initially, the dataset included 10,175 participants, of whom 6360 individuals aged ≥40 years were selected. Subsequently, participants with no information on MHR (n = 91) or AAC (n = 675) were excluded. AAC data in NHANES were only available for participants aged ≥40 years. Therefore, age ≥40 years was adopted as an inclusion criterion based on data availability. To address potential confounding factors, age was included as a continuous covariate in all multivariable regression models. Additionally, 384 individuals were excluded due to missing data on covariates, including education level, body mass index (BMI), alcohol consumption, smoking status, hypertension, diabetes, and laboratory test results. Finally, 2665 participants were included in the analysis. The study population selection criteria are detailed in Figure 1.

Figure 1.

Flowchart of participant selection. NHANES: National Health and Nutrition Examination Survey; MHR: monocyte-to-high-density lipoprotein cholesterol ratio; AAC: abdominal aortic calcification.

AAC evaluations

The Kauppila AAC score is computed utilizing dual-energy X-ray absorptiometry scans of the lateral lumbar spine.¹³ This score serves as an indicator of AAC severity, with elevated scores corresponding to increased calcification. The scoring system partitions the abdominal aortic wall into four sequential sections, each aligned with the L1–L4 vertebral regions. Each section is scored on a scale of 1–6, reflecting the degree of calcium deposition. The sum of these section scores is used to calculate the final AAC score, which ranges from 0 to 24. A total AAC score of >0 indicates the presence of AAC. This threshold has been consistently applied in previous research studies to quantify abdominal aortic calcification.¹⁴

MHR measurements

MHR was formulated as an exposure variable, calculated as the ratio of monocyte counts (in units of cells/µL) to the HDL-C level (mmol/L). Complete blood count measurements were performed using a Beckman Coulter DxH 800 instrument, which provided a detailed profile of blood cell distribution for each participant. HDL-C levels were assessed using a specific endpoint reaction, with the resultant product measured photometrically at a wavelength of 600 nm.

Covariate assessment

A multivariable adjustment model was developed to account for potential confounding factors and assess their influence on the association between MHR and AAC. Initially, demographic variables such as sex, age, race, and educational attainment were incorporated into the model. Subsequently, physical examination data, specifically body mass index (BMI), were included. Additionally, questionnaire-derived data pertaining to smoking habits, alcohol consumption, hypertension, and diabetes were integrated. Alcohol consumption was defined as the intake of any alcoholic beverage at least once a month over the past 12 months. Individuals who had smoked more than 100 cigarettes in their lifetime were classified as smokers. Medical comorbidities, including diabetes and hypertension, were confirmed through self-reported histories obtained via questionnaires. Finally, serum samples were transported to a central laboratory for analysis to determine the levels of total 25-hydroxyvitamin D, serum creatinine, alanine aminotransferase (ALT), aspartate aminotransferase (AST), serum calcium, serum phosphorus, serum uric acid, glycated hemoglobin (HbA1c), total cholesterol, blood urea nitrogen (BUN), and serum vitamin B12.

Statistical analyses

Based on the aggregated dataset from 2013 to 2014, sample weights were utilized to calculate national estimates, thereby enhancing the dataset’s reliability. Participants were categorized into two groups based on the presence of AAC. Owing to the skewed distribution of MHR, a log2 transformation was applied, and MHR was subsequently analyzed as a continuous variable. Continuous variables were reported as mean ± standard deviation, while categorical variables were presented as frequencies and percentages to provide a comprehensive perspective. To assess the differences in the distribution and categorization of variables, the Student’s t-test or chi-squared test was employed. A multiple logistic regression model was used to examine the association between MHR and AAC across three distinct models. Model 1 was not adjusted for covariates, Model 2 was adjusted for demographic data, and Model 3 was adjusted for all covariates. Additionally, restricted cubic splines regression with three knots was employed to investigate potential nonlinear associations between MHR and AAC. The likelihood ratio test was used to evaluate nonlinearity. To further explore the associations between MHR and AAC in different populations, subgroup analyses were conducted based on age, sex, race, alcohol consumption (yes/no), smoking status (yes/no), hypertension (yes/no), diabetes (yes/no), education level, and body mass index (BMI). The significance of interactions was assessed using P-values for the interaction coefficients between MHR and subgroup populations. Statistical analyses were performed using R software version 4.3.2 and EmpowerStats version 4.2, with statistical significance set at P <0.05.

Results

Baseline characteristics of the study population

Following the abovementioned selection criteria, this study included 2665 individuals, with 52.16% of them being female and 47.84% being male. The baseline characteristics of the study participants are illustrated in Table 1. Depending on the presence of AAC, the participants were divided into two groups—AAC formers (n = 800) and non-AAC formers (n = 1865). Notably, AAC formers had a significantly higher MHR than non-AAC formers (0.49 ± 0.25 vs. 0.45 ± 0.24). Compared with non-AAC formers, AAC formers tended to be older, be non-Hispanic white, have a BMI of 25–30, be smokers, and have hypertension and diabetes (P < 0.001). Furthermore, the levels of various biochemical parameters, including 25-hydroxyvitamin D, serum creatinine, serum uric acid, HbA1c, and BUN, were elevated in AAC formers (P < 0.05). Conversely, this group demonstrated lower levels of cholesterol than non-AAC formers (P < 0.05).

Table 1.

Weighted characteristics of the study population.

Characteristics	Overall(N = 2665)	Non-AAC(N = 1865)	AAC(N = 800)	P-value
Sex, n (%)				0.462
Male	1275 (47.84%)	883 (47.35%)	392 (49.00%)
Female	1390 (52.16%)	982 (52.65%)	408 (51.00%)
Age (years)	58.88 ± 11.96	56.19 ± 10.99	65.15 ± 11.79	0.001
Race/ethnicity, n (%)				0.001
Mexican American	351 (13.17%)	269 (14.42%)	82 (10.25%)
Other Hispanic	256 (9.61%)	192 (10.29%)	64 (8.00%)
Non-Hispanic White	1215 (45.59%)	773 (41.45%)	442 (55.25%)
Non-Hispanic Black	503 (18.87%)	388 (20.80%)	115 (14.38%)
Other race/multiracial	340 (12.76%)	243 (13.03%)	97 (12.12%)
Education level, n (%)				0.073
<High School	579 (21.73%)	402 (21.55%)	177 (22.12%)
High School	602 (22.59%)	401 (21.50%)	201 (25.12%)
>High School	1484 (55.68%)	1062 (56.94%)	422 (52.75%)
BMI (kg/m²) (%)				0.001
<25	747 (28.03%)	509 (27.29%)	238 (29.75%)
25–30	996 (37.37%)	658 (35.28%)	338 (42.25%)
>30	922 (34.60%)	698 (37.43%)	224 (28.00%)
Smoking status (%)				0.001
Yes	1234 (46.30%)	796 (42.68%)	438 (54.75%)
No	1431 (53.70%)	1069 (57.32%)	362 (45.25%)
Alcohol consumption (%)				0.719
Yes	1912 (71.74%)	1334 (71.53%)	578 (72.25%)
No	753 (28.26%)	531 (28.47%)	222 (27.75%)
Diabetes (%)				0.001
Yes	461 (17.30%)	284 (15.23%)	177 (22.12%)
No	2204 (82.70%)	1581 (84.77%)	623 (77.88%)
Hypertension (%)				0.001
Yes	1257 (47.17%)	771 (41.34%)	486 (60.75%)	0.001
No	1408 (52.83%)	1094 (58.66%)	314 (39.25%)
25-hydroxyvitamin D (nmol/L)	70.71 ± 29.14	68.80 ± 28.12	75.17 ± 30.96	0.001
Serum creatinine (mg/dL)	0.95 ± 0.54	0.92 ± 0.42	1.02 ± 0.75	0.001
AST (U/L)	25.52 ± 14.42	25.31 ± 13.42	26.00 ± 16.52	0.293
ALT (U/L)	24.49 ± 18.52	24.61 ± 16.13	24.19 ± 23.15	0.635
Serum calcium (mg/dL)	9.45 ± 0.37	9.44 ± 0.37	9.48 ± 0.36	0.006
Serum phosphorus (mmol/L)	1.22 ± 0.18	1.22 ± 0.18	1.23 ± 0.19	0.791
Serum uric acid (mg/dL)	5.45 ± 1.38	5.38 ± 1.37	5.59 ± 1.40	0.001
Glycated hemoglobin (%)	5.90 ± 1.14	5.85 ± 1.14	6.03 ± 1.11	0.001
Cholesterol (mg/dL)	195.66 ± 42.32	197.35 ± 41.71	191.74 ± 43.49	0.002
BUN (mmol/L)	5.12 ± 2.20	4.90 ± 1.92	5.63 ± 2.68	0.001
Serum vitamin B12 (pg/mL)	689.60 ± 944.18	670.79 ± 805.28	733.42 ± 1206.96	0.179
MHR	0.46 ± 0.24	0.45 ± 0.24	0.49 ± 0.25	0.001
HDL cholesterol (mg/dL)	54.21 ± 16.84	54.68 ± 17.29	53.14 ± 15.69	0.031
Monocyte number (1000 cells/µL)	0.58 ± 0.20	0.57 ± 0.19	0.61 ± 0.22	<0.001

AAC: abdominal aortic calcification; MHR: monocyte-to-high-density lipoprotein cholesterol ratio; BMI: body mass index; AST: aspartate aminotransferase; ALT: alanine aminotransferase; BUN: blood urea nitrogen; HDL: high-density lipoprotein.

Associations between MHR and AAC

Table 2 demonstrates the association between MHR and the risk of AAC. Across all models, log2-transformed MHR was significantly associated with an increased risk of AAC. In the unadjusted model (Model 1), each one-unit increase in log2-transformed MHR was linked to a 35% higher risk of AAC (odds ratio (OR) = 1.35, 95% confidence interval (CI): 1.20–1.53, P < 0.001). In Model 2, which was adjusted for age, sex, race, and education levels, the risk increased by 33% (OR = 1.33, 95% CI: 1.15–1.52, P < 0.001). Similarly, in Model 3, which was further adjusted for BMI, smoking, alcohol consumption, diabetes, hypertension, 25-hydroxyvitamin D, serum creatinine, AST, ALT, serum calcium, serum phosphorus, serum uric acid, HbA1c, cholesterol, BUN, and serum vitamin B12, the risk remained elevated at 33% (OR = 1.33, 95% CI: 1.13–1.57, P < 0.001).

Table 2.

Associations between MHR and the risk of AAC.

Characteristics	Model 1^a: OR (95% CI)	Model 2^b: OR (95% CI)	Model 3^c: OR (95% CI)
Log2-transformed MHR	1.35 (1.20–1.53)	1.33 (1.15–1.52)	1.33 (1.13–1.57)
MHR tertiles
Tertile 1	Reference	Reference	Reference
Tertile 2	1.51 (1.23–1.87)	1.52 (1.21–1.91)	1.53 (1.20–1.95)
Tertile 3	1.69 (1.37–2.08)	1.68 (1.32–2.12)	1.69 (1.29–2.22)
P for trend	<0.001	<0.001	<0.001

MHR: monocyte-to-high-density lipoprotein cholesterol ratio; AAC: abdominal aortic calcification; OR: odds ratio; CI: confidence interval; BMI: body mass index; AST: aspartate aminotransferase; ALT: alanine aminotransferase; BUN: blood urea nitrogen.

Model 1 was not adjusted for any covariates.

Model 2 was adjusted for age, sex, race, and education level.

Model 3 was further adjusted for BMI, smoking, alcohol, diabetes, hypertension, 25-hydroxyvitamin D, serum creatinine, AST, ALT, serum calcium, serum phosphorus, serum uric acid, glycated hemoglobin, cholesterol, BUN, and serum vitamin B12.

When stratified by MHR tertiles, the AAC risk was higher in Tertiles 2 and 3 than in Tertile 1 (reference group). In Model 1, the ORs for Tertiles 2 and 3 were 1.51 (95% CI: 1.23–1.87) and 1.69 (95% CI: 1.37–2.08), respectively. In Models 2 and 3, these estimates remained statistically significant with slight variations (P < 0.001).

These findings suggest that MHR is a potential independent risk factor for AAC. Even after adjusting for numerous confounding variables, higher MHR levels consistently demonstrated a strong and significant association with an increased risk of AAC.

Nonlinear correlation between MHR and AAC

Figure 2 presents a restricted cubic splines analysis illustrating the association between MHR levels and the risk of AAC, adjusted for all covariates. The horizontal axis denotes MHR levels, while the vertical axis indicates the ORs for AAC. The red curve represents the nonlinear relationship, with the shaded pink area marking the 95% CI.

Figure 2.

Restricted cubic splines analysis of the association between MHR and AAC, adjusted for all covariates. MHR: monocyte-to-high-density lipoprotein cholesterol ratio; AAC: abdominal aortic calcification.

The analysis revealed a significant nonlinear association (P for nonlinearity <0.001) between MHR and AAC risk. In particular, the AAC risk increases with rising MHR levels, peaking at an MHR of approximately 0.4, followed by a gradual decline as MHR levels continue to rise. Despite the decline, the OR remained above the reference value (OR = 1.0) for most of the observed range.

Furthermore, the overall association is statistically significant (P for overall <0.001), confirming that MHR is independently associated with the AAC risk. These findings highlight the complex and nonlinear nature of this relationship, emphasizing the need for further investigation and careful consideration in clinical practice and research.

Subgroup and sensitivity analyses

Figure 3 presents the subgroup analysis of the association between MHR and AAC, adjusted for various covariates. The analysis revealed significant heterogeneity in the relationship between the primary variable and outcome across different subgroups. Specifically, a stronger association was observed in females (OR: 2.88, 95% CI: 1.31–6.33), with a significant interaction effect (P for interaction = 0.002). Nonsmokers demonstrated a significantly higher OR (OR: 2.50, 95% CI: 1.16–5.36), with smoking status acting as a significant modifier of the relationship (P for interaction = 0.011). A notable association between MHR and AAC was also observed in individuals with a BMI of 25–30 (OR: 2.20, 95% CI: 1.07–4.51), although the interaction with BMI did not reach statistical significance (P for interaction = 0.782). No significant associations were observed for age, race, alcohol consumption, hypertension, diabetes, or education level (P for interaction >0.05).

Figure 3.

Subgroup analysis of the association between MHR and AAC. Each stratification was adjusted for age (continuous), sex, race, education levels, BMI (continuous), smoking status, alcohol consumption, diabetes, hypertension, 25-hydroxyvitamin D, serum creatinine, AST, ALT, serum calcium, serum phosphorus, serum uric acid, glycated hemoglobin, cholesterol, BUN, and serum vitamin B12. MHR: monocyte-to-high-density lipoprotein cholesterol ratio; AAC: abdominal aortic calcification; BMI: body mass index; AST: aspartate aminotransferase; ALT: alanine aminotransferase; BUN: blood urea nitrogen; OR: odds ratio.

Discussion

This cross-sectional study provides compelling evidence regarding a significant association between the MHR and the prevalence of AAC in individuals aged ≥40 years. Our findings underscore the potential of MHR as an accessible and effective biomarker for identifying individuals at an elevated risk of AAC, particularly in clinical settings where routine blood tests are commonly performed. The restricted cubic splines analysis demonstrated a significant nonlinear relationship between MHR and AAC risk, indicating that lower MHR levels confer a protective effect, while elevated MHR levels are associated with an increased risk of AAC. This suggests the need for clinical strategies focused on managing MHR levels, particularly in at-risk populations, aligning with findings from previous studies on inflammatory biomarkers in vascular calcification.^15,16

Our subgroup analyses further highlighted the robustness of the MHR–AAC relationship, particularly in females and nonsmokers. The stronger association in females is consistent with that reported in previous studies, suggesting that sex hormones, particularly estrogen, modulate inflammatory responses and lipid metabolism in women, contributing to differential disease outcomes.¹⁷ Additionally, smoking increases oxidative stress and systemic inflammation, which can accelerate vascular calcification.^18,19 These findings indicate the importance of considering both biological sex and lifestyle factors when evaluating the utility of MHR as a clinical biomarker. Previous studies have identified MHR as a significant inflammatory biomarker in various cardiovascular settings. Elevated MHR levels have been shown to correlate with poor outcomes in coronary artery disease, myocardial infarction, and heart failure, reflecting underlying inflammatory and oxidative stress mechanisms.^9,10,12 Importantly, recent evidence has begun to reveal the potential relevance of MHR in structural vascular changes, such as vascular stiffness and calcification. For instance, Erden et al.¹⁵ revealed that higher MHR was associated with increased carotid intima-media thickness in postmenopausal women, a surrogate marker of early vascular calcification. Additionally, Tacke et al.¹⁶ emphasized the critical role of monocyte-derived inflammation in promoting vascular calcification through smooth muscle cell transition and matrix mineralization. Despite these insights, direct investigations into the relationship between MHR and AAC remain scarce. Therefore, our study provides novel epidemiological evidence supporting the hypothesis that MHR may serve as an accessible surrogate biomarker for vascular calcification risk, particularly AAC.

Mechanisms linking MHR and AAC

MHR reflects the balance between the proinflammatory monocyte count and the anti-inflammatory, atheroprotective HDL-C, both of which play pivotal roles in vascular health. Monocytes, as central players in inflammation, can differentiate into macrophages and foam cells, which contribute to the initiation and progression of atherosclerotic lesions, a key component of AAC.^18,20 Additionally, HDL-C possesses anti-inflammatory and antioxidant properties, which help counteract the effects of oxidative stress and vascular calcification.^16,21 Therefore, an elevated MHR indicates a state of chronic low-grade inflammation, characterized by an increased monocyte count and impaired HDL function, both of which are critical to the pathogenesis of vascular calcification.²²

Our findings align with those of previous research on the link between inflammation and AAC. For instance, Pan et al. demonstrated that moderate levels of inflammation accelerate the progression of vascular calcification, while more severe inflammatory responses may trigger compensatory mechanisms that mitigate the progression of calcification. This finding may explain the nonlinear relationship observed in our dose–response analysis, where the AAC risk remained stable at lower MHR levels but significantly decreased at higher MHR levels. This indicates a threshold effect, where moderate inflammation accelerates vascular calcification; however, at higher levels, the inflammatory response may evolve into a protective mechanism that halts further progression.²³ Additionally, higher levels of oxidative stress in individuals with elevated MHR likely contribute to vascular smooth muscle cell transformation, a key step in vascular calcification.²⁴

Moreover, oxidative stress plays a central role in vascular calcification. It has been well-established that increased oxidative stress is closely related to endothelial dysfunction, vascular smooth muscle cell transformation, and accumulation of calcium deposits in the arterial wall.^20,21 In this regard, MHR, as a marker of systemic inflammation, may reflect an underlying oxidative burden that predisposes individuals to accelerated vascular calcification. Thus, interventions targeting oxidative stress and inflammation could have significant therapeutic potential in reducing the AAC burden, particularly in individuals with elevated MHR levels. This hypothesis is supported by studies that demonstrate the efficacy of antioxidants and anti-inflammatory therapies in reducing vascular calcification.^19,21

The significant association between MHR and AAC has important clinical implications. MHR can be readily calculated using data from routine blood tests, making it an easily accessible and cost-effective tool for early identification of individuals at risk of AAC. This is particularly relevant because AAC is a recognized predictor of adverse cardiovascular outcomes, including increased morbidity and mortality.^19,25 Early detection of individuals with elevated MHR could enable the implementation of targeted interventions aimed at reducing the inflammatory burden and slowing the progression of vascular calcification. The use of MHR as a screening tool is supported by similar studies, which identified its predictive value in cardiovascular disease and other vascular conditions.^16,20

Lifestyle modifications, including dietary changes, physical activity, and smoking cessation, may reduce the inflammatory state and potentially lower MHR levels. Pharmacological interventions, such as statins, anti-inflammatory drugs, and agents that modulate oxidative stress (e.g. antioxidants), may also play a role in mitigating the progression of AAC in high-risk individuals. Furthermore, the use of MHR in conjunction with other established risk factors for cardiovascular disease (e.g. hypertension, high cholesterol levels, and diabetes) could enhance the accuracy of risk stratification models and guide more personalized treatment approaches.^17,24

Our study sets the stage for future research aimed at elucidating the underlying mechanisms linking MHR to AAC. Understanding the specific pathways through which monocytes, HDL-C, and inflammatory mediators contribute to vascular calcification could provide new therapeutic targets. For instance, cytokines such as interleukin-6 and tumor necrosis factor-alpha, which are upregulated under inflammation, promote vascular calcification.^21,23 Future studies should investigate the role of these inflammatory markers in modulating AAC as well as the potential interactions between MHR and other risk factors such as hypertension and diabetes, which are known to independently contribute to vascular calcification.^19,22

Additionally, longitudinal studies are essential to establish causality between MHR and AAC. Our cross-sectional study provides evidence regarding an association between MHR and AAC; however, further research is needed to determine whether elevated MHR levels precede the development of AAC or whether they are merely a consequence of pre-existing disease. Long-term cohort studies, such as those by Wang et al., will help clarify whether reducing MHR levels through lifestyle or pharmacological interventions can reduce the risk of AAC and improve cardiovascular outcomes.²⁶ This would provide a stronger foundation for implementing MHR as a clinical biomarker for cardiovascular disease. Compared with previous studies, our analysis has several strengths that enhance its novelty. First, the use of nationally representative NHANES data enables broader generalizability. Second, we applied restricted cubic splines modeling to capture the nonlinear associations between MHR and AAC. Third, we conducted extensive subgroup and interaction analyses across demographic and clinical factors beyond sex alone. These methodological advancements improve the clinical applicability and interpretability of our findings.

Furthermore, the generalizability of our findings to diverse populations warrants consideration. Although our study utilized data from the NHANES cohort, which is primarily representative of the US population, further studies should validate the MHR–AAC association in different ethnic and geographic settings. The study by Zhu et al. suggests that inflammatory profiles differ between populations, and it is crucial to determine whether MHR holds similar predictive value across diverse demographic groups.²⁷ This will help refine the clinical utility of MHR as a global biomarker for vascular health.

Several limitations should be acknowledged in interpreting the results of our study. First, the cross-sectional nature of our design limits our ability to draw conclusions about causality. Although the association between MHR and AAC is evident, it remains unclear whether elevated MHR is a precursor to AAC or whether it represents a marker of established disease. Longitudinal studies are required to establish a temporal relationship between MHR levels and the onset of AAC. Second, despite adjusting for a wide range of confounders, residual confounding may still be present due to unmeasured factors such as diet, physical activity, and genetic predispositions. Future studies should include a more comprehensive set of covariates to better account for these potential sources of bias. Third, important variables such as lipid-lowering medication use and high-sensitivity C-reactive protein, which reflect therapeutic and inflammatory statuses, respectively, were not available or not consistently recorded in the NHANES 2013–2014 dataset and thus could not be included in our analysis. Their absence may contribute to residual confounding, and future studies should incorporate such variables to improve adjustment for cardiovascular and inflammatory risk. Finally, although we adjusted for various confounders, the dynamic nature of inflammation and oxidative stress during the progression of AAC requires further investigation through longitudinal studies to understand how these processes evolve over time and whether they are modifiable by interventions. Although we included age as a continuous covariate in all multivariable models, residual confounding due to age-related calcification processes cannot be completely ruled out.

Conclusion

Our study demonstrates a significant association between MHR and the prevalence of AAC in adults aged ≥40 years. This association highlights the potential utility of MHR as a biomarker for identifying individuals at risk of AAC, with significant clinical implications for early detection and intervention. Further research is needed to explore the underlying mechanisms linking MHR to AAC and establish the prognostic value of MHR in longitudinal cohorts. By advancing our understanding of the role of MHR in vascular health, we can better design strategies to mitigate the AAC burden and its associated cardiovascular risks. Given its ease of measurement and strong association with inflammation, MHR can become a valuable tool in cardiovascular risk assessment and disease prevention.

Footnotes

Acknowledgments

We acknowledge all participants who were involved in the original study.

Author contributions

Fengli Gao and Chao Ding: conceptualization, methodology, formal analysis, data curation, writing—original draft, writing—review & editing, visualization. Chao Ding, Yulin Miao, Jian Li, and Guohang Shen: data curation, writing—original draft, writing—review & editing. All authors have reviewed the manuscript.

Data availability

The data that support the findings of this study are available from .

Declaration of conflicting interests

The authors declare that there are no potential conflicts of interest related to the research, authorship, and/or publication of this article.

Ethical approval

The NHANES received approval from the NCHS Ethics Review Board, and all participants provided informed consent for the publication of their data.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Chao Ding

References

Bartstra

Mali

WPTM

Spiering

, et al. Abdominal aortic calcification: from ancient friend to modern foe. Eur J Prev Cardiol 2021; 28: 1386–1391.

Leow

Szulc

Schousboe

, et al. Prognostic value of abdominal aortic calcification: a systematic review and meta-analysis of observational studies. J Am Heart Assoc 2021; 10: e017205.

Bendix

Johansen

Ringgaard

, et al. Diabetes and abdominal aortic calcification-a systematic review. Curr Osteoporos Rep 2018; 16: 42–57.

Craiem

Guilenea

De Freminville

, et al. Abdominal aortic calcium and geometry in patients with essential hypertension. Diagn Interv Imaging 2024; 105: 174–182.

Tóth

Balogh

Jeney

Regulation of vascular calcification by reactive oxygen species. Antioxidants (Basel) 2020; 9: 963.

Song

Liu

, et al. Inflammation-associated ectopic mineralization. Fundam Res 2023; 3: 1025–1038.

Zhang

Zhao

Zhou

, et al. Role of inflammation, immunity, and oxidative stress in hypertension: new insights and potential therapeutic targets. Front Immunol 2022; 13: 1098725.

Fuery

Liang

Kaplan

, et al. Vascular ossification: pathology, mechanisms, and clinical implications. Bone 2018; 109: 28–34.

Ganjali

Gotto

Jr. Ruscica

, et al. Monocyte-to-HDL-cholesterol ratio as a prognostic marker in cardiovascular diseases. J Cell Physiol 2018; 233: 9237–9246.

10.

Canpolat

Çetin

Cetin

, et al. Association of monocyte-to-HDL cholesterol ratio with slow coronary flow is linked to systemic inflammation. Clin Appl Thromb Hemost 2016; 22: 476–482.

11.

Assmann

Gotto

AM.

Jr.

HDL cholesterol and protective factors in atherosclerosis. Circulation 2004; 109: III8–III14.

12.

Zhang

, et al. Impact of monocyte to high-density lipoprotein ratio on the identification of prevalent coronary heart disease: insights from a general population. Postgrad Med 2021; 133: 822–829.

13.

Kauppila

Polak

Cupples

, et al. New indices to classify location, severity and progression of calcific lesions in the abdominal aorta: a 25-year follow-up study. Atherosclerosis 1997; 132: 245–250.

14.

Huang

Tang

, et al. Identification of dietary components in association with abdominal aortic calcification. Food Funct 2023; 14: 8383–8395.

15.

Erden

Turk

Fidan

, et al. Relationship between blood monocyte-HDL ratio and carotid intima media thickness in with postmenopausal women. J Clin Densitom 2023; 26: 101428.

16.

Tacke

Weiskirchen

Trautwein

Role of monocytes and macrophages in the pathogenesis of atherosclerosis and vascular calcification. Atherosclerosis 2015; 240: 183–191.

17.

Robinson

Peng

Peckham

, et al. Sex hormones drive changes in lipoprotein metabolism. iScience 2021; 24: 103257.

18.

Petsophonsakul

Burgmaier

Willems

, et al. Nicotine promotes vascular calcification via intracellular Ca2+-mediated, Nox5-induced oxidative stress, and extracellular vesicle release in vascular smooth muscle cells. Cardiovasc Res 2022; 118: 2196–2210.

19.

Makena

Haswell

McEwan

, et al. An adverse outcome pathway for cigarette smoke-mediated oxidative stress in plaque formation. Front Toxicol 2025; 7: 1554747.

20.

Woollard

Geissmann

Monocytes in atherosclerosis: subsets and functions. Nat Rev Cardiol 2010; 7: 77–86.

21.

Bandeali

Farmer

High-density lipoprotein and atherosclerosis: the role of antioxidant activity. Curr Atheroscler Rep 2012; 14: 101–107.

22.

Shu

Han

Qiu

, et al. Association of the monocyte to high-density lipoprotein cholesterol ratio and neutrophil to high-density lipoprotein cholesterol ratio with the severity of new-onset coronary artery disease. J Inflamm Res 2025; 18: 463–476.

23.

Pan

Jie

Huang

Vascular calcification: molecular mechanisms and therapeutic interventions. MedComm 2023; 4: e200.

24.

Jiang

Yang

Zou

, et al. Monocyte-to-high-density lipoprotein-cholesterol ratio (MHR) and the risk of all-cause and cardiovascular mortality: a nationwide cohort study in the United States. Lipids Health Dis 2022; 21: 30.

25.

Zhao

Zhang

, et al. Elevated monocyte-to-high-density lipoprotein ratio is associated with increased risk of cognitive impairment and severe cerebral small vessel disease burden. Front Aging Neurosci 2025; 17: 1588488.

26.

Wang

Duan

Nagraj

, et al. The value of the monocyte to high-density lipoprotein cholesterol ratio in predicting the thrombosis burden and long-term prognosis of acute myocardial infarction patients: a retrospective cohort study. J Thorac Dis 2025; 17: 3370–3379.

27.

Zhu

Shen

Liu

, et al. Predictive value of MHR, PLR combined with NLRP1 for severity and long-term prognosis in premature coronary artery disease. Immun Inflamm Dis 2025; 13: e70202.

Association between the monocyte-to-high-density lipoprotein cholesterol ratio and abdominal aortic calcification in adults: Insights from the National Health and Nutrition Examination Survey (2013–2014)

Abstract

Background

Method

Results

Conclusions

Keywords

Introduction

Materials and methods

Study population

AAC evaluations

MHR measurements

Covariate assessment

Statistical analyses

Results

Baseline characteristics of the study population

Associations between MHR and AAC

Nonlinear correlation between MHR and AAC

Subgroup and sensitivity analyses

Discussion

Mechanisms linking MHR and AAC

Conclusion

Footnotes

Acknowledgments

Author contributions

Data availability

Declaration of conflicting interests

Ethical approval

Funding

ORCID iD

References