Abstract
Objective
Our research aimed to investigate the relationship between the monocyte-to-high-density lipoprotein-cholesterol ratio (MHR) and the risk of severe abdominal aortic calcification (SAAC).
Methods
We conducted a cross-sectional study using data from the National Health and Nutrition Examination Survey (NHANES) 2013–2014. We further performed multivariable logistic regression analysis, restricted cubic spline (RCS) plots, and subgroup analysis to study the relationship between the MHR and the risk of SAAC.
Results
A total of 3017 participants were included in this study. After complete adjustment for potential confounders, the incidence of SAAC increased with the higher quartile of MHR (P <0.001). According to the RCS plot, a positive non-linear was the relationship between the MHR and SAAC risk, and the incidence of SAAC increased with the increase of MHR.
Conclusion
There was a positive non-linear correlation between the MHR and SAAC in US adults. Our findings imply that close monitoring and adequate control of the MHR has the potential to improve SAAC prevention in the general population.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12986-025-00998-x.
Keywords: MHR, Severe abdominal aortic calcification, Cross-sectional study, NHANES
Introduction
Atherosclerosis, which involves the accumulation of plaques in the arteries, poses a significant global health threat [1]. Currently, there is no definitive treatment for this condition. While researchers continue to investigate the underlying causes, the lack of a comprehensive understanding and effective treatment strategy means that atherosclerosis remains a formidable health challenge across the world [2].
Severe abdominal aortic calcification (SAAC) is a strong predictor of cardiovascular disease events as a marker of subclinical atherosclerosis [3, 4]. SAAC is not only associated with increased risk of coronary heart disease (CHD) and cardiovascular disease (CVD) mortality but also with congestive heart failure (CHF) independent of CHD and other risk factors [5, 6] In patients with end-stage renal disease (ESRD), the presence of SAAC is significantly associated with both all-cause and cardiovascular mortality [7, 8]. Moreover, SAAC is a common finding in the general population and is often under-recognized as a significant marker of cardiovascular risk.
Understanding the complex factors that drive SAAC is essential for developing effective prevention and treatment strategies. SAAC is influenced by a variety of factors, including genetic predisposition, metabolic disturbances, and chronic inflammation [9–11]. For instance, high levels of serum phosphorus and calcium, as well as elevated calcium-phosphate product, have been identified as significant risk factors for vascular calcification [12, 13]. Additionally, chronic kidney disease (CKD) and diabetes mellitus are strongly associated with the progression of SAAC [14]. These findings highlight the importance of modifiable risk factors in the development and progression of SAAC.
Given the significant health implications of SAAC and its strong association with cardiovascular morbidity and mortality, further research is imperative. This involves pinpointing modifiable risk factors and exploring the interplay between genetic and environmental influences [15, 16]. Gaining insight into these components will enable us to create evidence-based strategies for prevention, ultimately reducing the occurrence and progression of atherosclerosis and its complications. Therefore, it is imperative to explore factors influencing vascular calcification and provide preventive measures.
Research suggests that a high level of monocyte-to-high-density lipoprotein-cholesterol ratio (MHR) is associated with myocardial infarction and cardiovascular disease mortality [17, 18]. Increased levels of MHR lead to increased inflammation, which is linked to increased oxidative stress [19]. Since inflammation and lipids play important roles in promoting severe abdominal aortic calcification, it would be worth exploring the relationship between MHR and SAAC. However, limited research has been conducted on the relationship between MHR and the risk of SAAC occurrence in the general population undergoing routine medical examinations, especially with increasing levels of MHR.
Therefore, to identify potential approaches for providing SAAC risk assessments based on MHR levels to individuals undergoing health screening, the purpose of this study was to examine the association between MHR levels and SAAC risk in the general population of the United States by analyzing National Health and Nutrition Examination Survey (NHANES) data for 2013 and 2014.
Materials and methods
Study population
The National Health and Nutrition Examination Survey (NHANES) is a nationally representative cross-sectional survey on the US population [20]. The National Health and Nutrition Examination Survey is an ongoing cross-sectional survey that uses a stratified, multi-stage, and probability sampling method to monitor the health and nutritional status of adults and children across the United States [21]. The study analyzed data from the 2013–2014 NHANES cycle as SAAC status was only examined during this period and a total of 10,175 participants were enrolled in this period. We aim to study the population older than 40 years. SAAC data were available for 3,140 participants. We further excluded 123 participants missing data on MHR and finally 3017 participants were included in our study. The National Center for Health Statistics Research Ethics Review Board approved the NHANES study. In addition, informed consent was obtained from each participant prior to the survey.
SAAC measurement
Severe abdominal aortic calcification (SAAC) was evaluated by means of the Kauppila 24-point score on a lateral spine scan [22, 23]. The AAC scores ranged from 0 to 24 and SAAC was defined as a total AAC-24 score > 6 [24, 25]. Detailed information about the assessment can be found at https://wwwn.cdc.gov/Nchs/Nhanes/2013-2014/DXXAAC_H.htm.
Assessment of MHR
The monocyte-to-high-density lipoprotein-cholesterol ratio (MHR) is the ratio of monocyte count to high-density lipoprotein-cholesterol (HDL-C). In the 2013–2014 cycle, HDL-C was measured on the Roche Modular P and Roche Cobas 6000 chemistry analyzers. The monocyte count was measured by the DxC800. The methods used to measure these variables are available in detail at https://www.cdc.gov/nchs/nhanes/.
Covariates
Potential confounding variables were included in our analysis, including age, sex, race/ethnicity, education level, marital status, hypertension, diabetes mellitus (DM), smoking status, drinking status, body mass index (BMI), hemoglobin (Hb), aspartate aminotransferase (AST), alanine aminotransferase (ALT), total bilirubin, gamma-glutamyl transferase (GGT), serum creatinine (Scr), albumin (ALB), uric acid (UA), and glycohemoglobin (HbA1c). The measurement processes of these variables are available at https://www.cdc.gov/nchs/nhanes/.
Statistical analysis
When calculating all estimates, we considered the NHANES sample weights. For normally distributed variables, report mean ± standard deviation (SD); For non-normally distributed variables, report median (interquartile range, IQR); For categorical variables are expressed as numbers and percentages. The MHR was categorized into quartiles, and the lowest quartile served as the reference group (Q1). A weighted multiple-regression model was used to investigate the association between the MHR and the risk of SAAC. In addition, an interaction term was added to test the heterogeneity of associations between the subgroups. The restricted cubic spline model was used for the dose-response analysis. MHR is included in the regression equation as both a continuous and categorical variable. Four models were performed in the regression models (Model 1: unadjusted; Model 2: age and sex were adjusted; Model 3: adjusted for covariates in Model 2 plus race/ethnicity, education level, marital status, hypertension, diabetes mellitus, smoking status, and drinking status; Model 4: adjusted for covariates in Model 3 plus body mass index, hemoglobin, aspartate aminotransferase, alanine aminotransferase, gamma-glutamyl transferase, serum creatinine, uric acid, albumin, total bilirubin, and glycohemoglobin, as the final model. Model 4 was used in the restricted cubic spline models and subgroup analyses. To assess potential multicollinearity among the predictors in our final model, we conducted a collinearity analysis using variance inflation factors (VIFs). Moreover, subgroup analyses stratified by sex (male or female), age (< 60 or ≥ 60 years), BMI (< 30 or ≥ 30 kg/m2), hypertension (yes or no), and diabetes (yes or no) were performed. The statistical analyses were performed using R version 4.2.3 (R Foundation for Statistical Computing, Vienna, Austria). We used the Multiple Imputation by Chained Equations (MICE) method to handle missing data (Information on the number of missing data was shown in Supplementary Table S1). A P-value < 0.05 was regarded as statistically significant.
Results
Baseline characteristics
As shown in Table 1, the baseline characteristics of the study participants are presented as the means (SDs) and proportions stratified by the MHR quartiles. A total of 3017 subjects were included in our analysis (Fig. 1) and the prevalence of SAAC was 8.9%. The age range of our study population was 40 years to 80 years. The mean age was 58.61 ± 12.01 years and 51.8% were female. There were significant differences in sex, race, hypertension, diabetes, smoking status, systolic blood pressure, body mass index, waist circumference, total cholesterol, hemoglobin, alanine aminotransferase, gamma-glutamyl transferase, serum creatinine, albumin, uric acid, total bilirubin and glycosylated hemoglobin among the Q1, Q2, Q3, and Q4 groups. Also, we have prepared a supplementary table to show characteristics of the study population based on AAC levels (Supplementary Table S2).
Table 1.
Characteristics of the study population based on MHR quartiles
| Characteristic | Overall | Q1 | Q2 | Q3 | Q4 | P |
|---|---|---|---|---|---|---|
| n | 3017 | 743 | 754 | 767 | 753 | |
| Age, years | 58.61 (12.01) | 58.01 (11.48) | 59.11 (12.06) | 58.13 (12.12) | 59.19 (12.34) | 0.003 |
| Sex (%) | < 0.001 | |||||
| Male | 1455 (48.2) | 190(25.6) | 314(41.6) | 426(55.5) | 525(69.7) | |
| Female | 1562(51.8) | 553(74.4) | 440 (58.4) | 341 (44.5) | 228(30.3) | |
| Race/ethnicity, % | < 0.001 | |||||
| Mexican American | 401(13.3) | 81(10.9) | 114(15.1) | 105(13.7) | 101 (13.4) | |
| Other Hispanic | 285(9.4) | 63 (8.5) | 78(10.4) | 78 (10.2) | 66 (8.8) | |
| Non-Hispanic White | 1340 (44.4) | 282 (37.9) | 325(43.1) | 353(46.0) | 380(50.5) | |
| Non-Hispanic Black | 576 (19.1) | 182(24.5) | 133(17.6) | 132 (17.2) | 129 (17.1) | |
| Non-Hispanic Asian | 353 (11.7) | 116 (15.6) | 96(12.7) | 85(11.1) | 56 (7.4) | |
| Other Race - Including Multi-Racial | 62(2.1) | 19 (2.6) | 8 (1.1) | 14 (1.8) | 21 (2.8) | |
| Marital status, % | 0.340 | |||||
| Having a partner | 1820 (60.3) | 431 (58.0) | 447 (59.3) | 475 (61.9) | 467 (62.0) | |
| Never married | 235 (7.8) | 65 (8.7) | 53 (7.0) | 65(8.5) | 52(6.9) | |
| No partner | 962 (31.9) | 247 (33.3) | 254 (33.7) | 227(29.6) | 234(31.1) | |
| Education level, % | 0.262 | |||||
| Above High school | 821(27.8) | 196 (26.9) | 203 (27.7) | 217(29.0) | 205 (27.6) | |
| High school | 667(22.6) | 147 (20.2) | 153 (20.9) | 177(23.7) | 190 (25.5) | |
| Less than high school | 1464 (49.6) | 385 (52.9) | 377 (51.4) | 353 (47.3) | 349 (46.9) | |
| Hypertension, % | < 0.001 | |||||
| No | 1589(52.7) | 428(57.6) | 425 (56.4) | 390 (50.8) | 346(45.9) | |
| Yes | 1428(47.3) | 315(42.4) | 329 (43.6) | 377(49.2) | 407(54.1) | |
| Diabetes, % | < 0.001 | |||||
| No | 2520 (83.5) | 675(90.8) | 647 (85.8) | 623(81.2) | 575(76.4) | |
| Yes | 497(16.5) | 68 (9.2) | 107(14.2) | 144 (18.8) | 178(23.6) | |
| Smoker, % | < 0.001 | |||||
| No | 1624(53.8) | 464(62.4) | 432 (57.3) | 384(50.1) | 344 (45.7) | |
| Yes | 1393(46.2) | 279 (37.6) | 322.0 (42.7) | 383(49.9) | 409 (54.3) | |
| Alcohol user, % | 0.448 | |||||
| No | 2628 (87.1) | 633 (85.2) | 661 (87.7) | 665 (86.7) | 669 (88.8) | |
| Yes | 389(12.9) | 110 (14.8) | 93(12.3) | 102(13.3) | 84 (11.2) | |
| SBP, mmHg | 126.99(19.01) | 125.51 (19.34) | 126.22 (18.97) | 127.79 (18.65) | 128.40 (18.95) | 0.046 |
| DBP, mmHg | 70.90 (13.29) | 70.67 (11.73) | 71.27 (12.72) | 70.93 (13.85) | 70.74 (14.67) | 0.684 |
| BMI, kg/m2 | 28.48 (5.58) | 26.31 (5.43) | 28.26 (5.41) | 29.19 (5.51) | 30.11 (5.25) | < 0.001 |
| Waist circumference, cm | 99.38 (13.72) | 92.20 (13.10) | 97.85 (12.59) | 101.53 (12.61) | 105.79 (12.88) | < 0.001 |
| TC, mg/dL | 194.82(43.10) | 202.76 (38.19) | 196.72 (45.65) | 194.41 (42.12) | 185.50 (44.31) | < 0.001 |
| Hemoglobin, g/dL | 13.94 (1.49) | 13.47 (1.29) | 13.84 (1.37) | 14.14 (1.49) | 14.31 (1.65) | < 0.001 |
| AST, U/L | 25.48 (14.12) | 24.91 (15.36) | 25.42 (12.55) | 25.73 (14.95) | 25.84 (13.42) | 0.497 |
| ALT, U/L | 24.63 (18.57) | 21.71 (15.10) | 23.91 (16.38) | 26.09 (23.66) | 26.75 (17.41) | < 0.001 |
| GGT, U/L | 29.42 (48.47) | 25.62 (33.70) | 27.70 (36.93) | 29.76 (43.81) | 34.55 (70.28) | 0.004 |
| Serum creatinine, umol/L | 83.62 (46.41) | 76.69 (39.81) | 80.11 (36.78) | 84.85 (59.30) | 92.74 (44.65) | < 0.001 |
| Albumin, g/L | 42.30 (3.16) | 42.28 (3.05) | 42.81 (2.95) | 42.25 (3.30) | 41.84 (3.25) | 0.002 |
| Uric acid, umol/L | 324.19(82.69) | 294.77 (77.94) | 315.62 (78.71) | 333.04 (79.48) | 352.79 (83.43) | < 0.001 |
| Total bilirubin, umol/L | 11.00 (5.29) | 11.37 (4.76) | 11.32 (5.07) | 10.93 (6.46) | 10.40 (4.58) | 0.010 |
| HbA1c, % | 5.92 (1.16) | 5.61 (0.84) | 5.81 (1.00) | 5.99 (1.21) | 6.27 (1.43) | < 0.001 |
| SAAC, % | < 0.001 | |||||
| No | 2747(91.1) | 705 (94.9) | 681(90.3) | 700 (91.3) | 661 (87.8) | |
| Yes | 270 (8.9) | 38 (5.1) | 73(9.7) | 67(8.7) | 92(12.2) |
Abbreviations: MHR, monocyte-to-high-density lipoprotein-cholesterol ratio; Q1, 0.001–0.006; Q2, 0.007–0.010; Q3, 0.011–0.014; Q4, 0.015–0.082; DM, diabetes mellitus; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HbA1c, glycohemoglobin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, gamma-glutamyl transferase; TC, total cholesterol; HbA1c, glycosylated hemoglobin, SAAC, severe abdominal aortic calcification
Fig. 1.
Flow chart of our study. Abbreviations: MHR, monocyte-to-high-density lipoprotein-cholesterol ratio; SAAC, severe abdominal aortic calcification
Associations of the MHR with SAAC
Multivariate logistic regression models were used to clarify the relationship between MHR and the incidence of SAAC. We have now conducted variance inflation factor (VIF) analyses for all relevant variables in fully adjusted model. The results indicate that the VIF values for all incorporated variables were below 5 (with individual VIFs < 5), suggesting no severe multicollinearity issues (Supplementary table S3). According to the fully adjusted model, compared with those of the Q1 group, the odds ratios (ORs) with 95% confidence intervals (CIs) of MHR association with SAAC across Q2, Q3, and Q4 were 1.93(1.23,3.06), 1.89(1.17,3.05) and 2.82(1.75,4.61), respectively (Table 2). As shown in Fig. 2, the restricted cubic spline (RCS) curves revealed a positive non-linear correlation between MHR and the risk of SAAC in US adults (P for non-linear < 0.05).
Table 2.
Adjusted ORs for associations between MHR and the risk of SAAC
| MHR | Model 1 | Model 2 | Model 3 | Model 4 |
|---|---|---|---|---|
| Continuous | 1.51(1.26,1.79) | 1.51(1.23,1.86) | 1.42(1.15,1.75) | 1.94(1.19,3.33) |
| Q1 | Ref | Ref | Ref | Ref |
| Q2 | 1.99(1.33,3.01) | 1.88(1.23,2.91) | 1.88(1.21,2.95) | 1.93(1.23,3.06) |
| Q3 | 1.78(1.18,2.70) | 1.84(1.19,2.89) | 1.68(1.07,2.67) | 1.89(1.17,3.05) |
| Q4 | 2.58(1.76,3.86) | 2.63(1.71,4.11) | 2.36(1.51,3.71) | 2.82(1.75,4.61) |
| P for trend | < 0.001 | < 0.001 | < 0.001 | < 0.001 |
Notes:
Model 1: unadjusted
Model 2: age and sex
Model 3: model 2 variables plus race/ethnicity, education level, marital status, hypertension, diabetes, smoking status, and drinking status
Model 4 was adjusted for model 3 variables plus body mass index, hemoglobin, albumin, aspartate aminotransferase, alanine aminotransferase, gamma-glutamyl transferase, serum creatinine, uric acid, total bilirubin, and glycosylated hemoglobin
Abbreviations: MHR, monocyte-to-high-density lipoprotein-cholesterol ratio; SAAC, severe abdominal aortic calcification; Q1, 0.001–0.006; Q2, 0.007–0.010; Q3, 0.011–0.014; Q4, 0.015–0.082; OR, odd ratio; CI, confidence interval
Fig. 2.
RCS curve of the association between MHR and the risk of SAAC in the NHANES database. Abbreviations: RCS, restricted cubic spline; OR, odds ratio; MHR, monocyte-to-high-density lipoprotein-cholesterol ratio; SAAC, severe abdominal aortic calcification
Subgroup analyses
The associations between the MHR and incident SAAC were estimated using subgroup analyses stratified by age, sex, hypertension, DM, and BMI. The results demonstrated a positive non-linear relationship between the MHR and the risk of SAAC in every subgroup. There was no significant interaction observed in the subgroups. (Fig. 3 and Table 3).
Fig. 3.
RCS curve for the association between MHR with the risk of SAAC. (A) the association between MHR and SAAC stratified by age; (B) the association between MHR and SAAC stratified by sex; (C) the association between MHR and SAAC stratified by BMI; (D) the association between MHR and SAAC stratified by hypertension; and (E) the association between MHR and SAAC stratified by diabetes. Abbreviations: RCS, restricted cubic spline; OR, odds ratio; MHR, monocyte-to-high-density lipoprotein-cholesterol ratio; SAAC, severe abdominal aortic calcification; BMI, body mass index
Table 3.
Subgroups analysis for the associations of MHR with the risk of SAAC
| MHR | Q1 | Q2 | Q3 | Q4 | P for trend | P for Interaction |
|---|---|---|---|---|---|---|
| OR (95%CI) | OR (95%CI) | OR (95%CI) | OR (95%CI) | |||
| Age | 0.720 | |||||
| ≥ 60 | Ref | 2.28(1.43,3.68) | 2.12(1.29,3.54) | 3.06(1.83,5.19) | < 0.001 | |
| < 60 | Ref | 2.10(0.57,8.92) | 3.32(0.96,13.5) | 5.02(1.39,21.3) | 0.012 | |
| Sex | 0.280 | |||||
| Male | Ref | 2.01(0.94,4.55) | 1.73(0.80,3.94) | 2.76(1.29,6.28) | 0.001 | |
| Female | Ref | 1.96(1.11,3.51) | 2.03(1.09,3.08) | 3.28(1.67,6.51) | 0.013 | |
| BMI | 0.190 | |||||
| ≥ 30 kg/m2 | Ref | 5.97(1.74,28.3) | 4.65(1.30,22.7) | 9.32(2.59,46.1) | 0.010 | |
| < 30 kg/m2 | Ref | 1.55(0.94,2.56) | 1.73(1.02,2.95) | 2.29(1.32,4.02) | 0.007 | |
| Hypertension | 0.330 | |||||
| No | Ref | 1.94(0.86,4.60) | 2.54(1.03,6.45) | 2.21(0.88,5.75) | 0.111 | |
| Yes | Ref | 2.24(1.30,3.92) | 1.89(1.08,3.37) | 3.73(2.07,6.85) | < 0.001 | |
| Diabetes | 0.940 | |||||
| No | Ref | 2.03(1.24,3.39) | 1.79(1.04,3.11) | 2.64(1.50,4.71) | 0.003 | |
| Yes | Ref | 2.24(0.78,7.30) | 2.97(1.01,9.93) | 4.87(1.01,16.5) | 0.004 |
Notes: Analysis was adjusted for age, sex, race/ethnicity, education level, marital status, hypertension, diabetes mellitus, smoking status, drinking status, body mass index, hemoglobin, aspartate aminotransferase, albumin, alanine aminotransferase, gamma-glutamyl transferase, serum creatinine, uric acid, total bilirubin, and glycosylated hemoglobin
Abbreviations: MHR, monocyte-to-high-density lipoprotein-cholesterol ratio; SAAC, severe abdominal aortic calcification; Q1, 0.001–0.006; Q2, 0.007–0.010; Q3, 0.011–0.014; Q4, 0.015–0.082; OR, odd ratio; CI, confidence interval
Discussion
This cross-sectional survey comprehensively examined the effects of the MHR on severe abdominal aortic calcification (SAAC). Elevated MHR levels were found to be positively correlated with an increased risk of SAAC, with positive non-linear correlation after adjustment for several potential covariates. Subgroup analyses suggested that the relationships among the different subgroups were mostly consistent with those among the total patients.
Severe abdominal aortic calcification (SAAC) is widely acknowledged as a robust predictor of cardiovascular disease (CVD) events [6, 26, 27]. The SAAC could help clinicians and patients predict disease prognosis and identify high-risk patients at an early stage [28]. In addition, a positive non-linear correlation between the MHR and the risk of SAAC was observed in participants of all ages, male or female, with or without hypertension, with or without diabetes, and with or without obesity.
The monocyte-to-high-density lipoprotein-cholesterol ratio (MHR) is a composite inflammatory index that has been proven to be a parameter of systemic inflammation and oxidative stress in many cardiovascular diseases [29]. The MHR was derived from lipid and cell inflammation parameters, including monocyte count and high-density lipoprotein cholesterol [30, 31]. Dyslipidemia and inflammation are pathological conditions that cause damage to the endothelium, leading to vascular remodelling and the development of atherosclerosis [32, 33]. Monocytes transform into foam cells upon the ingestion of oxLDL, contributing significantly to the formation of atherosclerotic lesions [34, 35]. In contrast, high-density lipoprotein cholesterol can inhibit the activation of monocytes and suppress the inflammatory response [36]. Also, high-density lipoprotein cholesterol participates in reverse cholesterol transport from the periphery to the liver [37]. Previous studies have shown that the MHR is a reliable inflammatory and antioxidant factor and is related to prognosis and disease progression in cardiovascular diseases [38]. Multivessel coronary disease was primarily associated with higher MHR [39]. Additionally, MHR has been associated with mortality in patients with coronary heart disease (CHD) [40]. Furthermore, a significant association between MHR and carotid atherosclerotic plaque incidence suggested that the MHR might play a role in subclinical atherosclerosis [41].
The MHR is a novel biomarker that reflects the equilibrium between pro-inflammatory and anti-inflammatory processes within the body. It holds substantial clinical and preventive significance. Specifically, MHR can assist clinicians in pinpointing patients who are at a heightened risk for SAAC. This capability enables more focused interventions, including lifestyle changes, pharmacological therapies, and enhanced surveillance of individuals at high risk. MHR serves as a straightforward and cost-efficient tool for identifying those at elevated risk for SAAC. Early detection through MHR assessment facilitates timely interventions to prevent the onset or progression of the disease. Such interventions may include lifestyle modifications, such as regular physical activity, a balanced diet, and weight management [42–44]. Additionally, medications that target inflammation and optimize lipid profiles can help reduce MHR by lowering monocyte counts and increasing HDL-C levels. During the initial phases of atherosclerosis, inflammatory processes can inflict damage upon the vascular endothelium, thereby establishing a nonlinear correlation between the MHR and the SAAC risk. With the escalation of MHR, inflammatory activity correspondingly intensifies. Nevertheless, upon surpassing a particular threshold, the incremental effect of heightened inflammation on disease risk progressively abates.
Compared with other influencing factors, MHR has several advantages in the prevention or control of SAAC. Firstly, MHR is a composite index that integrates both inflammatory and lipid metabolism parameters, providing a more comprehensive assessment of the underlying pathological processes associated with SAAC. Traditional risk factors such as age, gender, hypertension, diabetes, and obesity are important, but they do not fully capture the complex interplay between inflammation and lipid abnormalities that drive atherosclerosis. MHR, by combining monocyte count and HDL-C levels, offers a more holistic view of the systemic inflammatory and lipid status, which is crucial for identifying high-risk individuals. Secondly, MHR is relatively easy to measure and calculate, requiring only routine blood tests for monocyte count and HDL-C levels. This simplicity makes it a practical and cost-effective biomarker for widespread use in clinical settings and public health screenings. Unlike some advanced imaging techniques or genetic markers that may be expensive or difficult to obtain, MHR can be readily assessed in most laboratories. Thirdly, MHR has shown consistent associations with various cardiovascular outcomes across different populations and subgroups, as demonstrated in this study and previous research. This robustness suggests that MHR is a reliable indicator that can be applied to diverse patient groups, enhancing its utility in clinical decision-making and risk stratification. Lastly, MHR can guide targeted interventions. By identifying individuals with elevated MHR, clinicians can implement specific strategies to reduce inflammation and optimize lipid profiles, such as prescribing anti-inflammatory medications or lipid-lowering therapies, thereby potentially lowering the risk of SAAC and its associated cardiovascular complications.
This study has shown for the first time that MHR is associated with the risk of SAAC. This may have some implications for the health management of US adults. NHANES is a nationally representative survey of the US civil noninstitutionalized population. This increased the generalizability of the findings to the US adult population and provided the statistical power to detect relevant associations.
Conclusion
Our study revealed a positive non-linear correlation between the MHR and the risk of SACC in the general US population. However, further prospective studies are still needed to clarify this relationship.
Limitations
There are several limitations to this study. First, because this was a cross-sectional study, the intrinsic mechanism of the association between the MHR and SAAC was not clarified. Second, this study did not eliminate other confounding factors. Third, diseases were diagnosed according to the self-reports of individuals which may have led to subjective bias. Finally, this was a single-center study and more data from other countries and regions need to be included to further investigate the association of the MHR concentration with SAAC in the future.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
The authors thank the NHANES staff and participants for their valuable contributions.
Author contributions
Zhihao Zhao, Diya Qi, and Ying Gao designed the study. Fengyun Zhang, Yi Liang and Yu Yang collected the data. Zhihao Zhao and Diya Qi performed the data analysis and interpretation. Zhihao Zhao wrote the manuscript. Ying Gao reviewed and edited the manuscript. All the authors read and approved the final manuscript.
Funding
None.
Data availability
The datasets for this study can be found at [www.cdc.gov/nchs/nhanes/](http://www.cdc.gov/nchs/nhanes) .
Declarations
Ethics approval and consent for participation
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.
Zhihao Zhao and Diya Qi contributed equally to this work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets for this study can be found at [www.cdc.gov/nchs/nhanes/](http://www.cdc.gov/nchs/nhanes) .