Comparison of systemic immunoinflammatory biomarkers for assessing severe abdominal aortic calcification among US adults aged≥40 years: A cross-sectional analysis from NHANES

Quankai Cheng; Chang Liu; Haicheng Zhong; Ziming Wang; Sheng Zhou; Jingjing Sun; Sihai Zhao; Jie Deng

doi:10.1371/journal.pone.0325949

Abstract

Objective

Several novel biomarkers, including the systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), aggregate index of systemic inflammation (AISI), platelet-lymphocyte ratio (PLR), neutrophil-lymphocyte ratio (NLR), and monocyte-lymphocyte ratio (MLR), are linked to the systemic immunity inflammation response and the odds and severity of abdominal aortic calcification (AAC). However, still no previous research has systematically compared their association with severe AAC.

Methods

This study utilized a cross-sectional approach, examining a cohort of 3,047 adults from National Health and Nutrition Examination Survey (NHANES). Weighted logistic regression was utilized to investigate the associations between a range of immunoinflammatory biomarkers and the likelihood of severe AAC. Segmented regression and limited cubic spline models were used in the investigation to characterize the threshold effects and non-linear correlations. Additionally, subgroup and interaction tests, Spearman correlation, least absolute shrinkage, and selection operator regression studies were conducted.

Results

The 3047 participants included in this study had a mean age of 58.63 years and 51.79% were female. After fully adjusting for all covariates, the ln-SIRI (OR 1.39 [CI 1.10–1.74], P = 0.005), ln-AISI (OR 1.26 [1.03–1.53], P = 0.024), and ln-MLR (OR 1.62 [1.15–2.30], P = 0.006) were significantly correlated with the odds of severe AAC. A non-linear dose-response relationship was observed between ln-SII and severe AAC. Additional subgroup analyses revealed that this relationship was more evident in the diabetic population. Additionally, MLR (AUC = 0.644) predicted the prevalence of severe AAC better than other biomarkers, and the prediction model constructed in conjunction with screened clinical indicators showed good predictive value (AUC = 0.853).

Conclusions

In this study, we comprehensively evaluated and compared the associations between six biomarkers and severe AAC, and developed a clinical prediction model using the MLR with the best predictive effect. However, cohort studies and model validation are still needed in the future to further confirm their relationship.

Citation: Cheng Q, Liu C, Zhong H, Wang Z, Zhou S, Sun J, et al. (2025) Comparison of systemic immunoinflammatory biomarkers for assessing severe abdominal aortic calcification among US adults aged≥40 years: A cross-sectional analysis from NHANES. PLoS One 20(6): e0325949. https://doi.org/10.1371/journal.pone.0325949

Editor: Aleksandra Klisic, University of Montenegro-Faculty of Medicine, MONTENEGRO

Received: November 15, 2024; Accepted: May 22, 2025; Published: June 24, 2025

Copyright: © 2025 Cheng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The datasets generated during and/or analyses during the current study are available in the NAHENS repository at https://wwwn.cdc.gov/nchs/nhanes/.

Funding: This research was funded by Shaanxi Social Development Funding (2024SF2-GJHX-12) to J.D.. The funder had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. It solely provided financial support for the publication of this research.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Globally, cardiovascular disease (CVD) is the leading cause of death and has a significant influence on both worldwide health and economic systems. [1]. Abdominal aortic calcification (AAC), characterized by abnormal calcification in the walls of muscular or elastic arteries, is a common occurrence in the context of CVD [2]. Epidemiological evidence strongly links AAC with hypertension, diabetes mellitus, and other atherosclerotic conditions, severe AAC can induce hemodynamic alterations, heightening the risk of complications such as abdominal aortic aneurysms and other CVD [3]. Thus, diagnosing AAC is crucial for assessing overall cardiovascular risk and facilitating early interventions to mitigate the incidence of CVD. Currently, a lateral lumbar spine x-ray is a common and safe diagnostic method for AAC, effectively illustrating the extent and location of calcifications [4]. Additionally, the Kauppila AAC score has been devised to quantify the extent of AAC, thereby aiding clinical decision-making [4]. Although reliable diagnostic tools are available, further research is needed to assess risk factors for the development of severe AAC and to actively prevent it [5].

Inflammation is a complex physiological response designed to repair bodily damage involving various cells, including neutrophils, macrophages, lymphocytes, and platelets, alongside substances these cells produce [6]. Numerous studies have identified the inflammatory response is the initial catalyst for atherosclerosis, leading to endothelial dysfunction as well as the accumulation of inflammatory cells within the walls of blood vessels, which activates and promotes the release of cytokines and growth factors, enhancing the calcification process [7,8]. The interplay between inflammation and oxidative stress is also critical in promoting arterial calcification [9]. Thus, inflammation is recognized as a fundamental pathophysiological process in arterial calcification and plays a crucial role in the advancement of CVD.

Given the pivotal role that inflammation plays in the development of CVD, researchers have shown significant interest in identifying inflammatory biomarkers to assess arterial calcification, including monocyte-lymphocyte ratio (MLR), neutrophil-lymphocyte ratio (NLR), and platelet-lymphocyte ratio (PLR) [10–12]. These markers are essential for demonstrating the link between different symptoms of CVD and systemic inflammation [13,14]. Moreover, advancements have been achieved in creating more sophisticated and comprehensive indices, including systemic immune inflammation index (SII), systemic inflammatory response index (SIRI), and aggregate index of systemic inflammation (AISI). These three indices, which integrate four hematologic profiles, provide more accurate clinical insights compared to MLR, NLR, and PLR [15]. Previous research has validated the strong diagnostic and predictive capabilities of these immunoinflammatory markers in tumors and inflammation-related diseases. In contrast to complete blood counts, they can accurately characterize the body’s immune-inflammatory response, aid in the detection of inflammation, cancer, and arterial calcification, and have been shown to correlate with the development of most clinical diseases [16–19]. Despite these advances, there is still a lack of relevant studies to comprehensively assess the relationship between multiple systemic immunoinflammatory biomarkers and severe AAC and to compare their clinical predictive value. Therefore, our study aimed to comprehensively test the association of six systemic immunoinflammatory biomarkers (SII, SIRI, AISI, PLR, NLR, and MLR) with the likelihood of severe AAC and to explore the dose-response relationship between them and the differences between populations, and ultimately to assess the role of these biomarkers in predicting the severe AAC and construct a visualization model.

Methods

Study design and population

The NHANES is a nationally representative epidemiologic survey. It employs a complex multistage random sampling method to guarantee that it accurately represents the population [20]. During recruitment, the National Center for Health Statistics Research Ethics Review Committee approved the study methodology, and each participant signed a comprehensive written consent document [21]. We collected data on participants’ AAC scores during the 2013–2014 period, as well as other important health indicators, including BMI, waist circumference, grip strength, and disease status. During the data processing stage, we excluded 7,035 individuals with absent AAC data and 93 individuals with incomplete information on neutrophils, monocytes, lymphocytes, and platelets. In the completed analysis, Fig 1 displays that 3,047 individuals were deemed eligible and subsequently included.

Download:

Fig 1. Flowchart of the participants’ selection from NHANES 2013–2014.

https://doi.org/10.1371/journal.pone.0325949.g001

Definition of systemic immunoinflammatory biomarkers (SII, SIRI, AISI, PLR, NLR, MLR)

The NHANES database provides the essential data required for calculating systemic immunoinflammatory biomarkers. These counts underwent rigorous laboratory testing and are reported in units of × cells/μl. Using the results from these complete blood counts, we derived six systemic immunoinflammatory biomarkers with the following formulas [18]:

SII = platelet × neutrophil/lymphocyte count;

SIRI = monocyte × neutrophil/lymphocyte count;

AISI = neutrophil × platelet× monocyte/lymphocyte count;

PLR = platelet/lymphocyte count;

NLR = neutrophil/lymphocyte count;

MLR = monocyte/lymphocyte count.

Evaluation of severe AAC

To assess AAC, the Kauppila scoring system was utilized via dual energy X-ray absorptiometry (DXA) [21]. DXA is a method that calculates the bone mineral content and soft tissue composition of a scanned region based on the differential attenuation of high and low energy X-rays passing through the body. When evaluating AAC, DXA specifically targets the lumbar spine region for lateral scans, covering the vertebrae from L1-L4 [22–24], to quantitively evaluate the extent of AAC. In contrast, the Kauppila score method is mainly based on the midpoint of the intervertebral space as the boundary and divides the artery into anterior and posterior walls, and evaluates the calcification of the abdominal aorta in the vicinity of each vertebra separately, and the sum of the calcification scores of the anterior and posterior walls of the abdominal aorta in the vicinity of each vertebra is the total vascular calcification score [25,26]. The AAC score obtained by this method varies from 0 to 24, with elevated values representing greater calcification extent. In clinical and research settings, a threshold of AAC score ≥ 6 is commonly utilized to indicate severe calcification, signifying an elevated cardiovascular risk [27,28]. This scoring system, which can diagnose AAC with high specificity and sensitivity and expose subjects to lower radiation levels, has been recognized as a reliable method for assessing AAC [29,30].

Covariate

Based on previous studies, our study comprehensively analyzed multiple covariates that could influence systemic immunoinflammatory biomarkers associated with AAC [11,31]. These covariates were categorized into several broad groups: demographic variables, physical examination variables, laboratory data variables and living health variables [32]. S1 Table lists the explanations and precise definitions of all covariates. In this case, data on poverty income ratios (PIR) were derived directly from gross annual household income reported in the NHANES questionnaire (INDFMPIR), and was categorized into three classes: low-income, < 1.3; middle-income, 1.3–3.5; and high-income, ≥ 3.5 [33,34]. Body mass index (BMI) is categorized through WHO international standards (2000) and previous studies as normal, < 25; overweight, 25–30; and obese, ≥ 30 [34,35]. Diabetes was defined as 1) self-reported diabetes mellitus; 2) insulin or glucose-lowering medications; 3) hemoglobin A1c (HbA1c) ≥ 6. 5%; and 4) fasting blood glucose (FBG) ≥ 7.0 mmol/L. Prediabetes mellitus was defined as 1) self-reported pre-diabetes mellitus; 2) HbA1c 5.7–6.4%; and 3) FBG 5.6–6.9 mmol/L [34,36]. Hypertension was defined as 1) self-reported hypertension; 2) use of antihypertensive medication; and 3) mean systolic blood pressure ≥ 140 mmHg or mean diastolic blood pressure ≥ 90 mmHg [37,38]. Hyperlipidemia was defined as 1) self-reported high cholesterol levels; 2) use of cholesterol-lowering medications; and 3) laboratory tests showing total cholesterol ≥ 200 mg/dl and high-density lipoprotein-cholesterol (HDL-C) < 40 mg/dl (male) or < 50 mg/dl (female). The definition of other diseases relies heavily on self-reported data. These self-reported medical histories were provided by trained participants and collected by specialized staff, ensuring their reliability and rigor in epidemiological applications, as has been validated in the reports of multiple studies [12,39]. Collection methods and details can be found at https://wwwn.cdc.gov/nchs/nhanes/.

Statistical analysis

Baseline demographic data, represented by means (standard deviation) for normally distributed continuous variables; median (first quartile, third quartile) for skewed continuous variables; and frequencies (percentages) for categorical variables. Missing covariates were imputed using multiple imputations with five replications, employing a chained equation approach to account for missing data across replications [40]. Participants were categorized into severe AAC and non-severe AAC groups according to the Kauppila scoring system. Additionally, we assessed the normality of systemic immunoinflammatory biomarkers (SII, SIRI, AISI, PLR, NLR, and MLR) using both Kolmogorov-Smirnov tests and graphical methods by superimposing histograms with theoretical normal distribution curves. Since these data exhibited a non-normal distribution, a natural logarithm (ln) transformation was performed to achieve an approximate normal distribution (S2 Table and S1 Fig). Normally distributed continuous variables were analyzed using Student’s t-tests, skewed continuous variables were analyzed using Mann-Whitney U test, and categorical data were examined with Chi-square tests. In addition, all covariates involved in this study were tested for multicollinearity, and variables with high covariance were excluded to improve the stability of the model.

First, we utilized weighted multivariate logistic regression modeling to assess the correlation of SII, SIRI, AISI, PLR, NLR, and MLR with severe AAC, reporting the results as odds ratios (OR) with 95% confidence intervals (CI). Three distinct models were developed: a basic unadjusted model, model 2 with adjustments for all covariates identified, and model 1 with adjustments specifically for age, gender, and race. Since the effects of SII, AISI, and NLR on severe AAC were not significant for individual units, we explored the effects of SII, AISI, and NLR per 100 units on the potential for severe AAC in our analysis. Subsequently, the SII, SIRI, AISI, PLR, NLR, and MLR data were transformed into quartiles (Q1, Q2, Q3, and Q4), and the linear relationship between them and severe AAC was investigated using a trend test [14]. Second, we examined potential non-linear associations of these markers with severe AAC using restricted cubic spline (RCS) regression. RCS is able to capture the nonlinear trend of continuous variables (e.g., “U-shaped” and “J-shaped”) by flexibly setting the nodes, avoiding the bias of linear assumptions, and improving the model fitting effect and biological explanatory power. The reference point is set as the median, and the knots are determined according to the Minimizing Akaike Information Criterion (AIC). Upon detecting non-linearity, we calculated inflection points and employed segmented linear regression to analyze the threshold effects of these markers. Meanwhile, we performed subgroup analyses, interaction tests, and Spearman correlation analyses.

Moreover, least absolute contraction and selection operator (LASSO) regression were used to further screen the characteristic factors affecting AAC in the covariates. Additionally, the Receiver Operating Characteristic (ROC) curve was used to evaluate the model’s prediction ability for severe AAC. Finally, a comprehensive prediction model and nomogram were constructed. The validity of the model was assessed by ROC curve, decision curve analysis (DCA), and area under the curve (AUC). The nomogram depicted the correlation of overall scores with predicted prognosis. R software (version 4.3.3) was utilized to conduct every analysis. Statistical significance was determined with a p < 0.05 (2-sided).

Results

Baseline characteristics of participants

This study included 3,047 participants, as depicted in the screening flowchart (Fig 1). Table 1 presents the participants’ demographic characteristics along with other covariates. The groups are based on the presence of severe AAC. Interestingly, the participants’ average age was 58.63 years, 53.36% were under 60 years of age and 51.79% were female. Comparative analysis revealed that individuals with severe AAC tended to be of an advanced age, experienced greater incidences of smoking, and more frequently suffered from hypertension, hyperlipidemia, diabetes, CHD, myocardial infarction, stroke, and COPD, among other conditions.

Download:

Table 1. Baseline characteristics of subjects with and without severe AAC in NHANES 2013-2014.

https://doi.org/10.1371/journal.pone.0325949.t001

Assessment of multicollinearity of variables

We assessed multicollinearity among covariates using variance inflation factor (VIF) analysis. The results showed that the VIF values were below 5 for all variables except BMI and waist circumference (S3 Table). To be precise, a VIF value equal to 1 indicates that there is no multicollinearity between the factors; between 1 and 5 indicates that there may be correlation but it is not a cause for undue concern; between 5 and 10 indicates that there may be a high degree of correlation; and greater than 10 can be considered as too high a degree of multicollinearity and the model is relatively unstable [34]. The above results suggest that multicollinearity exists between BMI and waist circumference in the model of this study, which prompted us to exclude the variable of waist circumference, considering the central position of the BMI index in the assessment system and its higher clinical applicability. Subsequent VIF analyses showed that there was no significant multicollinearity in this study’s model (S3 Table).

Correlation of odds of severe AAC with systemic immunoinflammatory biomarkers (SII, SIRI, AISI, PLR, NLR, and MLR)

Table 2 summarizes the results of our analysis using multivariate logistic regression. In the unadjusted and minimally adjusted models, increases in the ln-transformed values of SII, SIRI, AISI, PLR and MLR were significantly correlated with a higher likelihood of severe AAC. However, when the model was thoroughly adjusted, there was no statistically significant correlation between ln-SII and ln-PLR and severe AAC. Conversely, the relationships with ln-SIRI (OR 1.39 [CI 1.10–1.74], P = 0.005), ln-AISI (OR 1.26 [1.03–1.53], P = 0.024), and ln-MLR (OR 1.62 [1.15–2.30], P = 0.006) remained significant. Specifically, a one-unit increase in ln-SIRI, ln-AISI, and ln-MLR corresponded to a 39%, 26%, and 62% greater odds of severe AAC. This trend persisted in further sensitivity analyses, where we categorized systemic immunoinflammatory biomarkers into quartiles. When fully adjusted for all covariates, the difference between the highest quartile’s (Q4) and lowest quartile’s (Q1) odds of severe AAC increased by 55% for SII, 85% for SIRI, 62% for AISI, 56% for PLR, and 65% for MLR (P < 0.05).

Download:

Table 2. Association between systemic immune-inflammatory biomarkers (SII, SIRI, AISI, PLR, NLR, MLR) and severe AAC.

https://doi.org/10.1371/journal.pone.0325949.t002

Dose-response relationship regarding severe AAC odds and systemic immunoinflammatory biomarkers (SII, SIRI, AISI, PLR, NLR, MLR)

The results of the RCS regression model are depicted in Fig 2. All RCS models were fitted with four knots according to the minimized AIC. After controlling for all confounding variables, we observed a non-linear dose-response relationship for SII, indicating a significant non-linear association with severe AAC (P < 0.05 for non-linearity). As detailed in Table 3, the outcomes from the segmented linear regression indicate a critical inflection point for ln-transformed SII at 5.27. Below the inflection point, ln-SII was not significantly related to the odds of severe AAC. Conversely, above an ln-SII value of 5.27, the likelihood of severe AAC rose by 41% (OR 1.41 [1.08–1.84], P = 0.012) (the log-likelihood test is 0.015). This suggests that the higher the level of SII, the greater the odds of severe AAC occurring.

Download:

Table 3. Two-piecewise linear regression modeling to analyze the threshold effect of lnSII on severe AAC.

https://doi.org/10.1371/journal.pone.0325949.t003

Download:

Fig 2. Dose-response relationships between ln-transformed SII, SIRI, AISI, PLR, NLR, MLR, and severe AAC.

(A) Dose-response of lnSII and severe AAC; (B) Dose-response of lnSIRI and severe AAC; (C) Dose-response of lnAISI and severe AAC; (D) Dose-response of lnPLR and severe AAC; (E) Dose-response of lnNLR and severe AAC; and (F) Dose-response of lnMLR and severe AAC. SII, systemic immune-inflammation index; SIRI, systemic inflammation response index; AISI, aggregate index of systemic inflammation; PLR, platelet- to-lymphocyte ratio; NLR, neutrophil-to-lymphocyte ratio; MLR, monocyte-to-lymphocyte ratio.

https://doi.org/10.1371/journal.pone.0325949.g002

Subgroup and interaction analyses

As detailed in Fig 3, we categorized the participants according to age, gender, BMI, and health conditions, and conducted multiple logistic regression analyses. In most subgroups, ln-SII, ln-SIRI, ln-AISI, ln-PLR, ln-NLR, and ln-MLR did not interact significantly with severe AAC (P for interaction > 0.05). However, there was a significant interaction between the biomarkers and severe AAC in the diabetes subgroup, except for ln-NLR (P for interaction < 0.05). Specifically, within the diabetic cohort, for each one-unit increase in ln-transformed SII, SIRI, AISI, PLR, and MLR, the odds of severe AAC rose significantly by 84%, 115%, 76%, 121%, and 175% respectively (S4 Table). These findings emphasize that diabetes may be a key modifier of the association of inflammation with AAC.

Download:

Fig 3. Subgroup and interaction analysis of the association between systemic immunoinflammatory biomarkers (SII, SIRI, AISI, PLR, NLR, MLR) and severe AAC.

https://doi.org/10.1371/journal.pone.0325949.g003

Correlation of systemic immunoinflammatory biomarkers with abdominal aortic calcification of participants

The relationship between systemic immunoinflammatory biomarkers, AAC scores, and the prevalence of severe AAC in research participants was evaluated using Spearman correlation analysis. All of the aforementioned indices were statistically significant with regard to the AAC score and the occurrence of severe AAC (p < 0.05), as shown in S5 Table. Notably, MLR exhibited the strongest positive correlation with AAC scores (R = 0.128, P < 0.001) and severe AAC (R = 0.154, P < 0.001), indicating a robust association. In addition, there appeared to be very weak negative and positive correlations between NLR and AAC scores (R = −0.021, P = 0.004) and severe AAC (R = 0.023, P = 0.002), respectively.

Diagnostic power of different systemic immunoinflammatory biomarkers for severe AAC

As illustrated in Fig 4A, we employed ROC curve analyses to assess the effectiveness of multiple systemic immunoinflammatory biomarkers in identifying participants with severe AAC, including SII, SIRI, AISI, PLR, NLR, and MLR. The results indicated that MLR, with an AUC value of 0.644 (95% CI 0.631–0.657, P < 0.001) and an optimum cutoff level of 0.344, a sensitivity of 0.749 (95% CI 0.732–0.765), and a specificity of 0.465 (95% CI 0.411–0.519), was particularly effective in identifying severe AAC (S6 Table). Among them, the optimal cutoff value was determined by maximizing the Youden index.

Download:

Fig 4. (A) Comparison of predictive values of SII, SIRI, AISI, PLR, NLR, and MLR for the prevalence of severe AAC; (B) Regression coefficient profile when using LASSO regression for feature screening of severe AAC prediction. Each curve represents the change trajectory of each feature coefficient; (C) Cross-validation curve of LASSO regression. Each red dot represents the mean square error (MSE) for each value. The ordinate is the value of the coefficient, the abscissa (top) is the number of non-zero coefficients in the model, and the abscissa (bottom) is the logarithm of the regularization parameter . The dashed line on the right shows the optimal value of lambda (best = −4.157). For the prediction of severe AAC, we used for variable screening. Eight variables were screened, including age, race, BMI, smoking status, CHD, diabetes, antihypertensive therapy, cholesterol-lowering therapy.

https://doi.org/10.1371/journal.pone.0325949.g004

LASSO regression analysis and construction of the final model

Additionally, we further refined our selection of clinical variables for the final prediction model using LASSO regression analysis, identifying 8 key factors (S7 Table and Fig 4B-C). As depicted in Fig 5A, ultimately, by results from LASSO regression analyses, we constructed a final prediction model incorporating 9 variables. This model demonstrated robust predictive performance, evidenced by a high AUC value of 0.853 (95% CI 0.8320.874), the model was 76.6% sensitive and 80.9% specific. Furthermore, the average AUC value of the predictive model was 0.847 based on 10-fold cross-validation, which was consistent with the previously constructed value (S2 Fig). Further DCA showed that the model possesses a high decision value (Fig 5D). Meanwhile, AUC of 0.845, Brier value of 0.076 (<0.25), and P = 0.873 (>0.05) are also shown by the calibration curve in Fig 5B, suggesting that this predictive model has strong predictive ability and that the fitted line agrees well with the standard reference line.

Download:

Fig 5. Performance evaluation of the severe AAC prediction model and construction of a nomogram.

(A) ROC curve. The AUC of the model was 0.853 (95% CI = 0.832-0.874). The specificity of the model was 80.9% and the sensitivity was 76.6%; (B) Calibration curve. The solid gray line (Ideal) represents the calibration curve for the ideal prediction model, the solid black line (Logistic calibration) represents the prediction model calibrated using logistic regression, and the dashed gray line (Nonparametric) represents the actual prediction model; (C) Nomogram. The top horizontal line is the score column, and the total score based on the item-by-item sum of the scores for each item is the probability of severe AAC. Taking the red dot as an example, the participants in this state are 37.3% more likely to suffer from AAC; (D) Decision curve analysis (DCA). The red line indicates the net benefit of using the predictive model. The black line indicates if the predictive model is not used.

https://doi.org/10.1371/journal.pone.0325949.g005

Construction of the nomogram

Subsequently, we developed a nomogram utilizing the nine clinical variables identified earlier, assigning a score to each variable. The cumulative total score from these variables can indicate a subject’s likelihood of developing severe AAC. This approach enhances the visualization and facilitates more precise predictions of the odds associated with developing severe AAC, as depicted in Fig 5C.

Discussion

Our research represents the initial cross-sectional analysis to simultaneously evaluate the predictive capability of six systemic immunoinflammatory biomarkers for severe AAC. Among the 3,047 participants included in our study, we observed several key findings: 1) SII, SIRI, AISI, PLR, and MLR were positively correlated with severe AAC, suggesting that an increased odds of severe AAC may be linked to higher levels of systemic inflammation; 2) A non-linear dose-response relationship was identified linking ln-SII with severe AAC, the inflection point occurred at ln-SII = 5.27, above which a positive correlation was evident; 3) Subgroup and interaction analyses indicated significant differences and interactions in the associations between SII, SIRI, AISI, PLR, MLR, and severe AAC among diabetic and non-diabetic participants; 4) ROC curve analysis demonstrated that MLR may be especially helpful in anticipating the odds of severe AAC.

Chen et al. conducted a notable study in this area, demonstrating that SII, NLR, and PLR are independent predictors of colorectal cancer, and that SII is particularly effective in differentiating patients with similar TNM staging but very high risk [41]. Wang et al. explored the relationship between SIRI and breast cancer, finding that patients with a SIRI < 0.65 exhibited higher overall survival rates, particularly in post-operative patients with breast cancer [42]. Concurrently, Wang et al. assessed the relationship between systemic immunoinflammatory biomarkers and prostate cancer, discovering that MLR was more sensitive and accurate in distinguishing the presence of prostate cancer [43]. Li et al. further expanded the scope of their research by exploring the connection between inflammatory states and non-cancerous diseases, particularly by comparing the levels of SII, SIRI, NHR (neutrophil-to-hemoglobin ratio), LHR (lymphocyte-to-hemoglobin ratio), MHR (monocyte-to-hemoglobin ratio), and PHR (platelet-to-hemoglobin ratio) with chronic kidney disease (CKD). They proposed that elevated levels of SIRI, SII, NHR, MHR, and PHR may increase the potential odds of developing CKD and emphasized that SIRI might be a better biomarker for predicting CKD [44]. Zhang et al. identified a potential association between PLR and MLR and primary membranous nephropathy (PMN), noting that PMN patients with poor treatment outcomes often exhibit higher PLR levels, and elevated MLR levels are associated with adverse renal prognosis [45]. Regarding nephrotic syndrome, Toraman and his team also confirmed the relationship between NLR and PLR and rapidly progressive glomerulonephritis (RPGN). They found that fibrinoid necrosis was positively correlated with PLR, and negatively correlated with lymphocyte count and hematocrit, concluding that the NLR-PLR marker can be used for grading glomerular inflammation and serves as a relatively accurate, economical new indicator for predicting RPGN clinical challenges [46]. Similarly, systemic immunoinflammatory biomarkers have been found to be closely related to the occurrence and progression of kidney stones. Mao et al.’s cross-sectional study indicated that higher levels of NLR are positively correlated with the prevalence of kidney stones and the number of stones excreted, suggesting that NLR, which can be easily obtained from blood tests, could serve as a novel indicator for kidney stone prevention and screening [47]. Furthermore, Tung et al. explored the association between NLR and both uric acid stones and CKD, demonstrating that patients with higher NLR levels, especially those with uric acid stones, are more likely to progress to CKD in the later stages [48]. In liver diseases, Zhao et al. examined the connection between SII and Non-alcoholic fatty liver disease (NAFLD), uncovering a “J”-shaped non-linear association of with mortality [49]. These findings underscore that the values of systemic immunoinflammatory biomarkers are closely related to the severity across various non-neoplastic diseases, broadening their potential applications.

Current research robustly supports the notion that inflammation is fundamental to the pathogenesis of vascular calcification [50,51]. This is primarily due to the inflammation’s regulation of vascular smooth muscle cell expression, which promotes calcification [52,53]. Specifically, monocytes are crucial in the early stages of arterial calcification; they migrate from the bloodstream to the vessel wall, influenced by chemokines, and later differentiate into macrophages [54]. These macrophages contribute to arterial calcification through producing various cytokines and matrix-degrading enzymes [55]. Neutrophils, which are abundant in the body, contribute to inflammation and calcium deposition in the arterial wall by oxidizing and degrading lipids and proteins through the release of oxidative stress product and proteases, further promoting the formation of calcified plaques [10]. Additionally, platelets serve a dual function in arterial calcification: they not only attach to the vessel wall, promoting the formation of atheromatous plaques [56], but their activation also exacerbates the inflammatory response and facilitates thrombosis, ultimately driving vascular calcification [57]. In contrast, lymphocytes may offer protective effects against arterial calcification by releasing anti-inflammatory and immunosuppressive factors, thus inhibiting excessive inflammatory responses and preventing further deterioration [58]. This aligns with the non-linear dose-response relationship between SII and severe AAC revealed in our study, suggesting that excessively high SII levels are associated with an increased odds of severe AAC. When SII is elevated, it may be due to an increase in platelet and neutrophil counts, leading to the release of a large amount of inflammatory mediators, chemokines, and reactive oxygen species, which adhere to the vessel wall, induce endothelial cell damage, and cause tissue ischemia, ultimately leading to adverse outcomes [59]. Moreover, elevated SII may also be attributed to a reduction in lymphocyte levels, as low lymphocyte counts weaken the suppression of endothelial cell inflammation, promoting the progression of atherosclerosis and vascular calcification [60]. Notably, beyond blood markers, a higher dietary inflammatory index was consistently linked with an increased AAC odds, displaying consistent trends across various populations. [61].

The relationships of SII, SIRI, AISI, PLR, and MLR with the prevalence for severe AAC after ln conversion were substantially different in diabetic and non-diabetic populations, according to additional subgroup analyses. This difference may be attributed to several key factors: 1) Diabetes, particularly type 2 diabetes, often induces a chronic, low-grade systemic inflammation. The hyperglycemic state activates multiple inflammatory pathways, such as nuclear factor kappa-B and NOD-like receptor thermal protein domain associated protein 3, which accelerate the production of inflammatory mediators like tumor necrosis factor-α, interleukin-6, and C-reactive protein [62]. These mediators contribute to endothelial cell damage and dysfunction, thereby accelerating the process of arterial calcification [63]. 2) Diabetic patients experience significant alterations in lipid metabolism and vascular function, characterized by increased low-density lipoprotein cholesterol and other components of non-high-density lipoprotein cholesterol [64]. The accumulation of lipids promotes atherosclerosis, leading to endothelial dysfunction, such as decreased nitric oxide synthesis and smooth muscle cell proliferation, ultimately influencing the progression of arterial calcification [65]. 3) Insulin resistance, a common feature in diabetes, leads to increased production of very-low-density lipoprotein (VLDL) by the liver. VLDL, through the enterohepatic circulation, causes hyperlipidemia, which raises levels of small, dense LDL, thereby increasing endothelial permeability, promoting monocyte-macrophage aggregation, and accelerating the formation of atherosclerotic plaques and calcification [34]. 4) Although the use of glucose-lowering or insulin medications improves insulin resistance and ameliorates vascular inflammation and arterial calcification, long-term use of such medications, especially sodium-glucose cotransporter 2 inhibitors and sulfonylureas, may induce local inflammation, thus influencing the progression of arterial calcification [66]. 5) Diabetes is often associated with bone metabolism disorders, with studies showing lower serum osteocalcin levels in diabetic patients, which may be closely linked to arterial calcification [67,68]. 6) Diabetes frequently coexists with multiple comorbidities, and the combined effects of these diseases may exacerbate the impact on arterial calcification [69,70]. However, due to the lack of specific classification of diabetes types and the absence of disease progression tracking and adjustment in the NHANES database, the association between diabetes and severe AAC still requires further investigation. Notably, this study observed a weak negative correlation between NLR and AAC (R = −0.021, P = 0.004) and a weak positive correlation with severe AAC (R = 0.023, P = 0.002). However, no significant association was found in both multivariate logistic regression and RCS analyses, suggesting that this weak correlation may lack biological significance. A multicenter study by Ban et al. demonstrated a significant association between NLR and AAC in patients with end-stage renal disease, but a similar report has not been seen in the general population [71]. This discrepancy suggests that the predictive value of NLR may be population-specific. Considering the large sample size of this study, these weak associations are more likely to be due to statistical noise. Future prospective studies with more rigorous design are needed to further validate the potential relationship between NLR and AAC in specific populations.

Given these insights, systemic immunoinflammatory biomarkers demonstrate substantial predictive value across various diseases. Since these biomarkers can be derived from routine cell count tests, which are low-cost and convenient, hold promising prospects for future clinical applications. Our study possesses several strengths. First, we utilized participants from the NHANES database, which furnishes a substantial and geographically representative sample. Second, we conducted subgroup analyses on various clinical indicators to affirm the robustness of the associations between systemic immunoinflammatory biomarkers and severe AAC in diabetic and non-diabetic populations. Third, we explored the threshold effect of SII on severe AAC using RCS curves and further performed segmented logistic regression. Moreover, while systemic immunoinflammatory biomarkers have often been analyzed as single indicators in previous studies, we compared the associations between the six systemic immunoinflammatory biomarkers and severe AAC more comprehensively, which has not been explored by researchers in this field. Finally, we preliminarily assessed the ability of the six systemic immunoinflammatory biomarkers to predict severe AAC using ROC curves, and constructed a more complete clinical prediction model and nomogram by further LASSO feature screening.

However, there are several limitations to our study. First, as a cross-sectional study utilizing the NHANES database, it cannot establish causality between systemic immunoinflammatory biomarkers and severe AAC; longitudinal cohort studies are required to elucidate this relationship. Second, the accuracy of medical history information in the NHANES database relies on self-reported questionnaires, data on AAC are limited to those aged 40 years and older who were included from 2013 to 2014, and some covariates are missing more severely, which could affect the control over diagnoses and the authenticity of disease extent. Third, as the NHANES dataset originates from a cross-sectional survey of the non-institutionalized U.S. population, it may not include hospitalized patients, individuals with severe diseases, or residents from certain geographic regions. Therefore, our study may not fully reflect the true situation of all affected populations, especially those with severe comorbidities or undergoing treatment, which may limit the generalizability of the results, particularly when assessing association for extreme or severely ill patients, leading to potential selection bias. Additionally, the voluntary nature of participation may result in underrepresentation of certain groups, such as low-income populations or older adults. Fourth, although multiple relevant correlations were considered, it is not possible to completely eliminate the influence of all potential factors, such as autoimmune diseases, genetic conditions, and hereditary disorders. Fifth, considering that our study sample is primarily derived from the U.S. population, with the majority being Mexican Americans, non-Hispanic Whites, and non-Hispanic Blacks, the applicability of the findings to other countries or regions may be limited. Finally, this study established an uncomplicated model to forecast the onset of severe AAC, but it lacked a professional model validation method, and its predictive value needs further validation. Given these considerations, future prospective, multi-center studies are needed to include samples from a wider range of ethnicities to further explore the causal relationship between systemic immunoinflammatory biomarkers and severe AAC prevalence. Additionally, incorporating more samples and adopting more specialized and comprehensive methods to refine our model will be crucial to ensure its generalizability and clinical value across different populations.

Conclusion

Our study identified a significant relationship linking SII, SIRI, AISI, PLR, MLR to severe AAC odds. The combination of these clinical indicators not only helps to develop models for predicting severe AAC in the clinic, but also could aid in early diagnosis and personalized treatment. In the future, a comprehensive assessment combining these biomarkers may aid in clinical diagnosis and treatment, thus providing diabetic patients and other high-risk groups with more optimized management plans and improved cardiovascular health, and ultimately providing practical guidance for clinical practice.

Supporting information

S1 Table. All covariates and categorical definition of some of them.

https://doi.org/10.1371/journal.pone.0325949.s001

(DOCX)

S2 Table. Kolmogorov-Smirnov tests for systemic immunoinflammatory biomarkers.

https://doi.org/10.1371/journal.pone.0325949.s002

(DOCX)

S3 Table. Multicollinearity assessment of covariates.

https://doi.org/10.1371/journal.pone.0325949.s003

(DOCX)

S4 Table. Subgroup and interaction analysis of the association between systemic immunoinflammatory biomarkers (SII, SIRI, AISI, PLR, NLR, MLR) and severe. AAC.

https://doi.org/10.1371/journal.pone.0325949.s004

(DOCX)

S5 Table. Spearman correlation of systemic immunoinflammatory biomarkers with abdominal aortic calcification of participants.

https://doi.org/10.1371/journal.pone.0325949.s005

(DOCX)

S6 Table. Diagnostic efficacy of systemic immunoinflammatory biomarkers (SII, SIRI, AISI, PLR, NLR, MLR) and constructed prediction model for severe. AAC.

https://doi.org/10.1371/journal.pone.0325949.s006

(DOCX)

S7 Table. Lasso regression analysis results (best log(λ_1se) = −4.157).

https://doi.org/10.1371/journal.pone.0325949.s007

(DOCX)

S1 Fig. Histograms and theoretical normal curve overlays of systemic immunoinflammatory biomarkers.

(A) SII and lnSII; (B) SIRI and lnSIRI; (C) AISI and lnAISI; (D) PLR and lnPLR; (E) NLR and lnNLR; (F) MLR and lnMLR.

https://doi.org/10.1371/journal.pone.0325949.s008

(TIF)

S2 Fig. ROC curves and area under the curve (95% CI) of the prediction model for 10-fold cross validation.

(A) ROC curve for each fold; (B) average ROC curve for all folds.

https://doi.org/10.1371/journal.pone.0325949.s009

(TIF)

Acknowledgments

We sincerely thank those who provided data for our research on the NHANES database.

References

1. GBD 2021 Causes of Death Collaborators. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2100–32. pmid:38582094
- View Article
- PubMed/NCBI
- Google Scholar
2. Ngai D, Lino M, Bendeck MP. Cell-Matrix interactions and matricrine signaling in the pathogenesis of vascular calcification. Front Cardiovasc Med. 2018;5:174. pmid:30581820
- View Article
- PubMed/NCBI
- Google Scholar
3. Leopold JA. Vascular calcification: mechanisms of vascular smooth muscle cell calcification. Trends Cardiovasc Med. 2015;25(4):267–74. pmid:25435520
- View Article
- PubMed/NCBI
- Google Scholar
4. Kauppila LI, Polak JF, Cupples LA, Hannan MT, Kiel DP, Wilson PW. New indices to classify location, severity and progression of calcific lesions in the abdominal aorta: a 25-year follow-up study. Atherosclerosis. 1997;132(2):245–50. pmid:9242971
- View Article
- PubMed/NCBI
- Google Scholar
5. Qin Z, Liu Q, Jiao P, Geng J, Liao R, Su B. Higher blood cadmium concentration is associated with increased likelihood of abdominal aortic calcification. Front Cardiovasc Med. 2022;9:870169. pmid:35557529
- View Article
- PubMed/NCBI
- Google Scholar
6. Chavarria A, Alcocer-Varela J. Is damage in central nervous system due to inflammation?. Autoimmun Rev. 2004;3(4):251–60. pmid:15246020
- View Article
- PubMed/NCBI
- Google Scholar
7. Passos LSA, Lupieri A, Becker-Greene D, Aikawa E. Innate and adaptive immunity in cardiovascular calcification. Atherosclerosis. 2020;306:59–67. pmid:32222287
- View Article
- PubMed/NCBI
- Google Scholar
8. Hu C-T, Shao Y-D, Liu Y-Z, Xiao X, Cheng Z-B, Qu S-L, et al. Oxidative stress in vascular calcification. Clin Chim Acta. 2021;519:101–10. pmid:33887264
- View Article
- PubMed/NCBI
- Google Scholar
9. Meng X, Yang J, Dong M, Zhang K, Tu E, Gao Q, et al. Regulatory T cells in cardiovascular diseases. Nat Rev Cardiol. 2015;13(3):167–79.
- View Article
- Google Scholar
10. Turkmen K, Ozcicek F, Ozcicek A, Akbas EM, Erdur FM, Tonbul HZ. The relationship between neutrophil‐to‐lymphocyte ratio and vascular calcification in end‐stage renal disease patients. Hemodial Int. 2013;18(1):47–53.
- View Article
- Google Scholar
11. Xie R, Liu X, Wu H, Liu M, Zhang Y. Associations between systemic immune-inflammation index and abdominal aortic calcification: results of a nationwide survey. Nutr Metab Cardiovasc Dis. 2023;33(7):1437–43. pmid:37156667
- View Article
- PubMed/NCBI
- Google Scholar
12. Jin C, Li X, Luo Y, Zhang C, Zuo D. Associations between pan-immune-inflammation value and abdominal aortic calcification: a cross-sectional study. Front Immunol. 2024;15:1370516. pmid:38605946
- View Article
- PubMed/NCBI
- Google Scholar
13. Kaplangoray M, Toprak K, Deveci E, Caglayan C, Şahin E. Could pan-immune-inflammation value be a marker for the diagnosis of coronary slow flow phenomenon?. Cardiovasc Toxicol. 2024;24(5):519–26. pmid:38622332
- View Article
- PubMed/NCBI
- Google Scholar
14. Liu K, Tang S, Liu C, Ma J, Cao X, Yang X, et al. Systemic immune-inflammatory biomarkers (SII, NLR, PLR and LMR) linked to non-alcoholic fatty liver disease risk. Front Immunol. 2024;15:1337241. pmid:38481995
- View Article
- PubMed/NCBI
- Google Scholar
15. Erdogan T. Role of systemic immune-inflammation index in asthma and NSAID-exacerbated respiratory disease. Clin Respir J. 2021;15(4):400–5. pmid:33249745
- View Article
- PubMed/NCBI
- Google Scholar
16. Mandaliya H, Jones M, Oldmeadow C, Nordman II. Prognostic biomarkers in stage IV non-small cell lung cancer (NSCLC): neutrophil to lymphocyte ratio (NLR), lymphocyte to monocyte ratio (LMR), platelet to lymphocyte ratio (PLR) and advanced lung cancer inflammation index (ALI). Transl Lung Cancer Res. 2019;8(6):886–94. pmid:32010567
- View Article
- PubMed/NCBI
- Google Scholar
17. Wang R-H, Wen W-X, Jiang Z-P, Du Z-P, Ma Z-H, Lu A-L, et al. The clinical value of neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), platelet-to-lymphocyte ratio (PLR) and systemic inflammation response index (SIRI) for predicting the occurrence and severity of pneumonia in patients with intracerebral hemorrhage. Front Immunol. 2023;14: 1115031.
- View Article
- Google Scholar
18. Song Y, Zhao Y, Shu Y, Zhang L, Cheng W, Wang L, et al. Combination model of neutrophil to high-density lipoprotein ratio and system inflammation response index is more valuable for predicting peripheral arterial disease in type 2 diabetic patients: a cross-sectional study. Front Endocrinol (Lausanne). 2023;14:1100453. pmid:36875480
- View Article
- PubMed/NCBI
- Google Scholar
19. Fani L, van Dam-Nolen DHK, Vernooij M, Kavousi M, van der Lugt A, Bos D. Circulatory markers of immunity and carotid atherosclerotic plaque. Atherosclerosis. 2021;325:69–74. pmid:33894597
- View Article
- PubMed/NCBI
- Google Scholar
20. Zhang Y, Xie R, Ou J. A U-shaped association between serum albumin with total triiodothyronine in adults. J Clin Lab Anal. 2022;36(6):e24473. pmid:35524426
- View Article
- PubMed/NCBI
- Google Scholar
21. Chen Y, Chang Z, Zhao Y, Liu Y, Fu J, Zhang Y, et al. Association between the triglyceride-glucose index and abdominal aortic calcification in adults: a cross-sectional study. Nutr Metab Cardiovasc Dis. 2021;31(7):2068–76. pmid:34053833
- View Article
- PubMed/NCBI
- Google Scholar
22. Schousboe JT, DeBold CR. Reliability and accuracy of vertebral fracture assessment with densitometry compared to radiography in clinical practice. Osteoporos Int. 2005;17(2):281–9.
- View Article
- Google Scholar
23. Zhou L, Wen X, Peng Y, Guo M, Zhao L. Red blood cell folate and severe abdominal aortic calcification: results from the NHANES 2013-2014. Nutr Metab Cardiovasc Dis. 2021;31(1):186–92. pmid:32988723
- View Article
- PubMed/NCBI
- Google Scholar
24. Moorthi RN, Moe SM. Recent advances in the noninvasive diagnosis of renal osteodystrophy. Kidney Int. 2013;84(5):886–94. pmid:23802194
- View Article
- PubMed/NCBI
- Google Scholar
25. Erratum: Kidney disease: improving global outcomes (KDIGO) CKD-MBD update work group. KDIGO 2017 clinical practice guideline update for the diagnosis, evaluation, prevention, and treatment of chronic kidney disease-mineral and bone disorder (CKD-MBD). Kidney Int Suppl. 2017;7:1–59. Kidney Int Suppl (2011). 2017;7(3):e1. pmid:30681074
- View Article
- PubMed/NCBI
- Google Scholar
26. Niu Q, Hong Y, Lee C-H, Men C, Zhao H, Zuo L. Abdominal aortic calcification can predict all-cause mortality and CV events in dialysis patients: a systematic review and meta-analysis. PLoS One. 2018;13(9):e0204526. pmid:30240443
- View Article
- PubMed/NCBI
- Google Scholar
27. Rodríguez AJ, Lewis JR, Scott DS, Kiel DP, Schousboe JT, Ebeling PR, et al. Aortic calcification is associated with five-year decline in handgrip strength in older women. Calcif Tissue Int. 2018;103(6):589–98. pmid:30039226
- View Article
- PubMed/NCBI
- Google Scholar
28. Lewis JR, Schousboe JT, Lim WH, Wong G, Zhu K, Lim EM, et al. Abdominal aortic calcification identified on lateral spine images from bone densitometers are a marker of generalized atherosclerosis in elderly women. Arterioscler Thromb Vasc Biol. 2016;36(1):166–73. pmid:26603153
- View Article
- PubMed/NCBI
- Google Scholar
29. Chen W, Eisenberg R, Mowrey WB, Wylie-Rosett J, Abramowitz MK, Bushinsky DA, et al. Association between dietary zinc intake and abdominal aortic calcification in US adults. Nephrol Dial Transplant. 2020;35(7):1171–8. pmid:31298287
- View Article
- PubMed/NCBI
- Google Scholar
30. Li Y-W, Chen W-L. Clinical relevance of serum uric acid and abdominal aortic-calcification in a national survey. Clin Cardiol. 2020;43(10):1194–201. pmid:32720347
- View Article
- PubMed/NCBI
- Google Scholar
31. Ramírez-Vélez R, García-Hermoso A, Correa-Rodríguez M, Lobelo F, González-Ruiz K, Izquierdo M. Abdominal aortic calcification is associated with decline in handgrip strength in the U.S. adult population ≥40 years of age. Nutr Metab Cardiovasc Dis. (2021) 31(4):1035–43. https://doi.10.1016/j.numecd.2020.11.003
- View Article
- Google Scholar
32. Qin Z, Du D, Li Y, Chang K, Yang Q, Zhang Z, et al. The association between weight-adjusted-waist index and abdominal aortic calcification in adults aged ≥ 40 years: results from NHANES 2013-2014. Sci Rep. (2022) 12(1):20354. https://doi.10.1038/s41598-022-24756-8
- View Article
- Google Scholar
33. Cheng Q, Wang Z, Zhong H, Zhou S, Liu C, Sun J, et al. Association of non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and gallstones among US adults aged ≤ 50 years: a cross-sectional study from NHANES 2017-2020. Lipids Health Dis. 2024;23(1):265. pmid:39175030
- View Article
- PubMed/NCBI
- Google Scholar
34. Yu B, Li M, Yu Z, Zheng T, Feng X, Gao A, et al. The non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) as a predictor of all-cause and cardiovascular mortality in US adults with diabetes or prediabetes: NHANES 1999-2018. BMC Med. 2024;22(1):317. pmid:39113030
- View Article
- PubMed/NCBI
- Google Scholar
35. World Health Organization. Obesity: preventing and managing the global epidemic: report of a WHO consultation. (2000). https://iris.who.int/handle/10665/42330
- View Article
- Google Scholar
36. American Diabetes Association Professional Practice Committee. diagnosis and classification of diabetes: standards of care in diabetes-2024. Diabetes Care. 2024;47(Suppl 1):S20–S42. https://doi.10.2337/dc24-S002
- View Article
- Google Scholar
37. Unger T, Borghi C, Charchar F, Khan NA, Poulter NR, Prabhakaran D, et al. 2020 International society of hypertension global hypertension practice guidelines. Hypertension. 2020;75(6):1334–57. pmid:32370572
- View Article
- PubMed/NCBI
- Google Scholar
38. Wu C-C, Wu C-H, Lee C-H, Cheng C-I. Association between neutrophil percentage-to-albumin ratio (NPAR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and long-term mortality in community-dwelling adults with heart failure: evidence from US NHANES 2005-2016. BMC Cardiovasc Disord. 2023;23(1):312. pmid:37344786
- View Article
- PubMed/NCBI
- Google Scholar
39. Muntner P, Hardy ST, Fine LJ, Jaeger BC, Wozniak G, Levitan EB, et al. Trends in blood pressure control among US adults with hypertension, 1999-2000 to 2017-2018. JAMA. 2020;324(12):1190–200. pmid:32902588
- View Article
- PubMed/NCBI
- Google Scholar
40. Farrar D, Fairley L, Santorelli G, Tuffnell D, Sheldon TA, Wright J, et al. Association between hyperglycaemia and adverse perinatal outcomes in south Asian and white British women: analysis of data from the Born in Bradford cohort. Lancet Diabetes Endocrinol. 2015;3(10):795–804. pmid:26355010
- View Article
- PubMed/NCBI
- Google Scholar
41. Chen J-H, Zhai E-T, Yuan Y-J, Wu K-M, Xu J-B, Peng J-J, et al. Systemic immune-inflammation index for predicting prognosis of colorectal cancer. World J Gastroenterol. 2017;23(34):6261–72. pmid:28974892
- View Article
- PubMed/NCBI
- Google Scholar
42. Wang L, Zhou Y, Xia S, Lu L, Dai T, Li A, et al. Prognostic value of the systemic inflammation response index (SIRI) before and after surgery in operable breast cancer patients. CBM. 2020;28(4):537–47.
- View Article
- Google Scholar
43. Wang L, Li X, Liu M, Zhou H, Shao J. Association between monocyte-to-lymphocyte ratio and prostate cancer in the U.S. population: a population-based study. Front Cell Dev Biol. 2024;12.
- View Article
- Google Scholar
44. Li X, Cui L, Xu H. Association between systemic inflammation response index and chronic kidney disease: a population-based study. Front Endocrinol (Lausanne). 2024;15:1329256. pmid:38455650
- View Article
- PubMed/NCBI
- Google Scholar
45. Zhang A-H, Dai G-X, Zhang Q-D, Huang H-D, Liu W-H. The value of peripheral blood cell ratios in primary membranous nephropathy: a single center retrospective study. J Inflamm Res. 2023;16:1017–25. pmid:36923464
- View Article
- PubMed/NCBI
- Google Scholar
46. Toraman A, Neşe N, Özyurt BC, Kürşat S. Association between neutrophil-lymphocyte & platelet lymphocyte ratios with prognosis & mortality in rapidly progressive glomerulonephritis. Indian J Med Res. 2019;150(4):399–406. pmid:31823922
- View Article
- PubMed/NCBI
- Google Scholar
47. Mao W, Wu J, Zhang Z, Xu Z, Xu B, Chen M. Neutrophil-lymphocyte ratio acts as a novel diagnostic biomarker for kidney stone prevalence and number of stones passed. Transl Androl Urol. 2021;10(1):77–86. pmid:33532298
- View Article
- PubMed/NCBI
- Google Scholar
48. Tung H-T, Liu C-M, Huang H-S, Lu Z-H, Liu C-J. Increased risk of chronic kidney disease in uric acid stone formers with high neutrophil-to-lymphocyte ratio. Sci Rep. 2023;13(1):17686. pmid:37848540
- View Article
- PubMed/NCBI
- Google Scholar
49. Zhao E, Cheng Y, Yu C, Li H, Fan X. The systemic immune-inflammation index was non-linear associated with all-cause mortality in individuals with nonalcoholic fatty liver disease. Ann Med. 2023;55(1):2197652. pmid:37052341
- View Article
- PubMed/NCBI
- Google Scholar
50. Dai L, Golembiewska E, Lindholm B, Stenvinkel P. End-stage renal disease, inflammation and cardiovascular outcomes. Contrib Nephrol. 2017;191:32–43. pmid:28910789
- View Article
- PubMed/NCBI
- Google Scholar
51. Zhang K, Gao J, Chen J, Liu X, Cai Q, Liu P, et al. MICS, an easily ignored contributor to arterial calcification in CKD patients. Am J Physiol Renal Physiol. 2016;311(4):F663–70. pmid:27335374
- View Article
- PubMed/NCBI
- Google Scholar
52. Kurozumi A, Nakano K, Yamagata K, Okada Y, Nakayamada S, Tanaka Y. IL-6 and sIL-6R induces STAT3-dependent differentiation of human VSMCs into osteoblast-like cells through JMJD2B-mediated histone demethylation of RUNX2. Bone. 2019;124:53–61.
- View Article
- Google Scholar
53. Sage AP, Tintut Y, Demer LL. Regulatory mechanisms in vascular calcification. Nat Rev Cardiol. 2010;7(9):528–36.
- View Article
- Google Scholar
54. Singh S, Anshita D, Ravichandiran V. MCP-1: function, regulation, and involvement in disease. Int Immunopharmacol. 2021;101(Pt B):107598. pmid:34233864
- View Article
- PubMed/NCBI
- Google Scholar
55. Wang M, Pan W, Xu Y, Zhang J, Wan J, Jiang H. Microglia-mediated neuroinflammation: a potential target for the treatment of cardiovascular diseases. J Inflamm Res. 2022;15:3083–94. pmid:35642214
- View Article
- PubMed/NCBI
- Google Scholar
56. Amsalem I, Asher E, Blaufeld I, Hitter R, Levi N, Taha L, et al. Mean platelet volume as a predictor of coronary artery disease severity and its association with coronary artery calcification. Clin Appl Thromb Hemost. 2023;29:10760296231159113. pmid:36999275
- View Article
- PubMed/NCBI
- Google Scholar
57. Davì G, Patrono C. Platelet activation and atherothrombosis. N Engl J Med. 2007;357(24):2482–94. pmid:18077812
- View Article
- PubMed/NCBI
- Google Scholar
58. Mozos I, Malainer C, Horbańczuk J, Gug C, Stoian D, Luca CT, et al. Inflammatory markers for arterial stiffness in cardiovascular diseases. Front Immunol. 2017;8:1058. pmid:28912780
- View Article
- PubMed/NCBI
- Google Scholar
59. Shah AD, Denaxas S, Nicholas O, Hingorani AD, Hemingway H. Neutrophil counts and initial presentation of 12 cardiovascular diseases: a CALIBER cohort study. J Am Coll Cardiol. (2017) 69(9):1160–69. https://doi.10.1016/j.jacc.2016.12.022
- View Article
- Google Scholar
60. Silvestre-Roig C, Braster Q, Ortega-Gomez A, Soehnlein O. Neutrophils as regulators of cardiovascular inflammation. Nat Rev Cardiol. 2020;17(6):327–40. pmid:31996800
- View Article
- PubMed/NCBI
- Google Scholar
61. Qin Z, Chang K, Liao R, Jiang L, Yang Q, Su B. Greater dietary inflammatory potential is associated with higher likelihood of abdominal aortic calcification. Front Cardiovasc Med. 2021;8:720834. pmid:34485417
- View Article
- PubMed/NCBI
- Google Scholar
62. Zhao X, Zhu M, Wang S, Zhang T, Wei X, Wang C, et al. Transcription factor 21 accelerates vascular calcification in mice by activating the IL-6/STAT3 signaling pathway and the interplay between VSMCs and ECs. Acta Pharmacol Sin. 2023;44(8):1625–36.
- View Article
- Google Scholar
63. Shoelson SE. Inflammation and insulin resistance. J Clin Invest. 2006;116(7):1793–801.
- View Article
- Google Scholar
64. Marx N, Federici M, Schütt K, Müller-Wieland D, Ajjan RA, Antunes MJ, et al. 2023 ESC Guidelines for the management of cardiovascular disease in patients with diabetes. Eur Heart J. 2023;44(39):4043–140. pmid:37622663
- View Article
- PubMed/NCBI
- Google Scholar
65. Cheng Q, Sun J, Zhong H, Wang Z, Liu C, Zhou S, et al. Research trends in lipid-lowering therapies for coronary heart disease combined with hyperlipidemia: a bibliometric study and visual analysis. Front Pharmacol. 2024;15:1393333. pmid:38828451
- View Article
- PubMed/NCBI
- Google Scholar
66. Mary A, Hartemann A, Liabeuf S, Aubert CE, Kemel S, Salem JE, et al. Association between metformin use and below-the-knee arterial calcification score in type 2 diabetic patients. Cardiovasc Diabetol. 2017;16(1):24. pmid:28202017
- View Article
- PubMed/NCBI
- Google Scholar
67. Shahrour HE, Al Fahom S, Al-Massarani G, AlSaadi AR, Magni P. Osteocalcin-expressing endothelial progenitor cells and serum osteocalcin forms are independent biomarkers of coronary atherosclerotic disease severity in male and female patients. J Endocrinol Invest. 2022;45(6):1173–80. pmid:35089541
- View Article
- PubMed/NCBI
- Google Scholar
68. Jiang L, Song X, Yan L, Liu Y, Qiao X, Zhang W. Molecular insights into the interplay between type 2 diabetes mellitus and osteoporosis: implications for endocrine health. Front Endocrinol (Lausanne). 2025;15:1483512. pmid:39897963
- View Article
- PubMed/NCBI
- Google Scholar
69. Harper E, Forde H, Davenport C, Rochfort KD, Smith D, Cummins PM. Vascular calcification in type-2 diabetes and cardiovascular disease: integrative roles for OPG, RANKL and TRAIL. Vascul Pharmacol. 2016;82:30–40. pmid:26924459
- View Article
- PubMed/NCBI
- Google Scholar
70. Russo D, Morrone LFP, Imbriaco M, Pota A, Russo L, Scognamiglio B, et al. Coronary artery calcification and outcomes in diabetic patients with and without chronic kidney disease. Blood Purif. 2013;36(1):17–20. pmid:23735333
- View Article
- PubMed/NCBI
- Google Scholar
71. Ban TH, Choi BS, Yoon SA, Kim Y, Jin K, Kim G-H, et al. Clinical significance of neutrophil-to-lymphocyte ratio on the risk of abdominal aortic calcification and decreased bone mineral density in patients with end-stage kidney disease. PLoS One. 2023;18(10):e0286612. pmid:37878613
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. GBD 2021 Causes of Death Collaborators. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2100–32. pmid:38582094
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Ngai D, Lino M, Bendeck MP. Cell-Matrix interactions and matricrine signaling in the pathogenesis of vascular calcification. Front Cardiovasc Med. 2018;5:174. pmid:30581820
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Leopold JA. Vascular calcification: mechanisms of vascular smooth muscle cell calcification. Trends Cardiovasc Med. 2015;25(4):267–74. pmid:25435520
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. Kauppila LI, Polak JF, Cupples LA, Hannan MT, Kiel DP, Wilson PW. New indices to classify location, severity and progression of calcific lesions in the abdominal aorta: a 25-year follow-up study. Atherosclerosis. 1997;132(2):245–50. pmid:9242971
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref5] 5. Qin Z, Liu Q, Jiao P, Geng J, Liao R, Su B. Higher blood cadmium concentration is associated with increased likelihood of abdominal aortic calcification. Front Cardiovasc Med. 2022;9:870169. pmid:35557529
View Article
PubMed/NCBI
Google Scholar

[18] View Article

[19] PubMed/NCBI

[20] Google Scholar

[ref6] 6. Chavarria A, Alcocer-Varela J. Is damage in central nervous system due to inflammation?. Autoimmun Rev. 2004;3(4):251–60. pmid:15246020
View Article
PubMed/NCBI
Google Scholar

[22] View Article

[23] PubMed/NCBI

[24] Google Scholar

[ref7] 7. Passos LSA, Lupieri A, Becker-Greene D, Aikawa E. Innate and adaptive immunity in cardiovascular calcification. Atherosclerosis. 2020;306:59–67. pmid:32222287
View Article
PubMed/NCBI
Google Scholar

[26] View Article

[27] PubMed/NCBI

[28] Google Scholar

[ref8] 8. Hu C-T, Shao Y-D, Liu Y-Z, Xiao X, Cheng Z-B, Qu S-L, et al. Oxidative stress in vascular calcification. Clin Chim Acta. 2021;519:101–10. pmid:33887264
View Article
PubMed/NCBI
Google Scholar

[30] View Article

[31] PubMed/NCBI

[32] Google Scholar

[ref9] 9. Meng X, Yang J, Dong M, Zhang K, Tu E, Gao Q, et al. Regulatory T cells in cardiovascular diseases. Nat Rev Cardiol. 2015;13(3):167–79.
View Article
Google Scholar

[34] View Article

[35] Google Scholar

[ref10] 10. Turkmen K, Ozcicek F, Ozcicek A, Akbas EM, Erdur FM, Tonbul HZ. The relationship between neutrophil‐to‐lymphocyte ratio and vascular calcification in end‐stage renal disease patients. Hemodial Int. 2013;18(1):47–53.
View Article
Google Scholar

[37] View Article

[38] Google Scholar

[ref11] 11. Xie R, Liu X, Wu H, Liu M, Zhang Y. Associations between systemic immune-inflammation index and abdominal aortic calcification: results of a nationwide survey. Nutr Metab Cardiovasc Dis. 2023;33(7):1437–43. pmid:37156667
View Article
PubMed/NCBI
Google Scholar

[40] View Article

[41] PubMed/NCBI

[42] Google Scholar

[ref12] 12. Jin C, Li X, Luo Y, Zhang C, Zuo D. Associations between pan-immune-inflammation value and abdominal aortic calcification: a cross-sectional study. Front Immunol. 2024;15:1370516. pmid:38605946
View Article
PubMed/NCBI
Google Scholar

[44] View Article

[45] PubMed/NCBI

[46] Google Scholar

[ref13] 13. Kaplangoray M, Toprak K, Deveci E, Caglayan C, Şahin E. Could pan-immune-inflammation value be a marker for the diagnosis of coronary slow flow phenomenon?. Cardiovasc Toxicol. 2024;24(5):519–26. pmid:38622332
View Article
PubMed/NCBI
Google Scholar

[48] View Article

[49] PubMed/NCBI

[50] Google Scholar

[ref14] 14. Liu K, Tang S, Liu C, Ma J, Cao X, Yang X, et al. Systemic immune-inflammatory biomarkers (SII, NLR, PLR and LMR) linked to non-alcoholic fatty liver disease risk. Front Immunol. 2024;15:1337241. pmid:38481995
View Article
PubMed/NCBI
Google Scholar

[52] View Article

[53] PubMed/NCBI

[54] Google Scholar

[ref15] 15. Erdogan T. Role of systemic immune-inflammation index in asthma and NSAID-exacerbated respiratory disease. Clin Respir J. 2021;15(4):400–5. pmid:33249745
View Article
PubMed/NCBI
Google Scholar

[56] View Article

[57] PubMed/NCBI

[58] Google Scholar

[ref16] 16. Mandaliya H, Jones M, Oldmeadow C, Nordman II. Prognostic biomarkers in stage IV non-small cell lung cancer (NSCLC): neutrophil to lymphocyte ratio (NLR), lymphocyte to monocyte ratio (LMR), platelet to lymphocyte ratio (PLR) and advanced lung cancer inflammation index (ALI). Transl Lung Cancer Res. 2019;8(6):886–94. pmid:32010567
View Article
PubMed/NCBI
Google Scholar

[60] View Article

[61] PubMed/NCBI

[62] Google Scholar

[ref17] 17. Wang R-H, Wen W-X, Jiang Z-P, Du Z-P, Ma Z-H, Lu A-L, et al. The clinical value of neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), platelet-to-lymphocyte ratio (PLR) and systemic inflammation response index (SIRI) for predicting the occurrence and severity of pneumonia in patients with intracerebral hemorrhage. Front Immunol. 2023;14: 1115031.
View Article
Google Scholar

[64] View Article

[65] Google Scholar

[ref18] 18. Song Y, Zhao Y, Shu Y, Zhang L, Cheng W, Wang L, et al. Combination model of neutrophil to high-density lipoprotein ratio and system inflammation response index is more valuable for predicting peripheral arterial disease in type 2 diabetic patients: a cross-sectional study. Front Endocrinol (Lausanne). 2023;14:1100453. pmid:36875480
View Article
PubMed/NCBI
Google Scholar

[67] View Article

[68] PubMed/NCBI

[69] Google Scholar

[ref19] 19. Fani L, van Dam-Nolen DHK, Vernooij M, Kavousi M, van der Lugt A, Bos D. Circulatory markers of immunity and carotid atherosclerotic plaque. Atherosclerosis. 2021;325:69–74. pmid:33894597
View Article
PubMed/NCBI
Google Scholar

[71] View Article

[72] PubMed/NCBI

[73] Google Scholar

[ref20] 20. Zhang Y, Xie R, Ou J. A U-shaped association between serum albumin with total triiodothyronine in adults. J Clin Lab Anal. 2022;36(6):e24473. pmid:35524426
View Article
PubMed/NCBI
Google Scholar

[75] View Article

[76] PubMed/NCBI

[77] Google Scholar

[ref21] 21. Chen Y, Chang Z, Zhao Y, Liu Y, Fu J, Zhang Y, et al. Association between the triglyceride-glucose index and abdominal aortic calcification in adults: a cross-sectional study. Nutr Metab Cardiovasc Dis. 2021;31(7):2068–76. pmid:34053833
View Article
PubMed/NCBI
Google Scholar

[79] View Article

[80] PubMed/NCBI

[81] Google Scholar

[ref22] 22. Schousboe JT, DeBold CR. Reliability and accuracy of vertebral fracture assessment with densitometry compared to radiography in clinical practice. Osteoporos Int. 2005;17(2):281–9.
View Article
Google Scholar

[83] View Article

[84] Google Scholar

[ref23] 23. Zhou L, Wen X, Peng Y, Guo M, Zhao L. Red blood cell folate and severe abdominal aortic calcification: results from the NHANES 2013-2014. Nutr Metab Cardiovasc Dis. 2021;31(1):186–92. pmid:32988723
View Article
PubMed/NCBI
Google Scholar

[86] View Article

[87] PubMed/NCBI

[88] Google Scholar

[ref24] 24. Moorthi RN, Moe SM. Recent advances in the noninvasive diagnosis of renal osteodystrophy. Kidney Int. 2013;84(5):886–94. pmid:23802194
View Article
PubMed/NCBI
Google Scholar

[90] View Article

[91] PubMed/NCBI

[92] Google Scholar

[ref25] 25. Erratum: Kidney disease: improving global outcomes (KDIGO) CKD-MBD update work group. KDIGO 2017 clinical practice guideline update for the diagnosis, evaluation, prevention, and treatment of chronic kidney disease-mineral and bone disorder (CKD-MBD). Kidney Int Suppl. 2017;7:1–59. Kidney Int Suppl (2011). 2017;7(3):e1. pmid:30681074
View Article
PubMed/NCBI
Google Scholar

[94] View Article

[95] PubMed/NCBI

[96] Google Scholar

[ref26] 26. Niu Q, Hong Y, Lee C-H, Men C, Zhao H, Zuo L. Abdominal aortic calcification can predict all-cause mortality and CV events in dialysis patients: a systematic review and meta-analysis. PLoS One. 2018;13(9):e0204526. pmid:30240443
View Article
PubMed/NCBI
Google Scholar

[98] View Article

[99] PubMed/NCBI

[100] Google Scholar

[ref27] 27. Rodríguez AJ, Lewis JR, Scott DS, Kiel DP, Schousboe JT, Ebeling PR, et al. Aortic calcification is associated with five-year decline in handgrip strength in older women. Calcif Tissue Int. 2018;103(6):589–98. pmid:30039226
View Article
PubMed/NCBI
Google Scholar

[102] View Article

[103] PubMed/NCBI

[104] Google Scholar

[ref28] 28. Lewis JR, Schousboe JT, Lim WH, Wong G, Zhu K, Lim EM, et al. Abdominal aortic calcification identified on lateral spine images from bone densitometers are a marker of generalized atherosclerosis in elderly women. Arterioscler Thromb Vasc Biol. 2016;36(1):166–73. pmid:26603153
View Article
PubMed/NCBI
Google Scholar

[106] View Article

[107] PubMed/NCBI

[108] Google Scholar

[ref29] 29. Chen W, Eisenberg R, Mowrey WB, Wylie-Rosett J, Abramowitz MK, Bushinsky DA, et al. Association between dietary zinc intake and abdominal aortic calcification in US adults. Nephrol Dial Transplant. 2020;35(7):1171–8. pmid:31298287
View Article
PubMed/NCBI
Google Scholar

[110] View Article

[111] PubMed/NCBI

[112] Google Scholar

[ref30] 30. Li Y-W, Chen W-L. Clinical relevance of serum uric acid and abdominal aortic-calcification in a national survey. Clin Cardiol. 2020;43(10):1194–201. pmid:32720347
View Article
PubMed/NCBI
Google Scholar

[114] View Article

[115] PubMed/NCBI

[116] Google Scholar

[ref31] 31. Ramírez-Vélez R, García-Hermoso A, Correa-Rodríguez M, Lobelo F, González-Ruiz K, Izquierdo M. Abdominal aortic calcification is associated with decline in handgrip strength in the U.S. adult population ≥40 years of age. Nutr Metab Cardiovasc Dis. (2021) 31(4):1035–43. https://doi.10.1016/j.numecd.2020.11.003
View Article
Google Scholar

[118] View Article

[119] Google Scholar

[ref32] 32. Qin Z, Du D, Li Y, Chang K, Yang Q, Zhang Z, et al. The association between weight-adjusted-waist index and abdominal aortic calcification in adults aged ≥ 40 years: results from NHANES 2013-2014. Sci Rep. (2022) 12(1):20354. https://doi.10.1038/s41598-022-24756-8
View Article
Google Scholar

[121] View Article

[122] Google Scholar

[ref33] 33. Cheng Q, Wang Z, Zhong H, Zhou S, Liu C, Sun J, et al. Association of non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and gallstones among US adults aged ≤ 50 years: a cross-sectional study from NHANES 2017-2020. Lipids Health Dis. 2024;23(1):265. pmid:39175030
View Article
PubMed/NCBI
Google Scholar

[124] View Article

[125] PubMed/NCBI

[126] Google Scholar

[ref34] 34. Yu B, Li M, Yu Z, Zheng T, Feng X, Gao A, et al. The non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) as a predictor of all-cause and cardiovascular mortality in US adults with diabetes or prediabetes: NHANES 1999-2018. BMC Med. 2024;22(1):317. pmid:39113030
View Article
PubMed/NCBI
Google Scholar

[128] View Article

[129] PubMed/NCBI

[130] Google Scholar

[ref35] 35. World Health Organization. Obesity: preventing and managing the global epidemic: report of a WHO consultation. (2000). https://iris.who.int/handle/10665/42330
View Article
Google Scholar

[132] View Article

[133] Google Scholar

[ref36] 36. American Diabetes Association Professional Practice Committee. diagnosis and classification of diabetes: standards of care in diabetes-2024. Diabetes Care. 2024;47(Suppl 1):S20–S42. https://doi.10.2337/dc24-S002
View Article
Google Scholar

[135] View Article

[136] Google Scholar

[ref37] 37. Unger T, Borghi C, Charchar F, Khan NA, Poulter NR, Prabhakaran D, et al. 2020 International society of hypertension global hypertension practice guidelines. Hypertension. 2020;75(6):1334–57. pmid:32370572
View Article
PubMed/NCBI
Google Scholar

[138] View Article

[139] PubMed/NCBI

[140] Google Scholar

[ref38] 38. Wu C-C, Wu C-H, Lee C-H, Cheng C-I. Association between neutrophil percentage-to-albumin ratio (NPAR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and long-term mortality in community-dwelling adults with heart failure: evidence from US NHANES 2005-2016. BMC Cardiovasc Disord. 2023;23(1):312. pmid:37344786
View Article
PubMed/NCBI
Google Scholar

[142] View Article

[143] PubMed/NCBI

[144] Google Scholar

[ref39] 39. Muntner P, Hardy ST, Fine LJ, Jaeger BC, Wozniak G, Levitan EB, et al. Trends in blood pressure control among US adults with hypertension, 1999-2000 to 2017-2018. JAMA. 2020;324(12):1190–200. pmid:32902588
View Article
PubMed/NCBI
Google Scholar

[146] View Article

[147] PubMed/NCBI

[148] Google Scholar

[ref40] 40. Farrar D, Fairley L, Santorelli G, Tuffnell D, Sheldon TA, Wright J, et al. Association between hyperglycaemia and adverse perinatal outcomes in south Asian and white British women: analysis of data from the Born in Bradford cohort. Lancet Diabetes Endocrinol. 2015;3(10):795–804. pmid:26355010
View Article
PubMed/NCBI
Google Scholar

[150] View Article

[151] PubMed/NCBI

[152] Google Scholar

[ref41] 41. Chen J-H, Zhai E-T, Yuan Y-J, Wu K-M, Xu J-B, Peng J-J, et al. Systemic immune-inflammation index for predicting prognosis of colorectal cancer. World J Gastroenterol. 2017;23(34):6261–72. pmid:28974892
View Article
PubMed/NCBI
Google Scholar

[154] View Article

[155] PubMed/NCBI

[156] Google Scholar

[ref42] 42. Wang L, Zhou Y, Xia S, Lu L, Dai T, Li A, et al. Prognostic value of the systemic inflammation response index (SIRI) before and after surgery in operable breast cancer patients. CBM. 2020;28(4):537–47.
View Article
Google Scholar

[158] View Article

[159] Google Scholar

[ref43] 43. Wang L, Li X, Liu M, Zhou H, Shao J. Association between monocyte-to-lymphocyte ratio and prostate cancer in the U.S. population: a population-based study. Front Cell Dev Biol. 2024;12.
View Article
Google Scholar

[161] View Article

[162] Google Scholar

[ref44] 44. Li X, Cui L, Xu H. Association between systemic inflammation response index and chronic kidney disease: a population-based study. Front Endocrinol (Lausanne). 2024;15:1329256. pmid:38455650
View Article
PubMed/NCBI
Google Scholar

[164] View Article

[165] PubMed/NCBI

[166] Google Scholar

[ref45] 45. Zhang A-H, Dai G-X, Zhang Q-D, Huang H-D, Liu W-H. The value of peripheral blood cell ratios in primary membranous nephropathy: a single center retrospective study. J Inflamm Res. 2023;16:1017–25. pmid:36923464
View Article
PubMed/NCBI
Google Scholar

[168] View Article

[169] PubMed/NCBI

[170] Google Scholar

[ref46] 46. Toraman A, Neşe N, Özyurt BC, Kürşat S. Association between neutrophil-lymphocyte & platelet lymphocyte ratios with prognosis & mortality in rapidly progressive glomerulonephritis. Indian J Med Res. 2019;150(4):399–406. pmid:31823922
View Article
PubMed/NCBI
Google Scholar

[172] View Article

[173] PubMed/NCBI

[174] Google Scholar

[ref47] 47. Mao W, Wu J, Zhang Z, Xu Z, Xu B, Chen M. Neutrophil-lymphocyte ratio acts as a novel diagnostic biomarker for kidney stone prevalence and number of stones passed. Transl Androl Urol. 2021;10(1):77–86. pmid:33532298
View Article
PubMed/NCBI
Google Scholar

[176] View Article

[177] PubMed/NCBI

[178] Google Scholar

[ref48] 48. Tung H-T, Liu C-M, Huang H-S, Lu Z-H, Liu C-J. Increased risk of chronic kidney disease in uric acid stone formers with high neutrophil-to-lymphocyte ratio. Sci Rep. 2023;13(1):17686. pmid:37848540
View Article
PubMed/NCBI
Google Scholar

[180] View Article

[181] PubMed/NCBI

[182] Google Scholar

[ref49] 49. Zhao E, Cheng Y, Yu C, Li H, Fan X. The systemic immune-inflammation index was non-linear associated with all-cause mortality in individuals with nonalcoholic fatty liver disease. Ann Med. 2023;55(1):2197652. pmid:37052341
View Article
PubMed/NCBI
Google Scholar

[184] View Article

[185] PubMed/NCBI

[186] Google Scholar

[ref50] 50. Dai L, Golembiewska E, Lindholm B, Stenvinkel P. End-stage renal disease, inflammation and cardiovascular outcomes. Contrib Nephrol. 2017;191:32–43. pmid:28910789
View Article
PubMed/NCBI
Google Scholar

[188] View Article

[189] PubMed/NCBI

[190] Google Scholar

[ref51] 51. Zhang K, Gao J, Chen J, Liu X, Cai Q, Liu P, et al. MICS, an easily ignored contributor to arterial calcification in CKD patients. Am J Physiol Renal Physiol. 2016;311(4):F663–70. pmid:27335374
View Article
PubMed/NCBI
Google Scholar

[192] View Article

[193] PubMed/NCBI

[194] Google Scholar

[ref52] 52. Kurozumi A, Nakano K, Yamagata K, Okada Y, Nakayamada S, Tanaka Y. IL-6 and sIL-6R induces STAT3-dependent differentiation of human VSMCs into osteoblast-like cells through JMJD2B-mediated histone demethylation of RUNX2. Bone. 2019;124:53–61.
View Article
Google Scholar

[196] View Article

[197] Google Scholar

[ref53] 53. Sage AP, Tintut Y, Demer LL. Regulatory mechanisms in vascular calcification. Nat Rev Cardiol. 2010;7(9):528–36.
View Article
Google Scholar

[199] View Article

[200] Google Scholar

[ref54] 54. Singh S, Anshita D, Ravichandiran V. MCP-1: function, regulation, and involvement in disease. Int Immunopharmacol. 2021;101(Pt B):107598. pmid:34233864
View Article
PubMed/NCBI
Google Scholar

[202] View Article

[203] PubMed/NCBI

[204] Google Scholar

[ref55] 55. Wang M, Pan W, Xu Y, Zhang J, Wan J, Jiang H. Microglia-mediated neuroinflammation: a potential target for the treatment of cardiovascular diseases. J Inflamm Res. 2022;15:3083–94. pmid:35642214
View Article
PubMed/NCBI
Google Scholar

[206] View Article

[207] PubMed/NCBI

[208] Google Scholar

[ref56] 56. Amsalem I, Asher E, Blaufeld I, Hitter R, Levi N, Taha L, et al. Mean platelet volume as a predictor of coronary artery disease severity and its association with coronary artery calcification. Clin Appl Thromb Hemost. 2023;29:10760296231159113. pmid:36999275
View Article
PubMed/NCBI
Google Scholar

[210] View Article

[211] PubMed/NCBI

[212] Google Scholar

[ref57] 57. Davì G, Patrono C. Platelet activation and atherothrombosis. N Engl J Med. 2007;357(24):2482–94. pmid:18077812
View Article
PubMed/NCBI
Google Scholar

[214] View Article

[215] PubMed/NCBI

[216] Google Scholar

[ref58] 58. Mozos I, Malainer C, Horbańczuk J, Gug C, Stoian D, Luca CT, et al. Inflammatory markers for arterial stiffness in cardiovascular diseases. Front Immunol. 2017;8:1058. pmid:28912780
View Article
PubMed/NCBI
Google Scholar

[218] View Article

[219] PubMed/NCBI

[220] Google Scholar

[ref59] 59. Shah AD, Denaxas S, Nicholas O, Hingorani AD, Hemingway H. Neutrophil counts and initial presentation of 12 cardiovascular diseases: a CALIBER cohort study. J Am Coll Cardiol. (2017) 69(9):1160–69. https://doi.10.1016/j.jacc.2016.12.022
View Article
Google Scholar

[222] View Article

[223] Google Scholar

[ref60] 60. Silvestre-Roig C, Braster Q, Ortega-Gomez A, Soehnlein O. Neutrophils as regulators of cardiovascular inflammation. Nat Rev Cardiol. 2020;17(6):327–40. pmid:31996800
View Article
PubMed/NCBI
Google Scholar

[225] View Article

[226] PubMed/NCBI

[227] Google Scholar

[ref61] 61. Qin Z, Chang K, Liao R, Jiang L, Yang Q, Su B. Greater dietary inflammatory potential is associated with higher likelihood of abdominal aortic calcification. Front Cardiovasc Med. 2021;8:720834. pmid:34485417
View Article
PubMed/NCBI
Google Scholar

[229] View Article

[230] PubMed/NCBI

[231] Google Scholar

[ref62] 62. Zhao X, Zhu M, Wang S, Zhang T, Wei X, Wang C, et al. Transcription factor 21 accelerates vascular calcification in mice by activating the IL-6/STAT3 signaling pathway and the interplay between VSMCs and ECs. Acta Pharmacol Sin. 2023;44(8):1625–36.
View Article
Google Scholar

[233] View Article

[234] Google Scholar

[ref63] 63. Shoelson SE. Inflammation and insulin resistance. J Clin Invest. 2006;116(7):1793–801.
View Article
Google Scholar

[236] View Article

[237] Google Scholar

[ref64] 64. Marx N, Federici M, Schütt K, Müller-Wieland D, Ajjan RA, Antunes MJ, et al. 2023 ESC Guidelines for the management of cardiovascular disease in patients with diabetes. Eur Heart J. 2023;44(39):4043–140. pmid:37622663
View Article
PubMed/NCBI
Google Scholar

[239] View Article

[240] PubMed/NCBI

[241] Google Scholar

[ref65] 65. Cheng Q, Sun J, Zhong H, Wang Z, Liu C, Zhou S, et al. Research trends in lipid-lowering therapies for coronary heart disease combined with hyperlipidemia: a bibliometric study and visual analysis. Front Pharmacol. 2024;15:1393333. pmid:38828451
View Article
PubMed/NCBI
Google Scholar

[243] View Article

[244] PubMed/NCBI

[245] Google Scholar

[ref66] 66. Mary A, Hartemann A, Liabeuf S, Aubert CE, Kemel S, Salem JE, et al. Association between metformin use and below-the-knee arterial calcification score in type 2 diabetic patients. Cardiovasc Diabetol. 2017;16(1):24. pmid:28202017
View Article
PubMed/NCBI
Google Scholar

[247] View Article

[248] PubMed/NCBI

[249] Google Scholar

[ref67] 67. Shahrour HE, Al Fahom S, Al-Massarani G, AlSaadi AR, Magni P. Osteocalcin-expressing endothelial progenitor cells and serum osteocalcin forms are independent biomarkers of coronary atherosclerotic disease severity in male and female patients. J Endocrinol Invest. 2022;45(6):1173–80. pmid:35089541
View Article
PubMed/NCBI
Google Scholar

[251] View Article

[252] PubMed/NCBI

[253] Google Scholar

[ref68] 68. Jiang L, Song X, Yan L, Liu Y, Qiao X, Zhang W. Molecular insights into the interplay between type 2 diabetes mellitus and osteoporosis: implications for endocrine health. Front Endocrinol (Lausanne). 2025;15:1483512. pmid:39897963
View Article
PubMed/NCBI
Google Scholar

[255] View Article

[256] PubMed/NCBI

[257] Google Scholar

[ref69] 69. Harper E, Forde H, Davenport C, Rochfort KD, Smith D, Cummins PM. Vascular calcification in type-2 diabetes and cardiovascular disease: integrative roles for OPG, RANKL and TRAIL. Vascul Pharmacol. 2016;82:30–40. pmid:26924459
View Article
PubMed/NCBI
Google Scholar

[259] View Article

[260] PubMed/NCBI

[261] Google Scholar

[ref70] 70. Russo D, Morrone LFP, Imbriaco M, Pota A, Russo L, Scognamiglio B, et al. Coronary artery calcification and outcomes in diabetic patients with and without chronic kidney disease. Blood Purif. 2013;36(1):17–20. pmid:23735333
View Article
PubMed/NCBI
Google Scholar

[263] View Article

[264] PubMed/NCBI

[265] Google Scholar

[ref71] 71. Ban TH, Choi BS, Yoon SA, Kim Y, Jin K, Kim G-H, et al. Clinical significance of neutrophil-to-lymphocyte ratio on the risk of abdominal aortic calcification and decreased bone mineral density in patients with end-stage kidney disease. PLoS One. 2023;18(10):e0286612. pmid:37878613
View Article
PubMed/NCBI
Google Scholar

[267] View Article

[268] PubMed/NCBI

[269] Google Scholar

Figures

Abstract

Objective

Methods

Results

Conclusions

Introduction

Methods

Study design and population

Evaluation of severe AAC

Covariate

Statistical analysis

Results

Baseline characteristics of participants

Assessment of multicollinearity of variables

Correlation of odds of severe AAC with systemic immunoinflammatory biomarkers (SII, SIRI, AISI, PLR, NLR, and MLR)

Dose-response relationship regarding severe AAC odds and systemic immunoinflammatory biomarkers (SII, SIRI, AISI, PLR, NLR, MLR)

Subgroup and interaction analyses

Correlation of systemic immunoinflammatory biomarkers with abdominal aortic calcification of participants

Diagnostic power of different systemic immunoinflammatory biomarkers for severe AAC

LASSO regression analysis and construction of the final model

Construction of the nomogram

Discussion

Conclusion

Supporting information

S1 Table. All covariates and categorical definition of some of them.

S2 Table. Kolmogorov-Smirnov tests for systemic immunoinflammatory biomarkers.

S3 Table. Multicollinearity assessment of covariates.

S4 Table. Subgroup and interaction analysis of the association between systemic immunoinflammatory biomarkers (SII, SIRI, AISI, PLR, NLR, MLR) and severe. AAC.

S5 Table. Spearman correlation of systemic immunoinflammatory biomarkers with abdominal aortic calcification of participants.

S6 Table. Diagnostic efficacy of systemic immunoinflammatory biomarkers (SII, SIRI, AISI, PLR, NLR, MLR) and constructed prediction model for severe. AAC.

S7 Table. Lasso regression analysis results (best log(λ1se) = −4.157).

S1 Fig. Histograms and theoretical normal curve overlays of systemic immunoinflammatory biomarkers.

S2 Fig. ROC curves and area under the curve (95% CI) of the prediction model for 10-fold cross validation.

Acknowledgments

References

S7 Table. Lasso regression analysis results (best log(λ_1se) = −4.157).