Skip to main content
Download PDF

Association between S100A12 and risk of peripheral arterial disease in patients with dyslipidemia: a cross-sectional study

BMC Cardiovascular Disorders volume 25, Article number: 313 (2025) Cite this article

Abstract

Objective

S100A12 acts as a pro-inflammatory agent in vivo, with a close relationship with plaque formation in patients with acute coronary syndrome (ACS), end-stage renal disease, and diabetes. Peripheral arterial disease (PAD) can lead to mobility difficulties and ultimately disability and amputation. The association between S100A12 and risk of peripheral arterial disease remains unclear. This study aims to investigate the association between S100A12 and the risk of PAD in patients with dyslipidemia.

Methods

From March 2023 to June 2024, 478 patients were included in this cross-sectional study. They were divided into PAD group (n = 105) and control group (n = 373) according to the presence or absence of PAD (The diagnosis of PAD is a combination of the patient’s clinical symptoms, imaging evidence and ankle-brachial index). Plasma S100A12 was detected by available kit. General information, disease history, smoking history, and laboratory indicators were collected from both groups. The relationship between S100A12 and the risk of PAD was analyzed using statistical methods.

Results

Levels of S100A12 were significantly higher in the PAD group of dyslipidemia [0.22 (0.13,1.49) ng/cL vs. 0.13 (0.10,0.18)ng/cL, p value < 0.001]. Univariate and multivariate logistic regression analyses suggested that the risk of PAD was significantly higher with increasing levels of S100A12 [Odd ratio (OR) (95%CI) = 2.264 (1.681, 3.047), p value < 0.05]. In addition, lower high-density lipoprotein cholesterol (HDL-C) level and diabetes mellitus (DM) were independent risk factors for PAD [OR (95%CI) = 0.388 (0.186,0.809), p value = 0.012; OR = 2.375 (1.527,3.695), p value < 0.001]. Subgroup analysis suggested that S100A12 was significantly and positively associated with the risk of PAD in all subgroups, regardless of whether HDL-C levels < 1.03 mmol/L, age > 60 years, and presence of diabetes or hypertension. Restricted cubic spline (RCS) curves suggested that the correlation between S100A12 and the risk of PAD was nonlinear (p-non-linear value < 0.05). The RCS curves showed that the positive correlation between S100A12 and the risk of PAD was stronger when the S100A12 level was less than 1.00ng/cL.

Conclusion

In conclusion, elevated S100A12 level is an independent risk factor for PAD in patients with dyslipidemia. In different subgroups, S100A12 was significantly and positively associated with the risk of PAD after adjusting for different factors. There is a non-linear relationship between S100A12 and the risk of PAD, with a stronger positive correlation at S100A12 levels below 1.00ng/cL. These findings implied that S100A12 is a potential biomarker for identifying patients with dyslipidemia who are at high risk of developing PAD. They also implied that S100A12 levels can be routinely monitored in dyslipidemic populations for the early detection of PAD and to guide the management of PAD. Finally, the results of this study emphasize that inflammation in dyslipidemia patients plays an important role in the development of PAD, suggesting that lipid control and immunomodulation may be effective in the prevention of PAD.

Clinical trial number

MR-35-24-038431.

Peer Review reports

Introduction

Peripheral arterial disease (PAD) is a disease caused by atherosclerosis, leading to narrowing or even blockage of the peripheral arteries, and ultimately leads to insufficient blood supply to tissues and organs [1]. PAD has shown a marked increase in global prevalence over recent decades [2]. It is estimated that at least 113 million people worldwide, and possibly as many as 236 million, suffer from PAD, although there is significant variation in the estimates of its prevalence [3]. PAD is associated with significantly increased risks of cardiovascular mortality and morbidity [2, 4]. However, current evidence suggests that PAD remains substantially underdiagnosed and undertreated compared to other cardiovascular diseases [2].

To stratify the risk for patients, studies analyzed the risk factors for mortality in PAD and found that age, hypertension, diabetes, smoking, and dyslipidemia are associated with an increased 10-year mortality rate in PAD patients [5, 6]. Lipid profiles, particularly LDL-C (low-density lipoprotein cholesterol), play a critical role in PAD. Many studies have shown that lipid levels, especially LDL-C, are important risk factors for PAD [7,8,9]. LDL-C on the one hand accumulates in the vascular wall and allows the formation of atherosclerotic plaques, and on the other hand promotes inflammation, which contributes to the progression of plaques [10].

S100A12, a calcium-binding protein secreted by neutrophils and macrophages, acts as a pro-inflammatory agent in vivo by binding to the receptor for advanced glycation end products (RAGE) and stimulating the transduction of a series of intracellular signaling pathways [11]. The binding of RAGE and its ligands is thought to play an important role in inflammation during atherosclerosis [12, 13]. The binding of S100A12 to the RAGE activates intracellular signaling pathways such as mitogen-activated protein kinase (MAPK) and nuclear factor-κB(NF-κB), thereby inducing the production of inflammatory cytokines [such as tumor necrosis factor-α(TNF-α), Interleukin-1β(IL-1β)] and adhesion molecules [such as intercellular adhesion molecule-1(ICAM-1), vascular cell adhesion molecule-1(VCAM-1)] [14]. These cytokines and adhesion molecules work together to recruit the inflammatory cells such as macrophages, neutrophils to infiltrate the vessel wall, exacerbating vascular inflammation, which leading to PAD. It has also been found that binding of S100A12 to RAGE activates Rac1, which triggers NADPH oxidase1 (Nox1)-dependent formation of reactive oxygen species (ROS) and promotes atherosclerosis. It has also been shown that S100A12 binds to CD36, which promotes oxLDL uptake and the formation of foam cells, exacerbating lipid core formation and plaque development [15]. It has been shown that there is a significant correlation between S100A12 and the risk of atherosclerosis in patients with diabetes mellitus (DM) and end-stage renal disease [16, 17]. It has also been shown that S100A12 levels are significantly elevated in patients with coronary artery disease and are a potential predictor of CAD [18, 19].

The correlation between S100A12 and PAD risk in patients with dyslipidemia is still unclear. It is reasonable to hypothesize that the inflammatory response mediated by S100A12 promotes the formation and progression of atherosclerosis in people with dyslipidemia, thereby increases the risk of PAD. The present study was conducted to validate the above hypothesis.

Methods

Study population

This cross-sectional study was conducted at the Department of Cardiology, Shanghai Sixth People’s Hospital Fujian from March 2023 to June 2024. A total of 610 subjects were consecutively enrolled in the study. 518 patients over the age of 18 with dyslipidemia were included in the study. The criteria for dyslipidemia were referred to the “Chinese guidelines for lipid management (2023)” [20]. Patients were classified as having dyslipidemia if their baseline lipid levels failed to meet the lipid control targets corresponding to their risk stratification category (Tables 1 and 2).

The exclusion criteria are as follows: (1) severe renal insufficiency[eGFR ≤ 30 ml/(min*1.73 m²)]; (2) patients with malignant tumors; (3) patients with severe infections[sequential organ failure assessment (SOFA) > 2]; (4) patients with rheumatic immune diseases; (5) patients with severe trauma[abbreviated injury scale (AIS) > 3]; (6) patients with extreme obesity(BMI > 40 kg/m²). Samples with undiagnosed dyslipidemia and PAD due to missing data were excluded. Finally, 478 patients were included in the study (Fig. 1). Patients enrolled in the study would be collected for age, gender, height, weight, history of diseases, and smoking history. In addition, fasting venous blood would be drawn to obtain white blood cell count, creatinine, various lipid profiles, and glycosylated hemoglobin (HA1c) levels. Lipid profiles were tested by the kits purchased from Sichuan Mack Biological Co and LABOSPECT 008AS automatic biochemistry analyser (Hitachi, Japan) was used. WBC count was measured by XN-550 automatic modular blood and fluid analyser (Sysmex, Japan). HbA1c was measured by D100 Automatic Glycated Haemoglobin Analyser (Borel, USA) and the accompanying reagents. The calculating formulas for body mass index (BMI) and estimated glomerular filtration rate (eGFR) are shown below:

BMI (kg/m2)= (weight)/ (height2)

eGFR (Male)[ml/ (min*1.73m2)] = 186× (creatinine)−1.154× (age)−0.203

eGFR (Female)[ml/ (min*1.73m2)] = 186× (creatinine)−1.154× (age)−0.203 × 0.742

This study is in accordance with the 2013 revision of the Declaration of Helsinki. This study was approved by the Ethics Committee of Shanghai Sixth People’s Hospital Fujian (jjsyyyxll-2022030).The Clinical Trial Number is MR-35-24-038431.

Fig. 1
figure 1

Flowchart

Full size image

Diagnosis of peripheral arterial disease

PAD was diagnosed in patients who met all of the following criteria:

  1. 1)

    Clinical symptoms such as rest pain, claudication symptoms, and ischaemic ulcers.

  2. 2)

    With imaging evidence such as arterial ultrasound doppler.

  3. 3)

    Ankle-brachial blood pressure index (ABI) ≤ 0.80 in patients without a perior history of limb revascularization and ≤ 0.85 in patients with a perior history of limb revascularization [21].

Quantitative detection of S100A12

Fasting venous blood was drawn from the patients. Ethylenediaminetetraacetic acid (EDTA) anticoagulated samples were immediately centrifuged at 2000 g for 10 min, and plasma was placed in a refrigerator at -80℃ for assay. The concentration of S100A8/A9 and S100A12 were measured by ELISA method. And the kits were purchased from Shanghai Yansheng Biotechnology Industrial Co, LTD,. The analysisers were unaware of the subject’s diagnosis.

Statistical methods

The Kolmogorov-Smirnov test was used to check the normality of the distribution. All continuous variables included in this study did not conform to normal distribution after the normal distribution test. Therefore the analysis of the difference of continuous variables between the PAD and control groups was performed using the Kruskal-Wallis H test. Continuous variables were presented in quartiles. The study of the difference in categorical variables between the two groups was performed using the chi-square test. Categorical variables were presented as percentages. Available case analysis is used to handle missing data. Logistic regression was used for univariate and multivariate analysis of risk factors for PAD. The association between S100A12 and the risk of PAD was analyzed in subgroups based on whether patients were smokers, had DM or hypertension (HTN), ≥ 60 years, and had high-density lipoprotein cholesterol (HDL-C) ≥ 1.03 mmol/L. Restricted cubic spline (RCS) curves were used to explore the nonlinear relationship between S100A12 and the risk of PAD. Software packages R and SPSS29.0 were used to analyze the data. All statistical tests were two-sided, and p-values less than 0.05 were considered statistically significant differences.

Result

Baseline characteristics of the study subjects

As shown in Table 1, a total of 478 patients were enrolled in the study, including 105 in the PAD group and 373 in the control group. The patients in the PAD group were older compared to the control group, while the level of HDL-C was significantly lower, the level of HA1c and the percentage of hypertensive and diabetic patients was significantly higher (all p value < 0.05). The level of S100A12 was higher in the PAD patients compared to the control group [0.22 (0.13,1.49) vs. 0.13 (0.10,0.18), p value < 0.001].

Table 1 Baseline characteristics of the study subjects
Full size table

Univariate logistic regression analyses for the risk predictors of PAD

As shown in Fig. 2, univariate logistic regression analyses revealed that diabetes and plasma S100A12 levels were promotive factors for PAD, whereas HDL-C levels were protective factors for PAD.

Fig. 2
figure 2

Univariate logistic regression analysis of risk factors for PAD

Full size image

Subgroup analyses of the relationship between S100A12 levels and PAD risk

As shown in Fig. 3, subgroup analyses of patients by HDL-C level, age, HTN and DM showed a significant association between S100A12 and the risk of PAD in all subgroups (all p value < 0.05).

Fig. 3
figure 3

Subgroup analyses of the relationship between S100A12 levels and PAD risk without adjusting for different factors

Full size image

Model of the relationship between S100A12 and PAD after adjusting for different risk predictors

As shown in Table 2, S100A12 levels were positively correlated with the risk of PAD significantly in different models adjusting for different factors. The participants were then grouped by quartiles of S100A12 levels. In the different models, the Q4 group significantly increased the risk of PAD compared to the Q1 group, which indicates that elevated levels of S100A12 increase the risk of PAD.

Table 2 Model of the relationship between S100A12 and PAD after adjusting for different risk predictors
Full size table

Subgroup analysis of each model after adjusting for different factors

As shown in Table 3, S100A12 levels were positively associated with the risk of PAD in all subgroups of the different models. Subgroup analyses also suggested that the correlation between S100A12 and the risk of PAD was stronger in younger (< 60y) or lower HDL-C (< 1.03 mmol/L) patients.

Table 3 Subgroup analysis of each model after adjusting for different factors
Full size table

Analyzing the non-linear relationship between S100A12 and PAD risk using RCS curves

As shown in Fig. 4, a nonlinear relationship between S100A12 level and risk of PAD was suggested by the RCS curves of the three models (all p-non-linear value < 0.05). The RCS curves showed that the positive correlation between S100A12 and the risk of PAD was stronger when the S100A12 level was less than 1.00ng/cL, whereas when S100A12 level was greater than 1.00ng/cL, the correlation between the two was weaker.

Fig. 4
figure 4

RCS plot showing the association between S100A12 levels and risk of PAD

Full size image

Discussion

This study, for the first time, showed a positive correlation between S100A12 and PAD risk among patients with dyslipidemia. Further subgroup analyses showed that the positive association between S100A12 and the risk of PAD was significant in all subgroups, regardless of whether HDL-C levels were less than 1.03 mmol/L, age greater than 60 years, and presence of diabetes or hypertension. In addition, RCS curves suggested a better positive correlation between the two at S100A12 less than 1.00ng/cL and a weaker relationship at greater than 1.00ng/cL.

The relationship between S100A12 and coronary atherosclerosis has been mentioned in previous studies [22,23,24]. The study by Zhang et al. suggests that S100A12 can be elevated within 2 h after the onset of symptoms in ST-segment elevation myocardial infarction (STEMI), and that S100A12 can diagnose STEMI more quickly compared to other biomarkers [22]. Hu et al. found S100A12 to be a reliable predictor of early prognosis in STEMI patients by bioinformatic analysis [23]. It has also been shown that increased levels of S100A12 is an independent predictor of in-stent restenosis in patients who have received coronary drug-eluting stent implantation [24]. However, PAD, as an important risk factor for cardiovascular disease, has not received enough attention in previous studies, so compared with previous studies, the present study focused more on patients with PAD. The correlation between S100A12 and PAD has been previously reported in several diseases [16, 17, 25]. Previous studies have suggested that higher S100A12 levels in patients with diabetes accelerate the development of PAD in patients, ultimately advancing the event of amputation [16]. It has also been shown that in patients with end-stage renal disease, there is a positive correlation between S100A12 levels and the risk of PAD [17]. A biochemical study suggested that S100A12 was one of the important genes in the co-occurrence of Crohn’s disease and PAD, and that neutrophil infiltration-mediated inflammation and immune modulation were important pathological processes involved [25]. The results of the present study suggested a positive correlation between S100A12 and the risk of PAD, which was consistent with previous studies [16, 17]. However, patients with dyslipidemia, as a high-risk group for the PAD, have not been sufficiently emphasized in previous studies. The present study focused on the patients with dyslipidemia, suggesting that S100A12 may play a broader role in PAD. S100A12 is expressed in myeloid cells including neutrophils and monocytes [11]. In the process of atherosclerosis, the accumulation of lipids such as LDL-C has a pro-inflammatory role [10]. During the inflammation, S100A12 acts as a cytokine by binding to cell surface receptors such as RAGE and toll-like receptor-4 (TLR-4). It has been shown that S100A12 binding to RAGE promotes the secretion of inflammatory factors such as IL-6 [26, 27]. This pro-inflammatory process may play an important role in endothelial damage and atherosclerotic plaque progression [28]. Tomohiro Komatsu et al. showed that the use of atorvastatin significantly reduced circulating levels of S100A12 [29], which corroborates the findings of the present study.

Interestingly, the present study found that S100A12 was significantly elevated in dyslipidemia PAD patients, but S100A8/A9, also as calcium-binding proteins which was reported to act an important role in oxidative stress [30], was not significantly elevated in these patients (p value = 0.069). The speculation may be twofold: first, since the p value is close to 0.05, it is reasonable to speculate that there is, in fact, a correlation between S100A8/9 and the risk of PAD. Because the sample size was not large enough, there was no significant correlation between the two in the database of the present study and a significant correlation between the two may be found after further expansion of the sample. Secondly, it has been proposed that compared to LDL-C, S100A8/A9 is more sensitive to oxidation, and therefore S100A8/A9 aggregation in atherosclerotic plaques would contribute to oxidant scavenging, resulting in less oxidative stress damage received by tissue cells during inflammation [31]. In contrast, S100A12 is more resistant to oxidation, which may explain the significantly higher risk of PAD in those with higher levels of S100A12, rather than higher S100A8/A9 [32].

In addition, after univariate logistic regression analyses, it was also found that lower HDL-C levels and DM were independent risk factors for PAD. A number of previous studies have shown similar findings [33, 34]. Increased blood glucose in diabetic patients may contribute to the formation of PAD by advanced glycation end-products (AGE) generation, oxidative stress, and epigenetic changes [35]. HDL-C reverses peripheral vascular atherosclerosis through reverse cholesterol transport, anti-inflammatory, antioxidant, endothelial protection, and antithrombotic effects [36]. Decreasing HDL-C therefore increases the risk of PAD.

It is noteworthy that in the present study HA1c levels were generally high in the PAD and control groups [7.90% (7.20%,9.00%) in the PAD group and 7.50 (6.20,8.70) in the control group]. Additionally, although the level of HA1c was significantly higher in the PAD group compared to the control group, HA1c was found to have a non-significant effect on the risk of PAD by univariate logistic regression analysis (p value = 0.088), which was inconsistent with previous finding [6]. We analyzed that this was due to the fact that in this retrospective study, clinicians often tend to check HA1c in patients with DM or risk factors for DM, whereas other patients do not routinely have their HA1c checked. And for these missing data, available case analysis was taken in the present study. Since the missing data were not randomly distributed, so the missing not at random (MNAR) leads to increased bias. Despite the large MNAR-induced bias in HA1c, we thought that HA1c could still reflect the effect of glycemic control on the risk of developing PAD to some extent, and therefore we still performed a univariate regression analysis of HA1c in the study. However, since HA1c was not measured in patients with relatively normal blood glucose, the statistical validity of the effect of HA1c on the risk of PAD was underestimated, leading to negative results suggested by univariate logistic regression analysis and we did not include HbA1c as a predictor in the subsequent prediction model.

The results of the subgroup analyses suggested that S100A12 was positively associated with the risk of PAD in all subgroups grouped by HDL-C, hypertension, diabetes, and age. Notably, the correlation between S100A12 and the risk of PAD may be stronger in subgroups with lower HDL-C levels (p value = 0.094). It has been suggested that HDL-C not only acts as a cholesterol reverse transporter during atherosclerosis but also inhibits inflammatory pathways in several ways [37]. Firstly, HDL-C activates endothelial nitric oxide synthase (eNOS) to maintain endothelial cell integrity and reduce the inflammatory response triggered by endothelial cell dysfunction [38]. Secondly, HDL-C reduces inflammation by preventing ox-LDL-mediated expansion of granulocyte monocyte progenitors (GMPs) through multiple pathways [39, 40]. In addition, macrophages exhibit pro- and anti-inflammatory properties depending on the environment, while HDL-C alleviates the pro-inflammatory characteristics of macrophages through multiple pathways [41,42,43]. Since S100A12 is a pro-inflammatory factor secreted by granulocytes, it is reasonable to speculate that the inflammatory response during plaque progression is not affected by the various anti-inflammatory mechanisms mediated by HDL-C in populations with lower levels of HDL-C, thus leading to a stronger correlation between S100A12 and PAD risk.

Meanwhile, the RCS curves showed that the correlation between S100A12 and PAD risk was non-linear. Such a nonlinear relationship between the two was not found in previous studies. This suggested that changes in circulating levels of S100A12 did not have a constant but limited effect on the risk of PAD. When S100A12 is elevated above 1.00ng/cL, its effect on the risk of PAD becomes weak. Previous studies have shown a nonlinear correlation between the systemic inflammation response index (SIRI) and the occurrence of revascularization [44], suggesting that the inflammatory response plays a nonlinear role in the process of vascular endothelial injury, which is similar to the results of the present study. It is reasonable to speculate that as S100A12 levels rise during the development of PAD, its binding receptors such as TLR-4 and RAGE may become saturated, which leads to saturation of the inflammatory response it mediates. Thus the continued rise in S100A12 levels no longer has a significantly impact on the risk of PAD.

The present study has several strengths. Firstly, this study for the first time found a significant correlation between S100A12 and the risk of PAD among patients with dyslipidemia, confirming that S100A12 plays an important role in the atherosclerotic process of peripheral arteries induced by LDL-C. Secondly the results were more reliable by logistic regression after adjusting other covariates and by subgroup analysis. Finally this study used RCS to explore the non-linear relationship between S100A12 and PAD risk and find the cutoff of the change of correlation between the two, which made the results of this study more accurate and can better guide the assessment of PAD risk and subsequent studies.

Limitations in this study also need to be noted. Firstly, this study is retrospective and there will be incomplete data collection and recall bias, leading to compromised results. In addition when dealing with missing data this study used available case analysis, which further exacerbated the bias. Secondly, this study only collected single-center samples, thus leading to selection bias. Finally, many factors affect the risk of PAD, and although as many factors as possible were collected for inclusion in this study, there is still a range of factors that were not included in the study, which could also affect the final results.

Based on the results of this study, there are several areas for further research. Firstly, multicenter prospective studies are needed to further validate the results of this study. In addition further follow-up is necessary for exploring the impact of S100A12 levels on the prognosis of PAD patients. Finally, the present study revealed a nonlinear relationship between S100A12 and PAD risk, and further studies are needed to investigate the molecular biological mechanisms underlying this nonlinear relationship.

Conclusion

In conclusion, elevated S100A12 level is an independent risk factor for PAD in patients with dyslipidemia. In different subgroups, S100A12 was significantly and positively associated with the risk of PAD after adjusting for different factors. There is a non-linear relationship between S100A12 and the risk of PAD, with a stronger positive correlation at S100A12 levels below 1.00ng/cL. These findings implied that S100A12 is a potential biomarker for identifying patients with dyslipidemia who are at high risk of developing PAD. They also implied that S100A12 levels can be routinely monitored in dyslipidemic populations for the early detection of PAD and to guide the management of PAD. Finally, the results of this study emphasize that inflammation in dyslipidemia patients plays an important role in the development of PAD, suggesting that lipid control and immunomodulation may be effective in the prevention of PAD.

Data availability

Data is provided within the manuscript.

Abbreviations

ABI:

Ankle-brachial blood pressure index

AGE:

Advanced glycation end-products

AIS:

Abbreviated injury scale

BMI:

Body mass index

BUN:

Blood urea nitrogen

DM:

Diabetes mellitus

EDTA:

Ethylenediaminetetraacetic acid

eNOS:

Endothelial nitric oxide synthase

GMPs:

Granulocyte monocyte progenitors

HA1c:

Glycosylated hemoglobin

HDL-C:

High-density lipoprotein cholesterol

HTN:

Hypertension

ICAM-1:

Intercellular adhesion molecule-1

IL-1β:

Interleukin-1β

LDL-C:

Low-density lipoprotein cholesterol

MAPK:

Mitogen-activated protein kinase

MNAR:

Missing not at random

NF-κB:

Nuclear factor-κB

Nox1:

NADPH oxidase 1

PAD:

Peripheral arterial disease

RAGE:

Receptor for advanced glycation end products

RCS:

Restricted cubic spline

ROS:

Reactive oxygen species

SIRI:

Systemic inflammation response index

SOFA:

Sequential organ failure assessment

STEMI:

ST-segment elevation myocardial infarction

TC:

Total cholesterol

TG:

Triglyceride

TLR-4:

Toll-like receptor-4

TNF-α:

Tumor necrosis factor-α

VCAM-1:

Vascular cell adhesion molecule-1

References

  1. You Y, Zeng N, Wu W, et al. Association of serum homocysteine with peripheral arterial disease in patients without diabetes: A study based on National health and nutrition examination survey database. Am J Cardiol. 2024;218:16–23.

    Article  CAS  PubMed  Google Scholar 

  2. Mazzolai L, Teixido-Tura G, Lanzi S, et al. 2024 ESC guidelines for the management of peripheral arterial and aortic diseases. Eur Heart J. 2024;45(36):3538–700.

    Article  PubMed  Google Scholar 

  3. Gornik HL, Aronow HD, Goodney PP, et al. ACC/AHA/AACVPR/APMA/ABC/SCAI/SVM/SVN/SVS/SIR/VESS Guideline for the Management of Lower Extremity Peripheral Artery Disease. A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2024;149(24):e1313–410.

  4. Mandaglio-Collados D, Marín F, Rivera-Caravaca JM. Peripheral artery disease: update on etiology, pathophysiology, diagnosis and treatment. Med Clin (Barc). 2023;161(8):344–50.

    Article  PubMed  Google Scholar 

  5. GBD 2019 Peripheral Artery Disease Collaborators. Global burden of peripheral artery disease and its risk factors, 1990–2019: a systematic analysis for the global burden of disease study 2019. Lancet Glob Health. 2023;11(10):e1553–65.

    Article  Google Scholar 

  6. Aursulesei Onofrei V, Ceasovschih A, Marcu DTM, et al. Mortality risk assessment in peripheral arterial Disease-The burden of cardiovascular risk factors over the years: A single center’s experience. Diagnostics (Basel). 2022;12(10):2499.

    Article  PubMed  Google Scholar 

  7. Yadav A, Sawant V, Singh Bedi V, et al. Dyslipidemia and peripheral arterial disease. Indian Heart J. 2024;76(Suppl 1):S86–9. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.ihj.2024.01.010. Epub 2024 Jan 13.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Tao M, Zhang Y, Li Q et al. Association of lipids and lipid-lowering drugs with peripheral arterial disease: A Mendelian randomization study. J Clin Lipidol. 2024,18(6):e968-e976.

  9. Pollak AW, Kramer CM. LDL Lowering in peripheral arterial disease: are there benefits beyond reducing cardiovascular morbidity and mortality? Clin Lipidol. 2012;7(2):141–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Gallo A, Le Goff W, Santos RD et al. Hypercholesterolemia and inflammation-Cooperative cardiovascular risk factors. Eur J Clin Invest. 2024 Oct 6:e14326.

  11. Oesterle A, Bowman MA. S100A12 and the S100/Calgranulins: emerging biomarkers for atherosclerosis and possibly therapeutic targets. Arterioscler Thromb Vasc Biol. 2015;35(12):2496–507.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Basta G, Sironi AM, Lazzerini G, et al. Circulating soluble receptor for advanced glycation end products is inversely associated with glycemic control and S100A12 protein. J Clin Endocrinol Metab. 2006;91(11):4628–34.

    Article  CAS  PubMed  Google Scholar 

  13. Wang X, Wang Q, Wei D, et al. Association between soluble receptor for advanced glycation end product and endogenous secretory soluble receptor for advanced glycation end product levels and carotid atherosclerosis in diabetes: A systematic review and Meta-Analysis. Can J Diabetes. 2021;45(7):634–40.

    Article  PubMed  Google Scholar 

  14. Meijer B, Gearry RB, Day AS. The role of S100A12 as a systemic marker of inflammation. Int J Inflam. 2012;2012:907078.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Xiao X, Yang C, Qu SL, et al. S100 proteins in atherosclerosis. Clin Chim Acta. 2020;502:293–304.

    Article  CAS  PubMed  Google Scholar 

  16. Malmstedt J, Kärvestedt L, Swedenborg J, et al. The receptor for advanced glycation end products and risk of peripheral arterial disease, amputation or death in type 2 diabetes: a population-based cohort study. Cardiovasc Diabetol. 2015;14:93.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Shiotsu Y, Mori Y, Hatta T, et al. Plasma S100A12 levels and peripheral arterial disease in end-stage renal disease. Nephron Extra. 2011;1(1):242–50.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Buyukterzi Z, Can U, Alpaydin S, et al. Enhanced S100A9 and S100A12 expression in acute coronary syndrome. Biomark Med. 2017;11(3):229–37.

    Article  CAS  PubMed  Google Scholar 

  19. Tsirebolos G, Tsoporis JN, Drosatos IA, et al. Emerging markers of inflammation and oxidative stress as potential predictors of coronary artery disease. Int J Cardiol. 2023;376:127–33.

    Article  PubMed  Google Scholar 

  20. Joint Committee on the Chinese Guidelines for Lipid Management. [Chinese guidelines for lipid management (2023)]. Zhonghua Xin Xue Guan Bing Za Zhi. 2023;51(3):221–55.

    Google Scholar 

  21. Bonaca MP, Bauersachs RM, Anand SS, et al. Rivaroxaban in peripheral artery disease after revascularization. N Engl J Med. 2020;382(21):1994–2004.

    Article  CAS  PubMed  Google Scholar 

  22. Zhang X, Cheng M, Gao N, et al. Utility of S100A12 as an early biomarker in patients with ST-Segment elevation myocardial infarction. Front Cardiovasc Med. 2021;8:747511.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Zhai H, Huang L, Gong Y, et al. Human plasma transcriptome implicates dysregulated S100A12 expression: A strong, Early-Stage prognostic factor in ST-Segment elevated myocardial infarction: bioinformatics analysis and experimental verification. Front Cardiovasc Med. 2022;9:874436.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Liang H, Cui Y, Bu H, et al. Value of S100A12 in predicting in-stent restenosis in patients with coronary drug-eluting stent implantation. Exp Ther Med. 2020;20(1):211–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Yao Z, Zhang B, Niu G, et al. Neutrophil infiltration characterized by upregulation of S100A8, S100A9, S100A12 and CXCR2 is associated with the Co-Occurrence of Crohn’s disease and peripheral artery disease. Front Immunol. 2022;13:896645.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Hofmann MA, Drury S, Fu C, et al. RAGE mediates a novel Proinflammatory axis: a central cell surface receptor for S100/calgranulin polypeptides. Cell. 1999;97:889–901.

    Article  CAS  PubMed  Google Scholar 

  27. Srikrishna G, Nayak J, Weigle B, et al. Carboxylated N-glycans on RAGE promote S100A12 binding and signaling. J Cell Biochem. 2010;110(3):645–59.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Tae HJ, Kim JM, Park S, et al. The N-glycoform of sRAGE is the key determinant for its therapeutic efficacy to attenuate injury-elicited arterial inflammation and neointimal growth. J Mol Med. 2013;91(12):1369–81.

    Article  CAS  PubMed  Google Scholar 

  29. Komatsu T, Ayaori M, Uto-Kondo H, et al. Atorvastatin reduces Circulating S100A12 levels in patients with carotid atherosclerotic plaques - A link with plaque inflammation. J Atheroscler Thromb. 2022;29(5):775–84.

    Article  CAS  PubMed  Google Scholar 

  30. Li Y, Chen B, Yang X, et al. S100a8/a9 signaling causes mitochondrial dysfunction and cardiomyocyte death in response to ischemic/reperfusion injury. Circulation. 2019;140(9):751–64.

    Article  CAS  PubMed  Google Scholar 

  31. Sroussi HY, Berline J, Palefsky JM. Oxidation of methionine 63 and 83 regulates the effect of S100A9 on the migration of neutrophils in vitro. J Leukoc Biol. 2007;81(3):818–24.

    Article  CAS  PubMed  Google Scholar 

  32. Lim SY, Raftery MJ, Goyette J, et al. Oxidative modifications of S100 proteins: functional regulation by redox. J Leukoc Biol. 2009;86:577–87.

    Article  CAS  PubMed  Google Scholar 

  33. Reiner Ž, De Sutter J, Ryden L, et al. Peripheral arterial disease and intermittent claudication in coronary heart disease patients. Int J Cardiol. 2021;322:227–32.

    Article  PubMed  Google Scholar 

  34. Gallagher KA, Mills JL, Armstrong DG, et al. Current status and principles for the treatment and prevention of diabetic foot ulcers in the cardiovascular patient population: A scientific statement from the American heart association. Circulation. 2024;149(4):e232–53.

    Article  PubMed  Google Scholar 

  35. Poznyak A, Grechko AV, Poggio P, et al. The diabetes Mellitus-Atherosclerosis connection: the role of lipid and glucose metabolism and chronic inflammation. Int J Mol Sci. 2020;21(5):1835.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Madaudo C, Bono G, Ortello A, et al. Dysfunctional High-Density lipoprotein cholesterol and coronary artery disease: A narrative review. J Pers Med. 2024;14(9):996.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Linton MF, Yancey PG, Tao H, et al. HDL function and atherosclerosis: reactive dicarbonyls as promising targets of therapy. Circ Res. 2023;132(11):1521–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Kratzer A, Giral H, Landmesser U. High-density lipoproteins as modulators of endothelial cell functions: alterations in patients with coronary artery disease. Cardiovasc Res. 2014;103:350–61.

    Article  CAS  PubMed  Google Scholar 

  39. Feng Y, Schouteden S, Geenens R, et al. Hematopoietic stem/progenitor cell proliferation and differentiation is differentially regulated by high-density and low-density lipoproteins in mice. PLoS ONE. 2012;7:e47286.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Gao M, Zhao D, Schouteden S, et al. Regulation of high-density lipoprotein on hematopoietic stem/progenitor cells in atherosclerosis requires scavenger receptor type BI expression. Arterioscler Thromb Vasc Biol. 2014;34:1900–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Yvan-Charvet L, Welch C, Pagler TA, et al. Increased inflammatory gene expression in ABC transporter-deficient macrophages: free cholesterol accumulation, increased signaling via toll-like receptors, and neutrophil infiltration of atherosclerotic lesions. Circulation. 2008;118:1837–47.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. De Nardo D, Labzin LI, Kono H, et al. High-density lipoprotein mediates anti-inflammatory reprogramming of macrophages via the transcriptional regulator ATF3. Nat Immunol. 2014;15:152–60.

    Article  PubMed  Google Scholar 

  43. Darabi M, Lhomme M, Dahik VD, et al. Phosphatidylserine enhances anti-inflammatory effects of reconstituted HDL in macrophages via distinct intracellular pathways. FASEB J. 2022;36:e22274.

    Article  CAS  PubMed  Google Scholar 

  44. Ma M, Wu K, Sun T, et al. Impacts of systemic inflammation response index on the prognosis of patients with ischemic heart failure after percutaneous coronary intervention. Front Immunol. 2024;15:1324890.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank Mr. Jianxing Zeng and Kaizhen Wen for assistance in laboratory test.

Funding

This study was supported by the fund of Medical Innovation Project of Fujian Provincial Health Commission (2022CXB015) by Zhong Chen.

Author information

Author notes

  1. Wenyu Cai and Yilin He contributed equally to this work.

Authors and Affiliations

  1. Department of Cardiology, Shanghai Sixth People’s Hospital Fujian, Jinjiang, Fujian, 362200, P.R. China

    Wenyu Cai, Dengqing Zhang, Zimin Chen & Zhong Chen

  2. Department of Cardiology, Shanghai Sixth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, P.R. China

    Yilin He, Shijia Jin & Zhong Chen

  3. Department of Clinical Laboratory, Shanghai Sixth People’s Hospital Fujian, Jinjiang, Fujian, 362200, P.R. China

    Guohua Li

  4. Department of Critical Care Unit, Shanghai Sixth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, P.R. China

    Yifan Zhang

Authors
  1. Wenyu Cai
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Yilin He
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Guohua Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Dengqing Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Zimin Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Shijia Jin
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Yifan Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Zhong Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Z.C. designed study, W.C. and Y.Z. performed data management, data analysis. W.C. and Y.Z. wrote the manuscript. D.Z., Z.C. also performed data management. Y.H. and Y.Z. edited the figures and article format. Z.C. made key revisions to the article. All authors contributed to the article and approved the submitted version.

Corresponding authors

Correspondence to Yifan Zhang or Zhong Chen.

Ethics declarations

Ethics approval and consent to participate

All the authors are accountable for all aspects of the work and ensure that the accuracy- or integrity-related issues are properly researched and addressed. This study is in accordance with the 2013 revision of the Declaration of Helsinki. The Ethics Committee of Shanghai Sixth People’s Hospital Fujian approved this study (jjsyyyxll-2022030). Written informed consent was obtained from all participants, and the consent form template was approved by the Ethics Committee.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cai, W., He, Y., Li, G. et al. Association between S100A12 and risk of peripheral arterial disease in patients with dyslipidemia: a cross-sectional study. BMC Cardiovasc Disord 25, 313 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12872-025-04752-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12872-025-04752-2

Keywords

BMC Cardiovascular Disorders

ISSN: 1471-2261

Contact us