Mediation effect of antithrombin III between chronic renal insufficiency and coronary artery disease in T2DM patients

doi:10.21203/rs.3.rs-3305085/v1

Download PDF

Research Article

Mediation effect of antithrombin III between chronic renal insufficiency and coronary artery disease in T2DM patients

https://doi.org/10.21203/rs.3.rs-3305085/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 08 Jan, 2024

Read the published version in Endocrine →

You are reading this latest preprint version

Purpose:The study aimed to investigate the potential effect of Antithrombin Ⅲ between chronic renal insufficiency and coronary artery disease in T2DM patients.

Methods:We collected data of T2DM patients hospitalized in ZhongDa Hospital affiliated with Southeast University. Relationships between renal function, Antithrombin Ⅲ, and coronary artery disease were explored using multivariate regression models. Multiplicative and additive interaction analysis was investigated. Bootstrap mediation analysis was utilized to investigate the role of Antithrombin Ⅲ.

Results:A total of 4,238 patients were included, among which the coronary artery disease prevalence was 23.8%. Lower levels of renal function and Antithrombin Ⅲ were observed in patients with coronary artery disease (P<0.05). In adjusted models, each 10-unit increase in Antithrombin Ⅲ was associated with an 8.3% (95%CI: 3.2%~13.1%; P<0.05) decrease in coronary artery disease risk. Patients with low levels of both eGFR and Antithrombin Ⅲ were in the highest risk (OR=1.45; 95%CI: 1.10-1.90, P<0.05) of coronary artery disease, while no multiplicative and additive interaction effects were significant. Bootstrap mediation analysis estimated that Antithrombin Ⅲ mediated approximately 4.40% of the effect of chronic renal insufficiency on coronary artery disease risk.

Conclusion:Antithrombin Ⅲ may serve as a mediator between chronic renal insufficiency and coronary artery disease, providing mechanistic insights into a new clinical observation.

Antithrombin Ⅲ

Coronary artery disease

Chronic renal insufficiency

Mediation analysis

Type 2 Diabetes Mellitus

The prevalence of type 2 diabetes mellitus (T2DM) has increased dramatically in recent decades, resulting in a significant increase in cardiovascular complications [1]. Coronary artery disease (CAD) has been an important cause of morbidity and mortality in T2DM patients, leading to a substantial social medical burden. Chronic renal insufficiency may be one of the most critical factors for CAD in T2DM patients[2–5], and the CAD risk even increases to 3.4 times [6]. However, the mechanisms underlying the relation between chronic renal insufficiency and CAD in T2DM patients are not fully revealed.

Hypercoagulability is reported to be deeply involved in the development of chronic kidney disease and CAD, characterized by increased thrombin generation and decreased anticoagulants [7]. Antithrombin Ⅲ (ATⅢ), the most important coagulation factor inhibitor, plays a significant role in this progression. ATⅢ is a non-vitamin K-dependent protease that inhibits coagulation by lysing thrombin and factor Xa, and possesses anti-inflammatory and cytoprotective effects [8, 9]. Previous studies have indicated that ATⅢ insufficiency is associated with renal dysfunction, [10] exacerbating renal injury in animal models and possibly in humans [11, 12]. Additionally, decreased ATⅢ was related to the increased risk of CAD [13, 14], potentially resulting from its role in a hypercoagulation state, as well as its anti-inflammation [15] and cardioprotective functions [16]. However, most studies have focused on taking renal dysfunction and ATⅢ insufficiency as independent risk factors for CAD, with limited research to identify the potential connection between these two tissues, which ignores their actual effects on CAD progression.

Therefore, we conducted the study to explore the actual role of ATⅢ in the association between renal function and CAD in 4,238 Chinese T2DM patients, to provide mechanic clues for further study in the pathogenesis of CAD.

2.1. Study Population

Our study included consecutive T2DM patients who were hospitalized in the ZhongDa Hospital affiliated with Southeast University between July 2013 and December 2018. Supplemental Fig. 1 provides a comprehensive flowchart of the exclusion process for the study. In summary, the study excluded patients with severe liver diseases, acute kidney injuries, acute thromboembolic events, severe infection, and patients undergoing maintenance hemodialysis. Because of the acute increase in ATⅢ in patients with acute coronary syndrome (ACS), we especially excluded ACS individuals to ensure the reliability of results. Ultimately, the study enrolled 4,238 T2DM patients with complete records. Approval for the study was obtained from the Research Ethics Committee of the Affiliated ZhongDa Hospital of Southeast University (Approved No. of the ethic committee: 2020ZDSYLL028-P01).

2.2. Data Collection

The data collected for each patient included information on demographics (gender, age, and ethnicity), medical history (diabetes mellitus, hypertension, and coronary heart disease), medication use history (metformin, insulin, anti-platelet aggregation, and statins), physical measurements (weight, height, and body mass index (BMI)), and living habits (smoking and drinking). After an overnight fast of 8 hours, venous blood samples were collected and sent to the laboratory for analysis. The following parameters were measured: total cholesterol (T-CHOL), triglyceride, apolipoprotein-A1, apolipoprotein-B, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and lipoprotein-a (Lpa) (lipid indicators); fasting blood glucose (FBG), HbA1c (glycemic targets); serum creatinine (Scr), urinary albumin/creatinine ratio (ACR), blood urea nitrogen (BUN), and serum uric acid (SUA) (renal measures); antithrombin-Ⅲ (ATⅢ), fibrinogen, prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), fibrinogen degradation product (FDP), and D-dimer (coagulation measures). The analysis of all blood samples was conducted by the professional clinical laboratory medical staff of Zhongda Hospital. The Laboratory Center of Zhongda Hospital Affiliated to Southeast University implements internal and external quality management procedures directed by the Chinese Laboratory Quality Control.

2.3. Definition of Variables

For each case, we recorded the primary diagnosis and any additional diagnoses at the time of discharge using the International Classification of Diseases (ICD)-10 coding system. The corresponding codes were used to identify various conditions, including diabetes (ICD-10 codes E10-14), hypertension (ICD-10 codes I10), and coronary heart disease (ICD-10 codes I20-I25). The diagnosis of CAD relied on the confirmation through coronary angiography. The eGFR was determined using the CKD-EPI equation [17, 18]. On account of data amount among groups, renal function was classified into three categories based on eGFR values: G1 for normal function (≥ 90 ml/min/1.73 m²), G2 for mild to moderate impairment (90–45[including 45]ml/min/1.73 m²), and G3 for moderate to severe impairment (< 45 ml/min/1.73 m²).

2.4. Statistical Analysis

We summarized the general demographic characteristics of all participants and compared their distributions based on CAD using statistical tests such as the Chi-square test, Student’s t-test, or Mann-Whitney U test. Logistic regression models were conducted to explore associations between eGFR and CAD, with adjustment for gender, diabetes duration, BMI, smoking, drinking, hypertension, CHOL, HbA1c, antiplatelet, and ACR. There, we only included diabetes duration as the independent variable in the multivariate regression analysis and did not include age due to the strong interference. The associations were also depicted with restricted cubic splines. To investigate the potential role of ATⅢ in the association between eGFR and CAD, we employed linear mixed regression models to estimate the regression coefficients (β) of ATⅢ with eGFR, and logistic regression models to estimate the odds ratio (OR) of CAD with ATⅢ. The study using the cross-product interaction term of ATⅢ and eGFR and the relative excess risk due to interaction (RERI) to investigate the multiplicative and additive interaction of ATⅢ and eGFR on CAD risk. Then the mediation analysis was performed in patients with chronic renal insufficiency to determine the proportion of the relationship between eGFR and CAD risk that is influenced by changes in ATⅢ levels, using Bootstrap mediation analysis with 5000 iterations [19, 20]. Supplemental Fig. 2 presents the mediation model, in which the "total effect" was divided into a "direct effect" (the effect of eGFR on CAD risk after controlling for ATⅢ) and an "indirect effect" (the effect of eGFR on CAD risk through changes in ATⅢ levels). The proportion of mediation by ATⅢ levels was calculated as the ratio of the indirect effect to the total effect. Data analysis was performed using SPSS version 26.0 and R version 4.2.1. P ≤ 0.05 was considered to be significant.

3.1. Basic characteristics

Table 1 displays the baseline characteristics of the 4,238 participants enrolled in this study. The average age was 64.3 years, with a male-to-female ratio of 1.42:1. Of all the participants, 1007 (23.8%) suffered CAD. Older age, longer diabetes duration, hypertension, greater use of antiplatelet and statin medications were more commonly observed in CAD group. In comparison to non-CAD subjects, those with CAD had significantly lower eGFR (72.70 ml/min/1.73m² vs 87.99ml/min/1.73m², P < 0.001) and ATⅢ (98.0% vs 104.0%, P < 0.001), while no statistically differences were observed in gender, BMI, and smoking percentage.

Table 1

Baseline clinical characteristics of all participants stratified by CAD phenotypes.
Variable	All participants	Non-CAD	CAD	P value
n (%)	4238	3231 (76.24%)	1007 (23.76%)
Male ,n(%)	2491 /4238 (58.78%)	1919 (59.40%)	572 (56.80%)	0.155
Age (years)	64.25 ± 13.81	61.56 ± 13.38	72.89 ± 11.42	< 0.001
BMI (kg/m²)	25.32 ± 3.79	25.34 ± 3.73	25.28 ± 3.98	0.716
Smoking,n/N(%)	1178/4218 (27.93%)	911 (28.30%)	267 (26.60%)	0.322
Drinking,n/N(%)	706/4218 (16.74%)	599 (18.60%)	107 (10.70%)	< 0.001
Hypertension,n/N(%)	2656/4238 (62.67%)	1799 (55.70%)	857 (85.10%)	< 0.001
Diabetes duration (years)	7.00 (6.00–9.00)	7.00 (5.00–8.00)	10.00 (8.00–11.00)	< 0.001
Antiplatelet user,n/N(%)	1841/3770 (48.83%)	1074 (37.50%)	767 (84.40%)	< 0.001
Statin user,n/N(%)	1595/3769 (42.32%)	1026 (35.90%)	569 (62.70%)	< 0.001
Insulin user,n/N(%)	2412/3780 (63.81%)	2099 (73.20%)	313 (34.40%)	< 0.001
Metformin user,n/N(%)	1061/3779 (28.01%)	925 (32.30%)	136 (14.90%)	< 0.001
FBG (mmol/L)	9.92 (7.06–14.65)	10.25 (7.16–15.09)	9.10 (6.86–12.86)	< 0.001
HbA1c (%)	8.30 (6.90–10.10)	8.50 (7.00-10.30)	7.70 (6.70–9.10)	< 0.001
T-CHO (mmol/l)	4.65 (3.94–5.44)	4.78 (4.07–5.55)	4.23 (3.52–5.07)	0.026
HDL-C (mmol/L)	1.15 (1.00-1.32)	1.17 (1.01–1.34)	1.10 (0.95–1.27)	< 0.001
LDL-C (mmol/L)	2.79 (2.25–3.39)	2.88 (2.35–3.47)	2.50 (1.95–3.11)	< 0.001
Apolipoprotein-A1 (g/L)	1.08 (0.94–1.24)	1.09 (0.96–1.25)	1.05 (0.90–1.19)	< 0.001
Apolipoprotein-B (g/L)	0.82 (0.69–0.96)	0.85 (0.72–0.98)	0.75 (0.62–0.90)	< 0.001
Triglyceride (mmol/L)	1.57 (1.08–2.53)	1.59 (1.09–2.40)	1.51 (1.07–2.19)	< 0.001
Lipoprotein-a (mg/L)	197.00 (99.00-340.00)	191.00 (96.00-335.00)	213.00 (108.00-358.50)	< 0.001
APTT (s)	30.60 (28.60–32.60)	30.60 (28.60–32.60)	30.60 (28.60-32.65)	0.826
ATⅢ (%)	103.00 (91.00-114.00)	104.00 (93.00-115.50)	98.00 (87.00-108.50)	< 0.001
D dimer (ug/L)	134.50 (74.00-254.00)	128.00 (70.00-244.00)	155.00 (91.00-281.00)	< 0.001
FDP (mg/L)	1.37 (0.82–2.15)	1.28 (0.78–2.05)	1.67 (1.00-2.51)	< 0.001
Fibrinogen (g/L)	3.82 (3.32–4.34)	3.80 (3.31–4.32)	3.86 (3.35–4.38)	0.089
INR	1.00 (0.95–1.06)	1.00 (0.95–1.06)	1.01 (0.95–1.07)	0.031
PT (s)	10.70 (10.10-11.37)	10.70 (10.10–11.30)	10.80 (10.10-11.47)	0.017
TT (s)	14.14 (13.40–14.90)	14.10 (13.40–14.90)	14.30 (13.40–15.10)	0.001
BUN (mmol/l)	5.60 (4.70-7.00)	5.50 (4.60–6.70)	6.10 (5.00-7.75)	< 0.001
SCr (umol/L)	82.43 ± 49.57	79.26 ± 48.18	92.62 ± 52.55	< 0.001
SUA(umol/l)	294.00 (233.00-365.00)	295.00 (236.00-362.50)	290.00 (210.00-371.50)	0.006
eGFR (ml/min/1.73m²)	84.36 ± 24.92	87.99 ± 23.89	72.70 ± 24.57	< 0.001
ACR(mg/g.Cr)	15.03 (8.00-52.95)	14.16 (7.77–45.58)	19.40 (9.10-90.24)	< 0.001
Abbreviations: CAD, coronary artery disease; BMI, body mass index; FBG, fasting blood-glucose; HbA1c, glycosylated hemoglobin; T-CHO, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; BUN, blood urea nitrogen; Scr, serum creatinine; SUA, serum uric acid; ATⅢ, antithrombin-Ⅲ, eGFR, estimated glomerular filtration rate; ACR, urinary albumin/creatinine ratio.
Data was presented as n/N (%) for categorical data, mean ± SD for parametrically distributed data, or median (interquartile range) for non-parametrically distributed data.
*P values were estimated by Student’s t-test or Mann-Whitney U test for continuous variables according to the data distribution and the Chi-square test for categorical variables.

3.2. Associations between renal function and the risk of coronary artery disease.

With the deterioration of renal function, the CAD morbidity rate increased significantly (G1: 14.4%; G2: 29.8%; G3: 46.2%; P < 0.001) (Supplemental Table 1). In the multivariate logistic regression models, for per 1 SD increase in value of eGFR, the risk of CAD declined 45.6% and 16.3% in crude and adjusted models respectively (Supplemental Table 2). Referred to renal function groups, the crude regression odds ratios (ORs) for CAD from Group 2 to Group 3 were 2.523 (2.152–2.957), and 5.116 (3.986–6.565) respectively (referring to Group 1, OR = 1). In adjusted models, the association between eGFR groups and CAD was slightly attenuated, but it still remained statistically significant (P trend = 0.012) (Fig. 1).

3.3. Associations between renal function and ATⅢ.

A positive association between renal function and ATⅢ was observed. Individuals with chronic renal insufficiency had lower ATⅢ levels than those with normal renal function (Supplemental Table 1). The association between renal function and ATⅢ remained statistically significant even after adjusting for confounding factors (Table 2). In adjusted models, each 1 SD increase in eGFR, ATⅢ increased by 0.946% and regression coefficients for ATⅢ from G1 to G3 were 1(referent), -3.582 (-4.863 ~ -2.303), -1.397 (-3.759 ~ 0.965) respectively (P trend < 0.001).

Table 2

Association between renal function and ATⅢ (N = 4238).
eGFR (ml/min/1.73m²)	ATⅢ, P50 (P25-P75)	Estimated changes (95%CI) in plasma ATⅢ (%)
		Crude β (95%CI)^a	Adjusted β (95%CI)^b
eGFR (per 1 SD)	103.00 (91.00-114.00)	2.933 (2.396–3.470)	0.946 (0.271–1.621)
P value		< 0.001	< 0.001
eGFR groups (as categorical variable)
G1 (≥ 90)	107.00 (95.00-117.00)	1 (referent)	1 (referent)
G2 (45 ~ 90)	100.00 (89.00-111.00)	-6.722 (-7.838 ~ -5.607)	-3.582 (-4.863 ~ -2.303)
G3 (< 45)	100.00 (88.50–112.00)	-5.948 (-8.015 ~ -3.882)	-1.397 (-3.759 ~ 0.965)
P for trend		< 0.001	< 0.001
Abbreviations: eGFR, estimated glomerular filtration rate; ATⅢ, antithrombin-Ⅲ.
*P value for trend was conducted by assigning the mean value to each group of renal function.
^a Models were single-factor logistic regressions without adjusting confounders.
^b Models were adjusted for gender, diabetes duration, BMI, smoking, drinking, hypertension, CHOL, HbA1c, use of anti-platelet, and ACR.

3.4. Associations between ATⅢ and the risk of coronary artery disease.

Figure 2 illustrated an upward trend in CAD risk as the ATⅢ level gradually decreased. In the linear mixed regression analysis, per 10 unit increase in ATⅢ was significantly associated with a 8.3% (3.2%~13.1%) decrease in the risk of CAD after adjusting for covariates (P < 0.001) (Supplemental Table 3).

3.5. Joint effects of ATⅢ and renal function on coronary artery disease risk

Low levels of ATⅢ and eGFR were statiscally associated with CAD individuals. According to the median of eGFR [88.5 (69.8-101.5) ml/min/1.73m²] and ATⅢ [103.0% (91.0%-114.0%)], we divided 4,238 patients into high or low levels of ATⅢ and eGFR groups. Supplemental Table 4 shows the joint effects of ATⅢ and eGFR on CAD risk. Individuals with low eGFR or low ATⅢ levels had a significantly higher risk of CAD. The adjusted OR (95%CI) of CAD was highest [1.45 (1.10–1.90), P < 0.05] in those with low levels of both eGFR and ATⅢ, compared to those with low levels of either alone. As the cubic spline showed, low eGFR was significantly associated with an increased risk of CAD among participants with low ATⅢ levels (Supplemental Fig. 3). However, there was no evidence of multiplicative or addictive interaction between eGFR and ATⅢ on the risk of CAD, as the multiplicative interaction term of eGFR and ATⅢ was 1.00 (0.99-1.00), and the estimate of RERI was − 0.027 (-0.184 ~ 0.129).

3.6. Mediating role of ATⅢ on the association between renal function and coronary artery disease.

ATⅢ had a significant statistically indirect effect of renal function on CAD risk in the patients with chronic renal insufficiency (Table 3). The total, direct, and indirect effect coefficients (*10^− 5) for eGFR on CAD were − 102.4 (-179, 0), -97.9 (-173, 0), -4.5 (-10.4, 0) (the interval does not include 0) respectively (P < 0.001). By 5000 iterations Bootstrap mediation analysis, the indirect effect pathway by ATⅢ contributed to 4.40% of the total effect of eGFR on CAD risk among 2,379 chronic renal insufficiency participants.

Table 3

Mediation effect of ATⅢ on the association between chronic renal insufficiency and CAD risk (N = 2,379).
Effects	β (*10^− 5)	95%CI (*10^− 5)	p value	Proportion mediated (%)
Total effects	-102.4	(-179, 0)^a	0.003	100%
Direct effects	-97.9	(-173, 0)^a	0.004	95.60%
Mediating effects	-4.5	(-10.4, 0)^a	0.014	4.40%
The mediation analysis adopted Bootstrap mediation analysis with 5000 iterations and examined in the patients with chronic renal insufficiency.
^a meant the interval does not include 0.

This study revealed a novel mediation role of ATIII in CAD risk and provided mechanistic insights into a new clinical observation. T2DM patients with chronic renal dysfunction and low levels of ATIII appeared to present a higher risk of developing CAD, which suggests that kidney functions and ATIII levels should be monitored more closely, and proactive measures should be taken to prevent or mitigate the development of CAD in T2DM patients.

Diabetes is well-known to contribute to accelerate atherosclerosis [21–24], along with atherosclerotic lesions starting to develop in the early stages of chronic kidney disease [25]. Renal dysfunction also promotes hypercoagulability, inflammation, dyslipidemia, and endothelial dysfunction, which are all strongly contributing to the development of CAD [26]. However, the underlying biological mechanisms connecting chronic renal insufficiency to CAD are not fully elucidated, while hypercoagulability and inflammation are believed to play important roles in this progression.

As the most important coagulation factor inhibitor, ATⅢ is a small glycoprotein produced by the liver, which can inactivate thrombin, thus inhibiting the coagulation cascade and playing a crucial role in hypercoagulability [27]. With the deterioration of renal function, hypercoagulability develops gradually in vivo, along with increased thrombin generation and decreased antithrombin [7]. In 1982, Chan et al. reported a marked decrease in ATⅢ levels in patients with diabetic nephropathy [28]. Since then, numerous studies were conducted to explore the reno-protective effects of ATⅢ [11, 12, 29, 30]. It has been proved that endogenous ATⅢ deficiency will worsen renal function [12] and exogenous administration will ameliorate renal injury [11, 30] and delay transition to chronic kidney disease [29]. However, few studies focused on the association between renal function and ATⅢ in Chinese T2DM population. Our results showed that decreased renal function was related to decreased ATⅢ in Chinese T2DM population, which further confirmed the existence of hypercoagulability in patients with renal insufficiency. Some researchers have suggested that the decrease in ATⅢ in people with renal insufficiency may be a result of protein loss in the urine [31]. However, even after adjusting for ACR in eGFR-ATⅢ logistic models, we still observed the positive dose-response relationships between them, which reinforced the robustness of our findings.

Previously, numerous studies have identified an important role of ATⅢ in the development of CAD [13, 14, 32]. For instance, Thompson et al. conducted a nine-year cohort and reported that the risk of cardiac events was significantly negatively related to antithrombin Ⅲ [13], which is consistent with our results. The mechanism by which ATⅢ affects CAD is multifaceted. Besides the crucial role in regulating hypercoagulability, its anti-inflammation function is also essential to CAD development. It has been hypothesized that AT binds via its D-helix to vessel wall HSPGs, thereby inducing the synthesis of prostacyclin and inhibition of NF-kB in vascular endothelial cells [33]. Moreover, ATⅢ has been shown to exert strong cardioprotective effects. Ma.Y et al raised that AT binds to HSPGs in heart tissues to invoke a cardioprotective function by triggering cardiac AMPK activation [16]. Collectively, these studies suggested that the correlation between decreased ATⅢ and increased CAD risk might be due to the combined effects of hypercoagulability, inflammation, and decreased cardioprotective function.

More importantly, our results firstly suggested that individuals with low levels of eGFR and ATⅢ had the highest risk of CAD and the negative association of renal function with CAD risk was strengthened by ATⅢ. We had observed the important medication role of ATⅢ, while no significant multiplicative or additive effects of ATⅢ and eGFR on CAD risk were observed. Recently, mediation analysis has become a useful approach in epidemiological studies for understanding causal relationships. The bootstrapping mediating effect test has been shown to provide better test efficiency due to its replication of sample strength [34, 35]. In our study, we employed the bootstrapping method with 5000 bootstrap iterations. We statistically demonstrating that ATⅢ played a mediation role in the negative association between renal function and CAD risk, which has not been elucidated in previous human or animal studies. Based on previous research, we put forward following conjecture. Patients with chronic kidney dysfunction develops the hypercoagulable state in vivo accompanied by decreased ATⅢ [7], which further promotes inflammation within the vascular endothelium[15, 36], and weakens cardioprotective function by triggering cardiac AMPK activation [16], accelerating the progression of cardiovascular diseases. The study also revealed that other coagulation indicators, such as Fibrinogen, FDP, and D-Dimer, did not have statistically mediation effects between renal function and CAD risk (Supplementary Table S3). The specific mediation role of ATⅢ might attribute to its anti-inflammatory and cardioprotective function.

This study has several notable strengths. Firstly, a relatively large Chinese population was included to reveal the potential roles of ATⅢ. Secondly, the use of mediation analysis provides a novel approach to exploring the potential biological mechanisms underlying this association. Although mediation analysis cannot definitively establish a causal molecular pathway, it does provide an additional step in understanding the underlying mechanisms. Finally, the study carefully controlled for various potential confounders in the analysis to minimize bias and increase the validity of our findings. Several limitations should be acknowledged in our study. First, one-time blood to measure renal function might not be accurate, compared to the use of multiple blood samples. Second, not all of CAD patients underwent coronary angiography to investigate the severity of CAD, although we had used multiple diagnostic criteria to minimize the misclassification as much as possible. Third, we couldn’t ensure all the mediation factors were considered in our study, although we had conducted mediation analysis among other coagulation indicators, and showed no significance (Supplemental Table 5). Finally, the role of ATⅢ were assessed in our study based on a population-level analysis. Further cellular or animal studies are needed to verify the concrete molecular pathways of ATⅢ in the pathogenesis of CAD related to chronic renal insufficiency.

Acknowledgments:

The authors would like to thank the patients, their families and all investigators involved in this study.

Funding:

This work was supported by the National Natural Science Foundation of China (No. 81870568, Shaohua Wang).

Conflicts of Interest: None of the authors have any potential conflicts of interest associated with this research.

W.J. Tu, Y. Xue, D. Nie, The Prevalence and Treatment of Diabetes in China From 2013 to 2018. Jama. 327, 1706 (2022)
M. Tonelli, P. Muntner, A. Lloyd, B.J. Manns, S. Klarenbach et al., Risk of coronary events in people with chronic kidney disease compared with those with diabetes: a population-level cohort study. Lancet. 380, 807–814 (2012)
E.L. Schiffrin, M.L. Lipman, J.F. Mann, Chronic kidney disease: effects on the cardiovascular system. Circulation. 116, 85–97 (2007)
A. Solini, G. Penno, E. Bonora, C. Fondelli, E. Orsi et al., Age, renal dysfunction, cardiovascular disease, and antihyperglycemic treatment in type 2 diabetes mellitus: findings from the Renal Insufficiency and Cardiovascular Events Italian Multicenter Study. J. Am. Geriatr. Soc. 61, 1253–1261 (2013)
van der M. Velde, K. Matsushita, J. Coresh, B.C. Astor, M. Woodward et al., Lower estimated glomerular filtration rate and higher albuminuria are associated with all-cause and cardiovascular mortality. A collaborative meta-analysis of high-risk population cohorts. Kidney Int. 79, 1341–1352 (2011)
A.S. Go, G.M. Chertow, D. Fan, C.E. McCulloch, C.Y. Hsu, Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl. J. Med. 351, 1296–1305 (2004)
M.J. Adams, A.B. Irish, G.F. Watts, R. Oostryck, G.K. Dogra, Hypercoagulability in chronic kidney disease is associated with coagulation activation but not endothelial function. Thromb. Res. 123, 374–380 (2008)
R.D. Rosenberg, Biochemistry of heparin antithrombin interactions, and the physiologic role of this natural anticoagulant mechanism. Am. J. Med. 87, 2s–9s (1989)
Z. Lu, F. Wang, M. Liang, SerpinC1/Antithrombin III in kidney-related diseases. Clin. Sci. (Lond). 131, 823–831 (2017)
G. Blavy, C. Kouame, [Renal pathology in the Ivory Coast: exploration and functional activity of antithrombin iii]. Nephrologie. 15, 387–389 (1994)
Z. Lu, D. Cheng, J. Yin, R. Wu, G. Zhang et al., Antithrombin III Protects Against Contrast-Induced Nephropathy. EBioMedicine. 17, 101–107 (2017)
F. Wang, G. Zhang, Z. Lu, A.M. Geurts, K. Usa et al., Antithrombin III/SerpinC1 insufficiency exacerbates renal ischemia/reperfusion injury. Kidney Int. 88, 796–803 (2015)
S.G. Thompson, C. Fechtrup, E. Squire, U. Heyse, G. Breithardt et al., Antithrombin III and fibrinogen as predictors of cardiac events in patients with angina pectoris. Arterioscler. Thromb. Vasc Biol. 16, 357–362 (1996)
J. Lu, D. Niu, D. Zheng, Q. Zhang, W. Li, Predictive value of combining the level of lipoprotein-associated phospholipase A2 and antithrombin III for acute coronary syndrome risk. Biomed. Rep. 9, 517–522 (2018)
M. Uchiba, K. Okajima, K. Murakami, Effects of various doses of antithrombin III on endotoxin-induced endothelial cell injury and coagulation abnormalities in rats. Thromb. Res. 89, 233–241 (1998)
Y. Ma, J. Wang, J. Gao, H. Yang, Y. Wang et al., Antithrombin up-regulates AMP-activated protein kinase signalling during myocardial ischaemia/reperfusion injury. Thromb. Haemost. 113, 338–349 (2015)
A.S. Levey, L.A. Stevens, C.H. Schmid, Y.L. Zhang, A.F. 3 Castro rd et al., A new equation to estimate glomerular filtration rate. Ann. Intern. Med. 150, 604–612 (2009)
18, KDIGO 2022 Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease. Kidney Int. 102, S1–s127 (2022)
J.C. Biesanz, C.F. Falk, V. Savalei, Assessing Mediational Models: Testing and Interval Estimation for Indirect Effects. Multivar. Behav Res. 45, 661–701 (2010)
M.W. Cheung, Comparison of methods for constructing confidence intervals of standardized indirect effects. Behav. Res. Methods. 41, 425–438 (2009)
K. Mohammedi, M. Woodward, M. Marre, S. Colagiuri, M. Cooper et al., Comparative effects of microvascular and macrovascular disease on the risk of major outcomes in patients with type 2 diabetes. Cardiovasc. Diabetol. 16, 95 (2017)
J.R. Brownrigg, C.O. Hughes, D. Burleigh, A. Karthikesalingam, B.O. Patterson et al., Microvascular disease and risk of cardiovascular events among individuals with type 2 diabetes: a population-level cohort study. Lancet Diabetes Endocrinol. 4, 588–597 (2016)
E. Dal Canto, P.J.M. Elders, van der A.A. Heijden, van A.J. Ballegooijen, B.I. Lissenberg-Witte et al., (2022) Kidney function measures and cardiovascular outcomes in people with diabetes: the Hoorn Diabetes Care System cohort. Diabetologia
M. Afkarian, M.C. Sachs, B. Kestenbaum, I.B. Hirsch, K.R. Tuttle et al., Kidney disease and increased mortality risk in type 2 diabetes. J. Am. Soc. Nephrol. 24, 302–308 (2013)
R.T. Gansevoort, R. Correa-Rotter, B.R. Hemmelgarn, T.H. Jafar, H.J. Heerspink et al., Chronic kidney disease and cardiovascular risk: epidemiology, mechanisms, and prevention. Lancet. 382, 339–352 (2013)
K. Skalsky, A. Shiyovich, T. Steinmetz, R. Kornowski, (2022) Chronic Renal Failure and Cardiovascular Disease: A Comprehensive Appraisal. J. Clin. Med. 11
A.R. Rezaie, H. Giri, Anticoagulant and signaling functions of antithrombin. J. Thromb. Haemost. 18, 3142–3153 (2020)
V. Chan, C.K. Yeung, T.K. Chan, Antithrombin III and fibrinogen degradation product (fragment E) in diabetic nephropathy. J. Clin. Pathol. 35, 661–666 (1982)
J. Yin, F. Wang, Y. Kong, R. Wu, G. Zhang et al., Antithrombin III prevents progression of chronic kidney disease following experimental ischaemic-reperfusion injury. J. Cell. Mol. Med. 21, 3506–3514 (2017)
Y. Kong, J. Yin, D. Cheng, Z. Lu, N. Wang et al., Antithrombin III Attenuates AKI Following Acute Severe Pancreatitis. Shock. 49, 572–579 (2018)
N.D. Vaziri, P. Paule, J. Toohey, E. Hung, S. Alikhani et al., Acquired deficiency and urinary excretion of antithrombin III in nephrotic syndrome. Arch. Intern. Med. 144, 1802–1803 (1984)
K.M. Pelkonen, U. Wartiovaara-Kautto, M.S. Nieminen, K. Ahonen, J. Sinisalo, Low normal level of protein C or of antithrombin increases risk for recurrent cardiovascular events. Blood Coagul Fibrinolysis. 16, 275–280 (2005)
J.S. Bae, A.R. Rezaie, Mutagenesis studies toward understanding the intracellular signaling mechanism of antithrombin. J. Thromb. Haemost. 7, 803–810 (2009)
J. Pearl, Interpretation and identification of causal mediation. Psychol. Methods. 19, 459–481 (2014)
L. Richiardi, R. Bellocco, D. Zugna, Mediation analysis in epidemiology: methods, interpretation and bias. Int. J. Epidemiol. 42, 1511–1519 (2013)
N. Harada, K. Okajima, S. Kushimoto, H. Isobe, K. Tanaka, Antithrombin reduces ischemia/reperfusion injury of rat liver by increasing the hepatic level of prostacyclin. Blood. 93, 157–164 (1999)

Download PDF

Journal Publication

published 08 Jan, 2024

Read the published version in Endocrine →

Reviewers agreed at journal
04 Sep, 2023
Reviewers invited by journal
04 Sep, 2023
Editor assigned by journal
30 Aug, 2023
First submitted to journal
28 Aug, 2023

You are reading this latest preprint version

Mediation effect of antithrombin III between chronic renal insufficiency and coronary artery disease in T2DM patients

Status:

Journal Publication

Version 1

Abstract

Figures

1. Introduction

2. Methods and materials

2.1. Study Population

2.2. Data Collection

2.3. Definition of Variables

2.4. Statistical Analysis

3. Results

3.1. Basic characteristics

3.2. Associations between renal function and the risk of coronary artery disease.

3.3. Associations between renal function and ATⅢ.

3.4. Associations between ATⅢ and the risk of coronary artery disease.

3.5. Joint effects of ATⅢ and renal function on coronary artery disease risk

3.6. Mediating role of ATⅢ on the association between renal function and coronary artery disease.

4. Discussion

Declarations

References

Supplementary Files

Status:

Journal Publication

Version 1