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Effect of limb ischemic preconditioning on the indirect index of insulin resistance in maintenance hemodialysis patients

BMC Cardiovascular Disorders volume 25, Article number: 238 (2025) Cite this article

Abstract

Background

Poor prognosis of maintenance hemodialysis (MHD) patients, including cardiovascular disease (CVD) and protein-energy wasting (PEW), is strongly associated with insulin resistance (IR). Previous studies have revealed that limb ischemic preconditioning (LIPC), as an intervention, is effective in reducing inflammation and oxidative stress levels in patients. The aim of this study was to elucidate the effects of LIPC on IR indirect indices, inflammation and oxidative stress indices, and to further explore the potential mechanisms of LIPC in reducing IR indices.

Methods

A retrospective analysis was performed on 62 patients with MHD who had previously undergone limb ischemia preconditioning (LIPC) or sham surgery (Sham). General clinical and laboratory data were collected. Furthermore, to assess the IR status of MHD patients, the following indices were employed: triglyceride-glucose index (TyG), triglyceride-glucose body mass index (TyG-BMI), triglyceride-to-high-density lipoprotein cholesterol ratio (TG/HDL-C), and metabolic score of insulin resistance (METS-IR). Inflammation and oxidative stress indicators included high-sensitivity C-reactive protein (hs-CRP), hs-CRP /albumin ratio (CAR), serum malondialdehyde (MDA) and superoxide dismutase (SOD). Mediation analysis was conducted using Model 4 in the SPSS PROCESS macro version 4.1.

Results

Following a four-week experiment, hs-CRP (15.46 ± 3.60 vs. 10.53 ± 5.42, p < 0.001), CAR (0.39 ± 0.10 vs. 0.26 ± 0.13, p < 0.001) and MDA (8.46(6.71,9.85) vs. 5.99(5.11,7.89), p = 0.001) indices were significantly decreased in the MHD patients of the LIPC group, whereas SOD indices (215.07(180.27,286.45) vs. 267.76(228.32,319.54), p = 0.012) were significantly higher. Only hs-CRP (-4.93 ± 5.68 vs. 0.16 ± 5.39, p = 0.001) and CAR (-0.14 ± 0.14 vs. -0.001 ± 0.15, p = 0.001) were significantly different in the LIPC group compared to the Sham group. In contrast, the changes in MDA (p = 0.058) and SOD (p = 0.107) were not statistically significant between groups. The intra- and inter-group differences in the four indirect indices of IR were significant (p < 0.05). The heatmap revealed a notable correlation between the changes in hs-CRP and CAR levels and the changes in the IR indirect indices. In addition, The mediation model showed that the inflammatory indicators hs-CRP played a partial mediating role in the improvement of IR indices (TyG-BMI) by LIPC.

Conclusion

LIPC has an excellent ability to inhibit inflammation and peroxidation. In addition, in MHD patients, inflammation plays a significant role in the process of LIPC improving IR index.

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Introduction

The incidence and prevalence of chronic kidney disease (CKD) is increasing at an alarming rate globally, as is the number of complications associated with it. Patients with CKD frequently suffer from cardiovascular disease (CVD) and protein-energy wasting (PEW), particularly in ESRD, where all-cause mortality is significantly higher [1]. Insulin resistance (IR), which is defined as reduced or impaired insulin sensitivity in target organs or tissues, manifested by impaired glucose uptake and oxidation, is an essential risk factor in the pathogenesis of diabetes mellitus and cardiovascular disease [2]. Notably, several clinical outcomes (cardiovascular disease, death [3], and protein energy expenditure [4]) in ESRD patients receiving hemodialysis have been associated with IR. IR its self is a risk for CVD and strongly associates with other CVD risks (dyslipidemia, hypertension, and inflammation). In ESRD patients undergoing hemodialysis, the cardiovascular risks worsen the arterial stiffness which contributes to the development of cardiovascular events and diseases [3]. In addition, protein metabolism is profoundly affected by insulin signaling as well. The activation of insulin receptor, in concert with insulin growth factor-1 receptor, activates PI3K and PKB/Akt which in turn promotes protein synthesis [5]. Thus, resistance to the protein anabolic effects of insulin may be an important factor contributing to PEW in MHD patients. Nonetheless, the etiology and mechanisms that lead to IR are complex and involve numerous factors, and yet some of the more well-defined mechanisms include inflammation and oxidative stress [6].

Ischemic preconditioning (IPC) is a straightforward, safe, and well-tolerated intervention, originally introduced to the heart as a protective measure against the harmful effects of myocardial ischemia/reperfusion, as described by Murry and colleagues in 1986. IPC is an endogenous protective mechanism in which brief periods of ischemia or hypoxia safeguard the heart from subsequent, more prolonged ischemic damage [7]. Subsequently, IPC has been demonstrated to be effective in other tissues as well. In a meta-analysis, Jiachang included 30 randomized controlled trials covering a total of 7,244 patients. The results showed that IPC significantly reduced the risk of acute kidney injury in patients undergoing cardiac and vascular interventions compared to controls [8]. In a series of studies to improve complications of MHD, we found that IPC was effective in reducing the incidence of hypotension in MHD patients [9]. In addition, by suppressing oxidative stress and inflammation, IPC was able to improve sleep disorders in MHD patients [10]. In the continuous development of IPC, limb ischemic preconditioning (LIPC) has been developed based on IPC. Compared with direct IPC, LIPC has the advantage of protecting without direct stress on the target organ [11]. A recent study evaluating the optimal IPC regimen demonstrated that LIPC provided superior protection when the number and duration of ischemic episodes were maintained constant [7]. The inhibitory effects of LIPC on inflammation and oxidative stress have attracted our attention, and therefore it is envisioned to improve IR in MHD patients by LIPC.

Practical indirect indicators of insulin sensitivity have been validated in MHD patient populations [12,13,14]. For instance, the triglyceride-glucose index (TyG) [15], the triglyceride-glucose body mass index (TyG-BMI) [16], the triglyceride-to-high-density lipoprotein cholesterol ratio (TG/HDL-C) [17], and the metabolic score for insulin resistance (METS-IR) [18] are examples. In the present study, we will use these four indices to reflect IR. Given that the pathogenesis of IR is closely related to inflammation and oxidative stress, LIPC has also indicated positive effects in ameliorating inflammation as well as oxidative stress [19, 20]. We hypothesized that LIPC may improve the IR index in MHD patients by reducing inflammation and oxidative stress. Therefore, the aim of this study was to assess the efficacy of LIPC in reducing IR in MHD patients, to explore in depth its specific effects on inflammation and oxidative stress status, and to validate the role of inflammation and oxidative stress in the pathway of LIPC in improving IR indices.

Method

Experimental design

This study is a retrospective analysis, with data collected from patients who implemented LIPC intervention and sham surgery (Sham) intervention from November 2022 to June 2023 in this subject group (Fig. 1). The inclusion criteria were as follows: (1) age greater than or equal to 18 years and less than 80 years; (2) duration of continuous dialysis greater than 3 months; (3) All patients maintained a stable medication regimen throughout the study period, with no dosage adjustments. The exclusion criteria were as follows: (1) patients with type 1 diabetes mellitus; (2) patients taking medication to lower triglycerides (fenofibrate); (3) patients in an acute state of infection or undergoing anti-inflammatory therapy were excluded; (4) patients receiving hormonal and/or immunosuppressive therapy were excluded; (5) patients with insufficient dialysis dose (single cell Kt/V < 1.2); (6) patients receiving hemoperfusion during the study period; (7) patients with incomplete clinical and laboratory data. The study received approval from the Medical Ethics Committee of Changzhou Second People’s Hospital ([2023]KY213-01).

Fig. 1
figure 1

Flow chart of patient selection

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Limb ischemia pretreatment program

The patient was positioned supine, with the lower limb on the operative side elevated such that the angle between the femur and the bed was approximately 30–45 degrees, and LIPC (LIPC group) or sham LIPC (control group) was performed once a day utilizing a LIPC device (RIP-906D, Nanfo Science and Technology Co., Ltd., Shenzhen, China). Blood pressure at the point of pressurization was measured prior to and following the procedure to observe the effectiveness of the intervention. LIPC: The LIPC cuff was applied to the patient’s femur (left or right) near the knee joint and inflated and pressurized for 5 min (200 mmHg), subsequently relaxed for 5 min (0 mmHg) for five consecutive cycles, for a total of 50 min. Pseudo-LIPC operation: the operation was the same as the LIPC operation except that the pressurization pressure of 20mmHg caused pseudo-ischemia of the limb. In both groups, the lower limb undergoing intervention was rotated daily to ensure both limbs received sufficient peripheral treatment over a 4-week period. Each participant received comprehensive instructions on how to operate the LIPC device. Patient usage and any changes in clinical symptoms were closely monitored on a daily basis through WeChat.

Data collection and definition

General patient information and laboratory test data were obtained from the healthcare system. General information included age, sex, height and weight (prior to and following each hemodialysis session), past medical history, baseline medication, as well as duration of hemodialysis. Laboratory tests included glycosylated hemoglobin, PTH, albumin, glucose, urea nitrogen, creatinine, uric acid, total cholesterol, triglycerides, HDL, LDL, and KT/V at baseline and the end of the intervention. Besides, inflammation and oxidative stress indices included high-sensitivity C-reactive protein (hs-CRP), serum malondialdehyde (MDA), and superoxide dismutase (SOD), were detected by enzyme-linked immunosorbent assay (ELISA). All laboratory data were collected from blood samples taken from patients prior to the initiation of fasting and short-gap dialysis. CAR was calculated from hs-CRP/albumin. The urea clearance index (Kt/V) values were calculated using the Daugirdas single-compartment model equation. Body mass index (BMI) was calculated as weight (kg) divided by height (m2) squared. The IR index was calculated according to the following formula: TyG index = Ln [TG (mg/dL) × FBG (mg/dL) ÷ 2]; TyG-BMI index = TyG × BMI (kg/m2); TG/HDL-C = TG (mg/dl) ÷ HDL-C (mg/dl); METS-IR = Ln [(2 × FBG (mg/dl)) + TG ( mg/dl)] × BMI (kg/m2) ÷ Ln [HDL-C (mg/dl)].

Statistical analysis

In this paper, PASS2021 software was used to calculate the sample size. Based on evidence from the relevant literature and preliminary experimental results [10], based on a set setting of bilateral α = 0.05, power of test = 90%, and a remission rate of approximately 10% in the control group and up to 55% in the LIPC-treated group. Subjects in the experimental and control groups were distributed in a 1:1 ratio with a withdrawal rate of 20%. Hence, the estimated total sample size should be no less than 46 subjects.

Data were analyzed employing SPSS version 26.0. Normally distributed continuous variables are expressed as mean and standard deviation. Non-normally distributed continuous variables are presented as median values with interquartile ranges. Categorical variables are reported as frequencies and percentages. To compare the two groups of normal variables, an independent samples t-test was employed, while for skewed and categorical variables, the Mann-Whitney U-test, chi-square test and Fisher’s exact test were employed. Furthermore, Outcome variables before and after the intervention were tested using the paired samples t-test and Wilcoxon paired rank test, respectively. Analysis of covariance (ANCOVA) tests were adopted to determine the level of significant difference between the two groups after the intervention while adjusting for baseline measurements, age and BMI. Post hoc analyses were conducted using the Bonferroni-Dunn method to correct for multiple comparisons. Spearman rank correlation analyses were employed to evaluate the relationships between changes in indicators of insulin resistance, inflammation, and oxidative stress, with the results visualized using Origin software. Model 4 in the SPSS plug-in PROCESS 4.1 was used to test for mediated effects. Sensitivity analyses were conducted to adjust for confounders. The following three models were included. Model1: unadjusted for covariates. Model2: adjusted for age, sex, BMI, glucose-lowering medication, insulin. Model3: adjusted for age, sex, BMI, glucose-lowering medication, insulin, blood glucose, urea nitrogen, creatinine, uric acid, albumin, HDL-C, and triglycerides. A two-sided p < 0.05 was considered significant.

Result

Comparison of baseline data between the LIPC group and sham group

In this study, the final 62 MHD patients [LIPC (n = 30) and sham (n = 32)] were included in the analysis. The baseline characteristics of the patients are indicated in Table 1, the distribution of gender, mean age, BMI, laboratory characteristics, and the number of years on dialysis were not significantly different between the two groups (p > 0.05).

Table 1 Baseline characteristics of subjects in the LIPC and Sham groups
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Comparison of inflammation and oxidative stress indices between the LIPC and Sham groups during the study period

Changes in inflammation and oxidative stress indices in patients before and after the LIPC or Sham intervention during the 4-week trial are illustrated in Table 2. At the start of the study, no significant differences were observed between the groups (p > 0.05). At the end of the 4-week intervention, within-group comparisons showed significant reductions in hs-CRP and CAR (p < 0.001) in the LIPC group. In addition, LIPC significantly reduced hs-CRP and CAR levels compared to the Sham group, and this result remained significant between study groups even after adjusting for baseline values, age, and BMI. Despite the fact that the oxidative indices MDA and SOD were substantially altered in the within-group comparison, the changes in MDA and SOD did not reach significant levels in comparison to the Sham group (p > 0.05).

Table 2 Intra- and inter-group comparisons of inflammation and oxidative stress indices in subjects before and after intervention in LIPC and Sham groups
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Comparison of insulin resistance index between LIPC and sham groups during the study period

Changes in IR indices at the beginning and end of the trial are illustrated in Table 3. No significant changes were observed in the baseline distribution of IR indices between patients in the LIPC and sham groups (p > 0.05). After the 4-week intervention, within-group comparisons indicated significant reductions in TyG (p < 0.001), TyG-BMI (p < 0.001), and TG/HDL-C (p = 0.007) in the LIPC group, while METS-IR (p = 0.03) was also significantly lower. There were differences in TyG (p < 0.001), TyG-BMI (p < 0.001), TG/HDL-C (p < 0.001), as well as METS-IR (p = 0.03) between the two groups. Between-group differences in the four indices remained statistically significant, even after adjusting for baseline values, age, and BMI. In summary, after a 4-week intervention, LIPC significantly reduced hs-CRP, CAR, and MDA levels as well as indirect indicators of insulin resistance in MHD patients, while significantly elevating SOD levels.

Table 3 Intra- and inter-group comparison of insulin resistance indices in subjects before and after intervention in LIPC and Sham groups
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Correlation analysis of inflammation and oxidative stress indexes and IR index change values during the study period

The correlation between LIPC or Sham intervention mode, inflammation and oxidative stress change values, and IR change values are revealed in Fig. 2. The results demonstrated that with the implementation of LIPC intervention, the less hs-CRP, the lower the IR index. Changes between IR indices (TyG, TyG-BMI, TG/HDL-C, METS-IR) were all significantly correlated. Surprisingly, no significant correlation was observed between the change in SOD values and the mode of intervention or the IR index. However, one of the change values in SOD showed a trend toward significance with the LIPC intervention (P = 0.107).

Fig. 2
figure 2

Correlation between LIPC, inflammation and oxidative stress indicators and IR index

The positive correlation is red, and the negative correlation is blue. The correlation ranges from − 1 to + 1; the higher the correlation, the larger the circle. × means no association

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Mediating role of inflammation in the reduction of IR index by LIPC

The relationship between intervention modality, inflammation, and IR change values was analyzed, revealing a correlation between the LIPC intervention and the IR index, as well as an association between the LIPC intervention and changes in inflammation values. In addition, the inflammation change value affected the IR index. Therefore, the value of change in IR index was used as the outcome variable, LIPC or Sham intervention modality as the independent variable, and hs-CRP and CAR as the mediating variables. A mediating effect was considered significant if the 95% confidence interval (CI) did not include zero. Bootstrap test results indicated that hs-CRP and CAR played a partial mediating role in the improvement of IR indices (TyG and TyG-BMI) by LIPC. However, this mediating effect was not present in TG/HDL-C, METS-IR (results not shown).

Sensitivity analysis

To assess the robustness of the mediation model, we implemented a sensitivity analysis and further adjusted for confounders in the process (Tables 4 and 5). The results showed that only hs-CRP remained significant in the relationship between LIPC and ΔTyG-BMI after adjusting for confounders. Notably, the inflammatory marker CAR, which exhibited significance in model 2, unexpectedly lost its effect in model 3.

Table 4 Sensitivity analysis with hs-CRP as a mediating variable
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Table 5 Sensitivity analysis with CAR as a mediating variable
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Intermediation model

After adjusting for confounders, the mediation model, as shown in Fig. 3, showed that hs-CRP partially mediated the effect of the LIPC intervention on TyG-BMI, and its mediating effect accounted for 24.10% of the total effect in MHD patients (Table 6).

Fig. 3
figure 3

Modeling of Δhs-CRP as a mediator of the effect of LIPC on ΔTyG-BMI. The presented effect size of each relationship was a standardized coefficient. The mediation effect was considered to be significant if the 95% confidence interval (CI) did not include zero, *P < 0.05, **P < 0.01. LIPC significantly affected Δhs-CRP and ΔTyG-BMI. (Coefficient = -4.34, P < 0.01; Coefficient = -9.06, P < 0.01). Δhs-CRP significantly affected ΔTyG-BMI. (Coefficient = 0.66, p < 0.05)

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Table 6 Decomposition of total, direct and mediating effects in the ΔTyG-BMI mediation model
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Discussion

This study aims to evaluate the impact of LIPC intervention on IR in ESRD patients undergoing hemodialysis. In our research, the IR status of MHD patients was assessed using TyG, TyG-BMI, TG/HDL-C, and METS-IR. The results indicated that a 4-week LIPC intervention led to an improvement in the IR index compared to the control group. In addition, we found that the change values of IR index were positively correlated with the change values of hs-CRP or CAR, which is generally consistent with the results of previous studies. Thus, proceeding to our mediation modeling analysis, we found that the inflammation indicators hs-CRP or CAR played a partial mediating role in the improvement of IR index (TyG, TyG-BMI) by LIPC. This not only further supports the potential of LIPC as a treatment for IR in MHD patients but also offers new insights for exploring the pathogenesis of IR.

Insulin resistance is commonly observed in patients with MHD [21], as insulin-mediated glucose metabolism relies on the proper functioning of the downstream insulin receptor substrate (IRS)-PI3K-Akt signaling pathway. Besides, at the molecular level, cells sense insulin through the insulin receptor, and signals are propagated through a molecular cascade collectively known as phosphatidylinositol 3-kinase (PI3K) as well as the downstream protein kinase B/Akt (PKB/Akt) signaling pathway [22]. This pathway is influenced by factors related to the uremic environment, such as inflammation, acidosis, vitamin D deficiency, anemia, and the accumulation of uremic toxins [23]. In a prospective report, one mechanism linking the inflammatory response to the development of insulin resistance involves the activation of serine/threonine ‘stress kinases’ that may be activated in response to inflammatory cytokines. Once activated, these stress kinases impact the insulin signaling cascade by phosphorylating IRS proteins on serine residues in insulin-sensitive cells, including hepatocytes, adipocytes, and myocytes [24].

While the hyperinsulin-normoglycemic clamp is regarded as the most accurate method for measuring insulin sensitivity, its clinical use has been restricted due to its high cost and complexity [16]. Recent studies have established a connection between intrahepatic and intrapancreatic fat accumulation and IR. Hyperesterolemia may contribute to the accumulation of fatty acids in non-adipose tissues, such as the liver, muscle and heart, which in turn induces ectopic lipid deposition that is lipotoxic [18]. In addition, it has been suggested that the cause of insulin resistance is not limited to the accumulation of fat within adipose tissue per se, but may also be closely related to the inflammatory response triggered by ectopic lipid deposition. Chronic low-grade systemic inflammation impairs insulin function within the insulin signaling pathway, disrupting glucose homeostasis and resulting in widespread metabolic dysregulation [25]. Based on these insights, convenient and effective methods for assessing IR, including TyG, TyG-BMI, TG/HDL-C, and METS-IR, have been established. In particular, for the Chinese cohort, TyG and TyG-BMI showed superior sensitivities compared to TG/HDL-C ratio and METS-IR [26]. These simple, convenient, and low-cost alternatives do not require insulin dosing and can be used in all subjects regardless of their insulin treatment status [27].

IR is considered to be a central pathogenetic feature of a cluster of cardiovascular risk factors, including hypertension, impaired glucose tolerance, hyperinsulinemia, dyslipidemia, as well as vascular stiffness [28, 29]. Endothelial dysfunction is an early manifestation of insulin resistance and plays a key role in the development of atherosclerosis [30]. To date, two models of endothelium-specific insulin receptor disruption have been reported. Vascular endothelial insulin receptor knockout mice and endothelium-specific dominant inactivating mutant insulin receptor overexpressing transgenic mice have demonstrated that intact insulin signaling in the endothelium is required for the maintenance of normal cellular function [31]. Moreover, clinical studies have demonstrated similar evidence that endothelial dysfunction is a key feature of CVD, and its associated risk factor states are also demonstrated in clinical studies [32]. In addition to endothelial dysfunction, several mechanisms may contribute to the potential causal relationship between IR and CVD. These include the promotion of oxidative stress, systemic inflammation, and the activation of the renin-angiotensin-aldosterone system [33].

Insulin is a well-recognized net protein anabolic hormone and resistance to the protein anabolic effects of insulin may be an essential factor contributing to PEW and sarcopenia in patients with MHD [34]. The various etiologies of PEW are still being refined, but the common pathways underlying all these mechanisms seem to involve increased protein degradation coupled with decreased protein synthesis [35]. PEW occurs not only due to inadequate nutritional intake in MHD patients, but also due to muscle depletion caused by inflammation, and metabolic and hormonal dysregulation, resulting in browning of adipose tissue, muscle atrophy, and increased resting energy expenditure, which ultimately leads to a severe poor prognosis for patients with MHD [36]. Additionally, the hemodialysis procedure itself is an essential factor in accelerating this process [37]. In the baseline diabetes-free cohort study, ED Siew et al. found that IR was common among patients with MHD and had a significant impact on skeletal muscle proteolysis. This association remained significant even after adjusting for various covariates. It is implied that PEW in MHD is strongly associated with IR [37]. Therefore, IR, which serves as a common risk factor for both CVD and PEW, is closely linked to poor prognosis in patients with MHD. Interventions aimed at improving IR could potentially enhance clinical outcomes and reduce mortality in this patient population.

In MHD patients, impaired endothelial function is closely associated with decreased NO bioavailability, a change that may exacerbate local inflammatory responses due to diminished NO anti-inflammatory properties. Notably, LIPC exhibited significant anti-inflammatory potential, which was exerted by efficiently blocking inflammatory signaling pathways [38]. In a rat model of myocardial infarction, A. Valtchanova-Matchouganska found that LIPC attenuated myocardial injury by inhibiting myocardial CRP production [39]. In a mouse model of sepsis-induced acute kidney injury, LIPC may exert anti-inflammatory and anti-apoptotic effects on septic AKI by inducing a systemic upregulation of miR-21 [40]. Of particular importance, this mechanism was also validated in healthy volunteers: ischemic preconditioning of the upper arm not only attenuated endothelial dysfunction in the contralateral arm, but also reduced inflammatory cell activation and suppressed the expression of pro-inflammatory genes in circulating leukocytes, achieving a teleprotective effect [41]. In an in-depth study by our group, we found that LIPC ameliorated contrast-induced acute kidney injury (CI-AKI) in mice, and the underlying mechanisms may include inhibition of mPTP opening, suppression of oxidative stress, and attenuation of inflammation through GSK-3β phosphorylation [42]. In a clinical study, by comparing it to the Sham group of MHD patients, we found significant improvements in restless legs syndrome and sleep quality scores in patients with LIPC intervention, which may be partly due to its reduction of ROS oxidation of DNA. Overall, in addition to the reduction in oxidative stress following prolonged ischemia, the LIPC group also exhibited a decrease in the inflammatory response [10]. Given that the pathogenesis of IR is closely related to inflammation and oxidative stress, LIPC has also demonstrated a positive effect in ameliorating inflammation and oxidative stress. Consequently, the main purpose of this study was to investigate whether LIPC was effective in reducing IR index in MHD patients and to further elucidate its potential mechanism of action.

Recent studies have indicated that the unifying mechanisms of insulin signaling and functional downregulation in MHD are increased inflammation and reactive oxygen species (ROS) production [31]. Furthermore, the interrelationships between inflammation and oxidative damage in the complex internal environment of MHD patients enable them to mutually activate one another [31]. For example, factors such as infections and uremic toxins can serve as triggers for this interplay. The mechanisms of action were therefore explored, and it was found that the improvement in IR indices by the LIPC intervention was, in part, mediated by the inhibition of hs-CRP. As mentioned previously, this may result from the effect of inflammation on the insulin signaling pathway. Consistent with our findings, Jin-Wen Xu et al. reported that CRP impairs insulin signaling by regulating Syk tyrosine kinase and RhoA, which in turn affects the phosphorylation of insulin receptor substrate-1, Akt, and endothelial nitric oxide synthase (eNOS) in vascular endothelial cells [30]. In addition, a recent study showed that infusion of inflammatory factors into healthy subjects directly inhibits glucose uptake and metabolism in skeletal muscle, thereby inducing insulin resistance. The underlying mechanism appears to be related to impaired phosphorylation of Akt substrate 160, leading to impaired GLUT4 translocation and glucose uptake [43]. Mengliu Yang, by constructing a CRP knockout (KO) rat model, determined that systemic reduction of CRP enabled rats to resist weight gain and insulin resistance induced by a high-fat diet and also significantly promoted energy expenditure and insulin-regulated glucose metabolism. glucose metabolism. These findings suggest that CRP is not merely an inflammation biomarker but also plays a pivotal role in regulating energy balance, body weight, insulin sensitivity, and glucose homeostasis [44]. Notably, although our study showed that LIPC was effective in reducing the levels of oxidative stress and IR, MDA and SOD did not show significant effects in the mediated effects model. This finding is at variance with our expectation because it has been widely reported in the research literature that LIPC can significantly inhibit oxidative stress, which is usually considered as one of the key factors inducing IR [45]. We speculate that this result may be related to the small sample size.

Limitations

This study has several limitations. Firstly, we did not include the insulin resistance index HOMA-IR because of its limited value for subjects receiving insulin therapy or patients with beta cell damage. Secondly due to the retrospective study it was not possible to add new indicators such as inflammatory indicators IL-6, TNF-α. Finally, the present study still suffers from a small observational sample size without longer term interventions and follow-up to understand the development of IR in the subjects, and some of the negative results may also be related to the small sample size. Therefore, prospective, multicenter, large sample size, randomized controlled studies are needed for further confirmation.

Conclusion

IR affects the prognosis of patients with MHD. Nonetheless, the etiology of IR is multifactorial, but chronic inflammation seems to be an important factor. The present study preliminarily validates LIPC as a safe and effective method to ameliorate IR in MHD patients.

Data availability

All data generated or analyzed during this study are included in this published article.

Abbreviations

MHD:

Maintenance hemodialysis

CVD:

Cardiovascular disease

PEW:

Protein-energy wasting

IR:

Insulin resistance

LIPC:

Limb ischemic preconditioning

TyG:

Triglyceride-glucose index

TyG-BMI:

Triglyceride-glucose body mass index

TG/HDL-C:

Triglyceride-to-high-density lipoprotein cholesterol ratio

METS-IR:

Metabolic score of insulin resistance

hs-CRP:

High-sensitivity C-reactive protein

MDA:

Serum malondialdehyde

SOD:

Superoxide dismutase

CKD:

Chronic kidney disease

IPC:

Ischemic preconditioning

RLS:

Restless Legs Syndrome

ELISA:

Enzyme-linked immunosorbent assay

BMI:

Body mass index

References

  1. Bi X, Ai H, Wu Q, Fan Q, Ding F, Hu C, Ding W. Insulin resistance is associated with Interleukin 1beta (IL-1beta) in Non-Diabetic Hemodialysis patients. Med Sci Monit. 2018;24:897–902.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Mustata S, Chan C, Lai V, Miller JA. Impact of an exercise program on arterial stiffness and insulin resistance in Hemodialysis patients. J Am Soc Nephrol. 2004;15(10):2713–8.

    Article  PubMed  Google Scholar 

  3. Duong TV, Shih CK, Wong TC, Chen HH, Chen TH, Hsu YH, Peng SJ, Kuo KL, Liu HC, Lin ET et al. Insulin Resistance and Cardiovascular Risks in Different Groups of Hemodialysis Patients: A Multicenter Study. BioMed research international. 2019;2019:1541593.

  4. Garibotto G, Sofia A, Russo R, Paoletti E, Bonanni A, Parodi EL, Viazzi F, Verzola D. Insulin sensitivity of muscle protein metabolism is altered in patients with chronic kidney disease and metabolic acidosis. Kidney Int. 2015;88(6):1419–26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Teta D. Insulin resistance as a therapeutic target for chronic kidney disease. J Ren Nutr. 2015;25(2):226–9.

    Article  CAS  PubMed  Google Scholar 

  6. Masenga SK, Kabwe LS, Chakulya M, Kirabo A. Mechanisms of oxidative stress in metabolic syndrome. Int J Mol Sci 2023, 24(9).

  7. Kocman EA, Ozatik O, Sahin A, Guney T, Kose AA, Dag I, Alatas O, Cetin C. Effects of ischemic preconditioning protocols on skeletal muscle ischemia-reperfusion injury. J Surg Res. 2015;193(2):942–52.

    Article  PubMed  Google Scholar 

  8. Hu J, Liu S, Jia P, Xu X, Song N, Zhang T, Chen R, Ding X. Protection of remote ischemic preconditioning against acute kidney injury: a systematic review and meta-analysis. Crit Care (London England). 2016;20(1):111.

    Article  Google Scholar 

  9. Mao Y, Xu L, Xu J, Tang Y, Liu T. Application value of limb ischemic preconditioning in preventing intradialytic hypotension during maintenance Hemodialysis. Kidney Blood Press Res. 2023;48(1):535–44.

    Article  CAS  PubMed  Google Scholar 

  10. Xu J, Qi Y, Tang Y, Zhang W, Zhang Q, Xu L, Ding Z, Liu T. Improvement of restless leg syndrome in maintenance Hemodialysis patients with limb ischemic preconditioning: a single-center randomized controlled clinical trial. Ren Fail. 2023;45(2):2283589.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Mahfoudh-Boussaid A, Zaouali MA, Hadj-Ayed K, Miled AH, Saidane-Mosbahi D, Rosello-Catafau J, Ben Abdennebi H. Ischemic preconditioning reduces Endoplasmic reticulum stress and upregulates hypoxia inducible factor-1alpha in ischemic kidney: the role of nitric oxide. J Biomed Sci. 2012;19(1):7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Xie E, Ye Z, Wu Y, Zhao X, Li Y, Shen N, Gao Y, Zheng J. The triglyceride-glucose index predicts 1-year major adverse cardiovascular events in end-stage renal disease patients with coronary artery disease. Cardiovasc Diabetol. 2023;22(1):292.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Jiang Z, Zhu J, Ding H, Yan L, Chen R, Wang B, Li Z, Liu H. Relationship between triglyceride glucose-body mass index and coronary artery calcium score in maintenance Hemodialysis patients. Front Med. 2024;11:1478090.

    Article  Google Scholar 

  14. Chang TI, Streja E, Soohoo M, Kim TW, Rhee CM, Kovesdy CP, Kashyap ML, Vaziri ND, Kalantar-Zadeh K, Moradi H. Association of serum triglyceride to HDL cholesterol ratio with All-Cause and cardiovascular mortality in incident Hemodialysis patients. Clin J Am Soc Nephrology: CJASN. 2017;12(4):591–602.

    Article  CAS  Google Scholar 

  15. Guerrero-Romero F, Simental-Mendia LE, Gonzalez-Ortiz M, Martinez-Abundis E, Ramos-Zavala MG, Hernandez-Gonzalez SO, Jacques-Camarena O, Rodriguez-Moran M. The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp. J Clin Endocrinol Metab. 2010;95(7):3347–51.

    Article  CAS  PubMed  Google Scholar 

  16. Er LK, Wu S, Chou HH, Hsu LA, Teng MS, Sun YC, Ko YL. Triglyceride Glucose-Body mass index is a simple and clinically useful surrogate marker for insulin resistance in nondiabetic individuals. PLoS ONE. 2016;11(3):e0149731.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Iwani NA, Jalaludin MY, Zin RM, Fuziah MZ, Hong JY, Abqariyah Y, Mokhtar AH, Wan Nazaimoon WM. Triglyceride to HDL-C ratio is associated with insulin resistance in overweight and obese children. Sci Rep. 2017;7:40055.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Bello-Chavolla OY, Almeda-Valdes P, Gomez-Velasco D, Viveros-Ruiz T, Cruz-Bautista I, Romo-Romo A, Sanchez-Lazaro D, Meza-Oviedo D, Vargas-Vazquez A, Campos OA, et al. METS-IR, a novel score to evaluate insulin sensitivity, is predictive of visceral adiposity and incident type 2 diabetes. Eur J Endocrinol. 2018;178(5):533–44.

    Article  CAS  PubMed  Google Scholar 

  19. Liu T, Fang Y, Liu S, Yu X, Zhang H, Liang M, Ding X. Limb ischemic preconditioning protects against contrast-induced acute kidney injury in rats via phosphorylation of GSK-3β. Free Radic Biol Med. 2015;81:170–82.

    Article  CAS  PubMed  Google Scholar 

  20. Onyango AN. Cellular Stresses and Stress Responses in the Pathogenesis of Insulin Resistance. Oxid Med Cell Longev. 2018;2018:4321714.

  21. Nishimura M, Tokoro T, Nishida M, Hashimoto T, Kobayashi H, Yamazaki S, Imai R, Okino K, Takahashi H, Ono T. Association of insulin resistance with de Novo coronary stenosis after percutaneous coronary artery intervention in Hemodialysis patients. Nephron Clin Pract. 2008;109(1):c9–17.

    Article  CAS  PubMed  Google Scholar 

  22. Xu H, Carrero JJ. Insulin resistance in chronic kidney disease. Nephrol (Carlton). 2017;22(Suppl 4):31–4.

    Article  CAS  Google Scholar 

  23. da Costa JA, Ikizler TA. Inflammation and insulin resistance as novel mechanisms of wasting in chronic Dialysis patients. Semin Dial. 2009;22(6):652–7.

    Article  PubMed  Google Scholar 

  24. D’Alessandris C, Lauro R, Presta I, Sesti G. C-reactive protein induces phosphorylation of insulin receptor substrate-1 on Ser307 and Ser 612 in L6 myocytes, thereby impairing the insulin signalling pathway that promotes glucose transport. Diabetologia. 2007;50(4):840–9.

    Article  PubMed  Google Scholar 

  25. Ahmed B, Sultana R, Greene MW. Adipose tissue and insulin resistance in obese. Biomed Pharmacother. 2021;137:111315.

    Article  CAS  PubMed  Google Scholar 

  26. Shangguan Q, Liu Q, Yang R, Zhang S, Sheng G, Kuang M, Zou Y. Predictive value of insulin resistance surrogates for the development of diabetes in individuals with baseline normoglycemia: findings from two independent cohort studies in China and Japan. Diabetol Metab Syndr. 2024;16(1):68.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Tao LC, Xu JN, Wang TT, Hua F, Li JJ. Triglyceride-glucose index as a marker in cardiovascular diseases: landscape and limitations. Cardiovasc Diabetol. 2022;21(1):68.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Zhang Z, Zhao L, Lu Y, Meng X, Zhou X. Association between non-insulin-based insulin resistance indices and cardiovascular events in patients undergoing percutaneous coronary intervention: a retrospective study. Cardiovasc Diabetol. 2023;22(1):161.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Després JP, Lamarche B, Mauriège P, Cantin B, Dagenais GR, Moorjani S, Lupien PJ. Hyperinsulinemia as an independent risk factor for ischemic heart disease. N Engl J Med. 1996;334(15):952–7.

    Article  PubMed  Google Scholar 

  30. Xu JW, Morita I, Ikeda K, Miki T, Yamori Y. C-reactive protein suppresses insulin signaling in endothelial cells: role of spleen tyrosine kinase. Mol Endocrinol. 2007;21(2):564–73.

    Article  CAS  PubMed  Google Scholar 

  31. Rajwani A, Cubbon RM, Wheatcroft SB. Cell-specific insulin resistance: implications for atherosclerosis. Diabetes Metab Res Rev. 2012;28(8):627–34.

    Article  CAS  PubMed  Google Scholar 

  32. Rajapakse NW, Chong AL, Zhang WZ, Kaye DM. Insulin-mediated activation of the L-arginine nitric oxide pathway in man, and its impairment in diabetes. PLoS ONE. 2013;8(5):e61840.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Wang Z, He H, Xie Y, Li J, Luo F, Sun Z, Zheng S, Yang F, Li X, Chen X, et al. Non-insulin-based insulin resistance indexes in predicting atrial fibrillation recurrence following ablation: a retrospective study. Cardiovasc Diabetol. 2024;23(1):87.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Deger SM, Hewlett JR, Gamboa J, Ellis CD, Hung AM, Siew ED, Mamnungu C, Sha F, Bian A, Stewart TG, et al. Insulin resistance is a significant determinant of sarcopenia in advanced kidney disease. Am J Physiol Endocrinol Metab. 2018;315(6):E1108–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Deger SM, Sundell MB, Siew ED, Egbert P, Ellis CD, Sha F, Ikizler TA, Hung AM. Insulin resistance and protein metabolism in chronic Hemodialysis patients. J Ren Nutr. 2013;23(3):e59–66.

    Article  CAS  PubMed  Google Scholar 

  36. Tsai MT, Ou SM, Chen HY, Tseng WC, Lee KH, Yang CY, Yang RB, Tarng DC. Relationship between Circulating Galectin-3, Systemic Inflammation, and Protein-Energy Wasting in Chronic Hemodialysis Patients. Nutrients. 2021;13(8).

  37. Siew ED, Pupim LB, Majchrzak KM, Shintani A, Flakoll PJ, Ikizler TA. Insulin resistance is associated with skeletal muscle protein breakdown in non-diabetic chronic Hemodialysis patients. Kidney Int. 2007;71(2):146–52.

    Article  CAS  PubMed  Google Scholar 

  38. Kharbanda RK, Peters M, Walton B, Kattenhorn M, Mullen M, Klein N, Vallance P, Deanfield J, MacAllister R. Ischemic preconditioning prevents endothelial injury and systemic neutrophil activation during ischemia-reperfusion in humans in vivo. Circulation. 2001;103(12):1624–30.

    Article  CAS  PubMed  Google Scholar 

  39. Valtchanova-Matchouganska A, Gondwe M, Nadar A. The role of C-reactive protein in ischemia/reperfusion injury and preconditioning in a rat model of myocardial infarction. Life Sci. 2004;75(8):901–10.

    Article  CAS  PubMed  Google Scholar 

  40. Pan T, Jia P, Chen N, Fang Y, Liang Y, Guo M, Ding X. Delayed remote ischemic preconditioning confersrenoprotection against septic acute kidney injury via Exosomal miR-21. Theranostics. 2019;9(2):405–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Murphy T, Walsh PM, Doran PP, Mulhall KJ. Transcriptional responses in the adaptation to ischaemia-reperfusion injury: a study of the effect of ischaemic preconditioning in total knee arthroplasty patients. J Translational Med. 2010;8:46.

    Article  Google Scholar 

  42. Liu T, Fang Y, Liu S, Yu X, Zhang H, Liang M, Ding X. Limb ischemic preconditioning protects against contrast-induced acute kidney injury in rats via phosphorylation of GSK-3beta. Free Radic Biol Med. 2015;81:170–82.

    Article  CAS  PubMed  Google Scholar 

  43. Plomgaard P, Bouzakri K, Krogh-Madsen R, Mittendorfer B, Zierath JR, Pedersen BK. Tumor necrosis factor-alpha induces skeletal muscle insulin resistance in healthy human subjects via Inhibition of Akt substrate 160 phosphorylation. Diabetes. 2005;54(10):2939–45.

    Article  CAS  PubMed  Google Scholar 

  44. Yang M, Qiu S, He Y, Li L, Wu T, Ding N, Li F, Zhao AZ, Yang G. Genetic ablation of C-reactive protein gene confers resistance to obesity and insulin resistance in rats. Diabetologia. 2021;64(5):1169–83.

    Article  CAS  PubMed  Google Scholar 

  45. D’Apolito M, Du X, Zong H, Catucci A, Maiuri L, Trivisano T, Pettoello-Mantovani M, Campanozzi A, Raia V, Pessin JE, et al. Urea-induced ROS generation causes insulin resistance in mice with chronic renal failure. J Clin Investig. 2010;120(1):203–13.

    Article  PubMed  Google Scholar 

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Acknowledgements

We thank all participants for their participation in our study. We also thank the supporting staff and study personnel, especially Dr. Juntian Xu for her assistance with recruiting patients.

Funding

This work was supported by Grant 81670627 from the National Natural Science Foundation of China, LGY2020035 from the High-level Health Personnel “Six One Project” Talent Project of Jiangsu Provence.

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Authors and Affiliations

  1. Department of Nephrology, The Second People’s Hospital of Changzhou, Third Affiliated Hospital of Nanjing Medical University, Changzhou, 213003, China

    Yu Zhang, Yushang Tang, Linfang Xu, Li Fang, Xiaoping Li, Wenbin Mao & Tongqiang Liu

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  1. Yu Zhang
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  2. Yushang Tang
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  3. Linfang Xu
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  4. Li Fang
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Contributions

YZ, YT and LX contributed conception and design of the study. LF, XL and WM organized the database. YZ performed the statistical analysis. YZ and YT wrote the first draft of the manuscript. LX and TL revised the manuscript.

Corresponding author

Correspondence to Tongqiang Liu.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the Ethics Committee of Changzhou Second People’s Hospital affiliated with Nanjing Medical University ([2023]KY213-01). All methods were performed in accordance with relevant guidelines and regulations. Patient information was kept confidential and no information that could lead to patient identification was included in the study. The signing of informed consent is exempted: according to the Measures for Ethical Review of Biomedical Research Involving Human Beings, research using human materials or data with identifiable information can no longer be found in the subjects, and the research project does not involve personal privacy or commercial interests. The requirement for informed consent is waived because the research is retrospective in nature. Clinical trial number: not applicable.

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Not applicable.

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The authors declare no competing interests.

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Zhang, Y., Tang, Y., Xu, L. et al. Effect of limb ischemic preconditioning on the indirect index of insulin resistance in maintenance hemodialysis patients. BMC Cardiovasc Disord 25, 238 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12872-025-04677-w

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Keywords

BMC Cardiovascular Disorders

ISSN: 1471-2261

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