The association between stress-induced hyperglycemia ratio and cardiovascular events as well as all-cause mortality in patients with chronic kidney disease and diabetic nephropathy

Abstract

The stress hyperglycemia ratio (SHR) is an emerging biomarker used to assess blood glucose levels under acute stress conditions and has been linked to the incidence of adverse clinical outcomes. However, the precise role of SHR in patients with diabetic kidney disease (DKD) and chronic kidney disease (CKD), particularly in relation to mortality, remains poorly understood. This study seeks to investigate the clinical value of SHR as a predictive tool for all-cause and cardiovascular mortality in these patient groups. This study analyzed data from the National Health and Nutrition Examination Survey (NHANES) spanning from 1999 to 2018, encompassing 3,507 individuals diagnosed with diabetic kidney disease (DKD) or chronic kidney disease (CKD). The primary endpoints included all-cause mortality and cardiovascular mortality, with mortality data obtained from the National Death Index (NDI) through December 31, 2019. Participants were categorized into quartiles based on the stress hyperglycemia ratio (SHR), and Cox proportional hazards regression models were employed to examine the association between SHR and mortality. Model 1 did not account for any covariates, Model 2 adjusted for age, sex, and race, while Model 3 additionally incorporated adjustments for educational attainment, marital status, body mass index, smoking behavior, hypertension, hyperlipidemia, and cardiovascular disease. The study comprised 3,507 patients with a mean age of 60.7 years, of whom 56% were female. The overall incidence of all-cause mortality was 38,000 per 100,000 person-years, while cardiovascular mortality was 11,405 per 100,000 person-years. Kaplan–Meier survival analysis revealed that the second quartile of the stress hyperglycemia ratio (SHR) (Q2) exhibited the lowest all-cause mortality (log-rank P = 0.003). Cox regression analysis indicated that the hazard ratio (HR) for all-cause mortality in Q2 was 0.76 (95% CI: 0.63, 0.92), whereas the HR for Q4 was 1.26 (95% CI: 1.04, 1.52). Restricted cubic spline (RCS) analysis revealed a J-shaped association between SHR and all-cause mortality, as well as a U-shaped association with cardiovascular mortality. The minimum risk values for SHR were 0.923 for all-cause mortality and 1.026 for cardiovascular mortality. In patients with diabetic kidney disease (DKD) and chronic kidney disease (CKD), SHR demonstrated a J-shaped relationship with all-cause mortality and a U-shaped relationship with cardiovascular mortality. Subgroup analyses indicated that the effect of spontaneous hypertension on mortality was consistent across all subgroups. This study highlights a significant association between the stress hyperglycemia ratio (SHR) and both all-cause and cardiovascular mortality in patients with diabetic kidney disease (DKD) or chronic kidney disease (CKD). SHR may serve as a critical biomarker for prognostic assessment in these populations, enabling clinicians to identify high-risk patients and tailor personalized treatment strategies that enhance patient quality of life and mitigate mortality risk.

Graphical abstract

Introduction

Acute hyperglycemia is characterized by a substantial increase in blood glucose levels in critical conditions, such as acute myocardial infarction (AMI), and is frequently associated with inadequate diabetes management, potentially aggravated by inflammation and hormonal disruptions induced by severe illness. Previous studies have demonstrated that short-term hyperglycemia is correlated with unfavorable outcomes in AMI patients[1]. However, admission blood glucose levels may not fully reflect the extent of acute hyperglycemia, as they can be influenced by the patient's long-term glycemic control. The stress hyperglycemia ratio (SHR), an index derived from the ratio of admission blood glucose (BG) to glycated hemoglobin (HbA1c), captures the acute change in blood glucose relative to the chronic glycemic state. Elevated SHR has been identified as a risk factor for cardiovascular diseases in both diabetic and non-diabetic individuals[1,2,3]. Research suggests that SHR can serve as a predictor for major adverse cardiovascular events (MACE) and mortality in AMI patients[1, 4,5,6,7,8]. A recent meta-analysis encompassing 26 cohorts revealed that a higher SHR in AMI patients is significantly associated with an increased risk of major adverse cardiovascular and cerebrovascular events (MACCE). In contrast, patients with lower SHR demonstrated reduced long-term all-cause mortality and in-hospital mortality. Nevertheless, the relationship between SHR, cardiovascular events, and all-cause mortality in individuals with diabetic kidney disease (DKD) or chronic kidney disease (CKD) remains insufficiently explored.

Therefore, the objective of this study is to examine the association between the stress hyperglycemia ratio (SHR) and both all-cause and cardiovascular mortality in patients with diabetic kidney disease (DKD) and chronic kidney disease (CKD). The study cohort comprises a nationally representative sample of patients with diabetes or prediabetes from the United States.

Methods

Study population

The data utilized in this study were sourced from the National Health and Nutrition Examination Survey (NHANES), which is managed by the Centers for Disease Control and Prevention (CDC) and the National Center for Health Statistics (NCHS). This project followed the STROBE guidelines for observational studies and received approval from the NCHS Ethics Review Board, with written informed consent obtained from all participants. Data spanning from 1999 to 2018, encompassing 10 survey cycles, were downloaded from the official NHANES website (https://www.cdc.gov/nchs/nhanes/index). The dataset includes demographic information, physical examination results, laboratory test outcomes, and survey responses.

In this study, a total of 116,876 participants from the NHANES 1999–2018 cycles were initially included. After excluding participants with incomplete baseline demographic data (such as age, sex, and race), 68,897 participants remained. Further exclusions were made for those with missing lifestyle data (including marital status, education level, BMI, smoking status, and tobacco use), leaving 45,428 participants. Additional exclusions for missing disease-related data (such as diabetes, chronic kidney disease, dyslipidemia, and cardiovascular disease) resulted in 45,018 participants. After removing individuals with missing data on key independent variables (including fasting glucose and HbA1c), 21,696 participants remained. Non-CKD participants were then excluded, leaving 3,853 individuals. Finally, after excluding participants with missing survival status data, 3,507 individuals were included in the analysis for all-cause mortality, which included those with chronic kidney disease or diabetic kidney disease. Among these, 2,568 participants had complete data for cardiovascular mortality (see Fig. 1).

Fig. 1

Flowchart of Sample Selection from NHANES 1999–2018

Fig. 2

Kaplan–Meier Curves: (A) All-cause mortality in chronic kidney disease patients stratified by quartiles of stress hyperglycemia ratio. (B) Cardiovascular mortality in chronic kidney disease patients stratified by quartiles of stress hyperglycemia ratio

Exposure and outcome variables

In accordance with the diagnostic criteria set forth by the American Diabetes Association (ADA) [9], diabetes is diagnosed based on the presence of any one of the following conditions: (1) Glycated hemoglobin (HbA1c) ≥ 6.5%; (2) A 75 g oral glucose tolerance test (OGTT) yielding a 2-h serum glucose concentration exceeding 200 mg/dL; (3) Fasting blood glucose (FBG) ≥ 126 mg/dL; (4) Self-reported diagnosis of diabetes; (5) Self-reported use of insulin or other antidiabetic medications.

According to the KDIGO 2021 guidelines, diabetic kidney disease (DKD) is defined by a history of diabetes coupled with either a urine albumin-to-creatinine ratio (UACR) ≥ 30 mg/g or an estimated glomerular filtration rate (eGFR) below 60 mL/min/1.73 m² [10].The NHANES does not directly measure glomerular filtration rate (GFR); rather, it employs the kinetic Jaffe method for serum creatinine measurement, with results calibrated to the Cleveland Clinic's standardized creatinine values (Ohio) [11]. Estimated GFR (eGFR) is calculated using both the MDRD and the recently developed CKD-EPI equations.The formula for estimating eGFR is as follows:eGFR = 175 × (standardized creatinine) − 1.154 × (age) − 0.203 × 1.212(if Black) × 0.742 (if Female)where eGFR is expressed in mL/min/1.73 m² and serum creatinine in mg/dL [12]. eGFR values exceeding 200 mL/min/1.73 m² are capped. The methods for the collection, analysis, and reporting of albuminuria have been described previously [13]. Persistent albuminuria is defined as a urine albumin-to-creatinine ratio (UACR) ≥ 30 mg/g. Chronic kidney disease (CKD) is defined as either persistent albuminuria or an eGFR < 60 mL/min/1.73 m², while diabetic kidney disease refers to the concurrent presence of both diabetes and CKD.

Covariates

The covariates incorporated into our analysis include age, sex, race/ethnicity (e.g., Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, non-Hispanic Asian, and other racial/ethnic groups), body mass index (BMI), marital status, educational attainment, smoking status, serum cotinine levels, hypertension, hyperlipidemia, and cardiovascular disease.The covariates are specifically defined as follows:1.Marital Status: Categorized into divorced, married, and unmarried. 2.Body Mass Index (BMI): Classified as normal (< 25 kg/m²), overweight (≥ 25 kg/m² and < 30 kg/m²), and obese (≥ 30 kg/m²). 3.Smoking Status: Divided into current smokers (individuals who have smoked more than 100 cigarettes and continue to smoke), former smokers (individuals who have smoked more than 100 cigarettes but have quit), and never smokers (individuals who have smoked fewer than 100 cigarettes). 4.Hypertension: Defined according to the 2017 American College of Cardiology/American Heart Association guidelines as systolic blood pressure ≥ 130 mmHg or diastolic blood pressure ≥ 80 mmHg, self-reported diagnosis, or current use of antihypertensive medications. 5.Hyperlipidemia: Defined according to the NCEP ATP III guidelines as total cholesterol ≥ 200 mg/dL, triglycerides ≥ 150 mg/dL, male HDL cholesterol (HDL-C) < 40 mg/dL, female HDL-C < 50 mg/dL, or low-density lipoprotein cholesterol (LDL-C) ≥ 130 mg/dL. 6.Cardiovascular Disease: Defined based on self-reported physician diagnosis of congestive heart failure, coronary artery disease, angina, myocardial infarction, or stroke. 7.Education Level: Categorized as follows: Primary education: Less than 9th to 11th grade. Secondary education: High school or equivalent. Higher education: College degree or higher, or some college/associate’s degree.

Assessment of SHR index

The Stress Hyperglycemia Ratio (SHR) serves as an indicator to evaluate fluctuations in blood glucose levels in patients undergoing acute or subacute stress conditions. It fundamentally reflects the degree and control of blood glucose variability in individuals experiencing stress events during hospitalization. The calculation of SHR incorporates not only the patient's baseline blood glucose levels but also highlights the influence of the stress response on glucose regulation.

The SHR is calculated using the following formula: FPG / (1.59 * HbA1c—2.59), where FPG refers to fasting plasma glucose, typically measured in mg/dL, and HbA1c represents glycated hemoglobin, expressed as a percentage (%). The SHR index is categorized into four groups (Q1, Q2, Q3, and Q4) using the quartile method, with the Q1 group serving as the reference for comparisons with the other groups. This stratification facilitates a more detailed evaluation of the relationship between the SHR index and other variables or health outcomes. Since SHR is derived from the patient’s FPG and HbA1c, it is applicable to all hospitalized patients requiring monitoring of blood glucose fluctuations and management.The NHANES dataset does not offer specific information regarding whether the SHR is influenced by conditions such as gallbladder surgery, hip fractures, or psychiatric hospitalizations.

Mortality assessment

In this study, mortality outcomes were derived by linking participants to the National Death Index (NDI) as of December 31, 2019, through propensity score matching techniques. This method enabled the calculation of all-cause mortality rates for each participant. Given the established associations between chronic kidney disease (CKD), diabetic kidney disease (DKD), and an increased risk of cardiovascular mortality, the study specifically aimed to evaluate the predictive capacity of the SHR index for cardiovascular mortality in individuals with these two conditions. Cardiovascular mortality was defined according to the International Classification of Diseases, 10th Edition (ICD-10) codes I00-I09, I11, I13, I120-I51, encompassing a range of cardiovascular diseases that may lead to death.

Statistical analysis

In this study, continuous variables were presented as means ± standard deviation (SD), while categorical variables were reported as counts (N) and percentages (%). Differences between groups with varying SHR levels were evaluated using weighted t-tests for continuous variables and weighted chi-square tests for categorical variables.

The differences among the four SHR quartiles were evaluated using Kruskal–Wallis tests for continuous variables and weighted chi-square tests for categorical variables. 1. Kaplan–Meier survival curves and log-rank tests were employed to compare survival rates across different SHR levels in patients with chronic kidney disease (CKD). 2. Cox proportional hazards regression models were utilized to assess mortality risk across different SHR groups. Hazard ratios (HR) and 95% confidence intervals (95% CI) were reported to quantify the changes in mortality risk. The analysis was conducted in three models: 3. Model 1 was adjusted for age, gender, and race/ethnicity; 4. Model 2 further adjusted for body mass index (BMI), marital status, education level, serum cotinine, and smoking status; 5. Model 3 included additional adjustments for hyperlipidemia, hypertension, and cardiovascular disease.

Additionally, Restricted Cubic Splines (RCS) were employed to model SHR as a continuous variable, allowing for the exploration of both linear and nonlinear relationships between SHR and mortality risk in patients with chronic kidney disease (CKD) and diabetic kidney disease (DKD). In cases where a nonlinear relationship was identified, the threshold and potential inflection points were determined. For both sides of the inflection point, a two-piece Cox regression model was applied to examine the association between SHR and mortality risk. Based on the two-piece Cox model, subgroup analyses were performed to assess the effects of SHR across different age groups (e.g., < 60 years vs. ≥ 60 years) and other covariates, with a focus on exploring potential interactions. Statistical significance was defined as a p-value < 0.05, and all tests were two-sided.

Results

Baseline characteristics

A total of 3,507 patients with diabetic kidney disease (DKD) or chronic kidney disease (CKD) were included in this study. The mean age of the participants was 60.7 years, with 56% of the patients being female. The average SHR values across the four quartiles (Q1, Q2, Q3, and Q4) were 0.76, 0.88, 0.96, and 1.16, respectively. Compared to patients in the Q4 group (the highest SHR quartile), those in the Q1 group were more likely to be older, female, non-Hispanic Black, and never smokers. Specifically, in the Q1 group, 20% of patients had a history of smoking, while 51% had never smoked. Regarding laboratory data, significant differences were observed across the SHR quartiles. Patients in the Q4 group had lower serum cotinine levels and blood pressure compared to those in the other groups. These findings highlight the relationship between SHR levels and various baseline characteristics, including age, gender, smoking status, and clinical markers.

All-cause mortality and cardiovascular mortality clinical outcomes

In this study, a total of 1,339 cases of all-cause mortality were recorded, resulting in an all-cause mortality rate of 38,000 per 100,000 person-years. Additionally, 400 cases of cardiovascular mortality were documented, yielding a cardiovascular mortality rate of 11,405 per 100,000 person-years.Kaplan–Meier (K-M) survival analysis revealed significant differences in all-cause mortality across the four SHR quartiles during the follow-up period. The lowest all-cause mortality was observed in the second quartile (Q2) group, with log-rank P values < 0.05 for all comparisons. The detailed results of the K-M survival analysis are shown in Fig. 2.Table 1 presents the results of three Cox regression models evaluating the association between SHR and all-cause mortality. These models assess how variations in SHR levels impact mortality risk, controlling for various covariates across the different models.

Fig. 3

Restricted Cubic Splines (RCS) Analysis: The association between the stress hyperglycemia ratio (SHR) and all-cause (A) and cardiovascular mortality (B) in patients with chronic kidney disease (CKD). In patients with CKD and diabetes, the relationship between SHR and all-cause mortality (C) and cardiovascular mortality (D) was adjusted for age, sex, race, education level, marital status, serum cotinine, body mass index (BMI), smoking status, hypertension, hyperlipidemia, and cardiovascular disease. The solid line and shaded region represent the estimated values and their corresponding 95% confidence intervals (CI), respectively. SHR, stress hyperglycemia ratio; BMI, body mass index; CI, confidence interval; HR, hazard ratio

Table 1 Participant characteristics by SHR level. (NHANES 1999–2018, N = 3,507)

Table 2 Threshold effect analysis of SHR on all-cause mortality and cardiovascular disease mortality in CKD patients

Table 3 Threshold effect analysis of the SHR on all-cause and cardiovascular mortality in patients with CKD

In the unadjusted model (without covariate adjustment), the hazard ratios (HR) and 95% confidence intervals (CI) for the quartiles were as follows: 1.00 (reference group), 0.75 (0.62, 0.91), 0.86 (0.70, 1.07), and 1.16 (0.94, 1.44). In Model 2, after adjusting for age, gender, race, education, marital status, serum cotinine, body mass index (BMI), and smoking status, the HRs and 95% CIs were: 1.00 (reference group), 0.76 (0.63, 0.92), 0.95 (0.79, 1.15), and 1.26 (1.04, 1.52) (all P values < 0.05). In Model 3, following further adjustment for hypertension, hyperlipidemia, and cardiovascular disease, the HRs and 95% CIs remained: 1.00 (reference group), 0.76 (0.63, 0.92), 0.95 (0.79, 1.15), and 1.26 (1.04, 1.52).Additionally, Table 2 presents the results of the Cox regression models for cardiovascular mortality. In the unadjusted model, the HRs and 95% CIs were: 1.00 (reference group), 0.99 (0.71, 1.36), 0.74 (0.47, 1.19), and 1.29 (0.85, 1.94). In Model 2, after adjusting for age, gender, race, education, marital status, serum cotinine, BMI, and smoking status, the HRs and 95% CIs were: 1.00 (reference group), 1.09 (0.77, 1.54), 0.98 (0.62, 1.54), and 1.38 (0.88, 2.16). In Model 3, after further adjustment for hypertension, hyperlipidemia, and cardiovascular disease, the HRs and 95% CIs were: 1.00 (reference group), 1.08 (0.76, 1.52), 1.01 (0.64, 1.59), and 1.34 (0.85, 2.10).

The non-linear relationship between SHR and mortality rate

This study utilized a Restricted Cubic Spline (RCS) model to explore the non-linear relationship between SHR levels and mortality in patients with diabetic kidney disease (DKD) or chronic kidney disease (CKD). The RCS analysis revealed a J-shaped association between SHR and all-cause mortality, even after adjusting for potential confounders, including age, gender, race, education level, marital status, serum cotinine, body mass index (BMI), smoking status, hypertension, hyperlipidemia, and cardiovascular disease (non-linear P < 0.05; see Fig. 3A and C). Specifically, in the CKD cohort, hazard ratios (HR) exhibited marked changes when SHR exceeded 0.923, while in the DKD cohort, HR changes were significant when SHR surpassed 0.971. Additionally, SHR demonstrated a U-shaped relationship with cardiovascular mortality (non-linear P < 0.05); for CKD, HR changes were most pronounced at an SHR of 1.026, whereas in DKD, the threshold was 1.059, with substantial HR fluctuations observed on both sides of these values (see Fig. 3B and D).

To further explore the relationship between SHR and mortality, we employed a piecewise Cox proportional hazards regression model. In the CKD cohort, using the RCS-derived inflection points (0.923 and 1.026) as thresholds, and adjusting for potential confounders—including age, gender, race, education level, marital status, serum cotinine, body mass index (BMI), smoking status, hypertension, hyperlipidemia, and cardiovascular disease—the risk of all-cause mortality and cardiovascular mortality was lowest at SHR values approaching these inflection points. Specifically, the risk of all-cause mortality decreased by 92% (HR 0.08, 95% CI: 0.03–0.23), while the risk of cardiovascular mortality dropped by 94% (HR 0.16, 95% CI: 0.07–0.41). As SHR continued to rise beyond these points, the risks for both all-cause mortality and cardiovascular mortality increased sharply, with HR values of 2.10 (95% CI: 1.39, 3.18) and 1.94 (95% CI: 1.23, 3.05), respectively (Table 3). Furthermore, the analysis of the SHR-mortality relationship in patients with diabetic nephropathy yielded results consistent with those observed in the CKD cohort, with the inflection points serving as critical thresholds for risk transition (Table 4).

Table 4 Threshold effect analysis of the SHR on all-cause and cardiovascular mortality in patients with CKD combined with diabetes

Table 5 Subgroup analyses of the association between the SHR and All-cause mortality

Subgroup analysis

Subgroup analysis was conducted to assess the relationship between spontaneous hypertension (SHR) and mortality across different populations, stratified by age (< 60 years, ≥ 60 years), gender (female, male), race (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, other races), body mass index (underweight, normal weight, overweight, obesity), and diabetes status (diabetic nephropathy, chronic kidney disease).

As demonstrated in Tables 5 and 6, the association between spontaneous hypertension and mortality was consistent across patients with diabetic nephropathy and chronic kidney disease. Furthermore, no significant interactions were identified between spontaneous hypertension and the stratified variables.

Table 6 Subgroup analyses of the association between the SHR and Cardiovascular mortality

Discussion

Chronic kidney disease (CKD) and diabetic kidney disease (DKD) are prevalent, chronic conditions globally, often accompanied by the onset of cardiovascular diseases. The stress hyperglycemia ratio (SHR), a marker reflecting blood glucose variability and the body's stress response, has garnered increasing attention in recent years. This study is the first to systematically examine the association between SHR and both all-cause and cardiovascular mortality in patients with DKD and CKD. Our findings reveal the following: (1) After adjusting for potential confounding factors, SHR is independently associated with both all-cause and cardiovascular mortality; (2) A J-shaped relationship exists between SHR and all-cause mortality. Specifically, in CKD patients, the risk of all-cause mortality significantly increases when SHR exceeds 0.923, while in DKD patients, a significant increase in mortality risk is observed when SHR surpasses 0.971; (3) A U-shaped relationship is evident between SHR and cardiovascular mortality. In CKD patients, cardiovascular mortality significantly rises when SHR exceeds 1.026, and in DKD patients, the trend toward increased cardiovascular mortality becomes more pronounced when SHR surpasses 1.059.

The stress hyperglycemia ratio (SHR), a straightforward and quantifiable method for evaluating stress-induced hyperglycemia, has been shown to correlate with a range of clinical outcomes. Early studies have established that SHR is significantly associated with adverse prognostic factors, including pulmonary infections during hospitalization [14], the severity of coronary artery disease [15], and thrombus burden [16]. Furthermore, SHR has been identified as an effective predictor of all-cause mortality in patients with acute myocardial infarction and acute decompensated heart failure. These findings underscore the substantial prognostic value of SHR in clinical practice, positioning it as an early warning tool for identifying high-risk patients.

Currently, studies investigating the relationship between SHR and cardiovascular events in patients with diabetic kidney disease (DKD) and chronic kidney disease (CKD) are limited. Our study identified a J-shaped relationship between SHR and all-cause mortality, a finding that aligns with several previous studies in the existing literature. Specifically, lower SHR values may reflect a diminished capacity of the body to manage stress, with undiagnosed underlying health issues potentially contributing to an increased risk of all-cause mortality. In contrast, higher SHR values may indicate prolonged hyperglycemia, thereby elevating the risk of mortality. Moderate SHR values, however, may suggest better stress adaptation, correlating with a reduced risk of death. Research by Gregory et al. has shown a U-shaped relationship between different levels of glycemic control and all-cause mortality, particularly when HbA1c levels fall below 6.0% or exceed 9.0%, both of which significantly elevate mortality risk. This highlights the critical importance of maintaining glycemic control within an optimal range to prevent adverse outcomes [17].

In terms of cardiovascular mortality, our study demonstrated a U-shaped relationship between SHR and cardiovascular mortality. Specifically, mild stress-induced hyperglycemia typically reflects the body's capacity to effectively manage short-term stress, leading to a lower rate of cardiovascular mortality. However, as SHR continues to rise, particularly in patients with concomitant cardiovascular disease, cardiovascular mortality increases significantly. This may be attributed to vascular damage and the cardiovascular burden induced by persistent hyperglycemia. Our findings indicate that in CKD patients, cardiovascular mortality significantly rises when SHR exceeds 1.026, while in DKD patients, the trend of increasing cardiovascular mortality becomes particularly pronounced when SHR surpasses 1.059.

After adjusting for potential confounding factors, we found that when SHR exceeds 0.971, the risk of all-cause mortality significantly increases. This result is consistent with the study by Yang et al., which identified a U-shaped relationship between SHR and major cardiovascular adverse events (MACE) in patients with acute coronary syndrome (ACS). Similarly, research by Roberts et al. also suggests a J-shaped relationship between SHR and the occurrence of critical illnesses, closely aligning with our findings. Furthermore, the meta-analysis by Karakasis et al. demonstrated a significant association between SHR and major adverse cardiovascular and cerebrovascular events (MACCE), as well as mortality, further validating the prognostic value of SHR across various patient populations [18].

The impact of SHR on clinical prognosis may involve multiple biological mechanisms. Higher SHR values may contribute to adverse outcomes through mechanisms such as insulin resistance, oxidative stress, and inflammatory responses. Specifically, sustained high SHR values can impair the body’s ability to respond to insulin, which is closely linked to the increased release of stress hormones like catecholamines, cortisol, and glucagon [19,20,21]. These hormones not only raise blood glucose levels but also exacerbate oxidative stress, promote atherosclerosis, and cause endothelial injury, all of which accelerate the onset of cardiovascular diseases [22, 23]. Moreover, elevated SHR may intensify the systemic inflammatory response by activating the renin–angiotensin–aldosterone system (RAAS) and triggering the release of pro-inflammatory factors, which exacerbates damage to both the kidney and cardiovascular systems [24]. Research has also shown that fluctuations in SHR may worsen tubular injury, further contributing to the deterioration of renal function [25].

Additionally, high SHR values may increase the risk of thrombosis by impacting the fibrinolytic system. In a hyperglycemic state, the level of the fibrinolysis inhibitor PAI-1 is typically elevated, which reduces fibrinolytic capacity and consequently increases the risk of cardiovascular events [26, 27]. Therefore, maintaining precise blood glucose control is essential for reducing cardiovascular mortality. Previous studies have demonstrated that chronic hyperglycemia contributes to adverse clinical outcomes through mechanisms such as oxidative stress, inflammation, and insulin resistance, while hypoglycemia is also significantly associated with poor outcomes [28,29,30,31]. This further emphasizes the importance of focusing on SHR and blood glucose fluctuations. In patients with diabetic kidney disease and chronic kidney disease, insulin clearance is impaired due to kidney damage, making them more susceptible to hypoglycemia. This phenomenon may exacerbate the condition and elevate mortality risk [32].

The limitations of this study include several factors. Firstly, despite our efforts to adjust for potential confounding factors, the observational nature of the study means that there may still be unaccounted biases that were not fully controlled. Secondly, the study data were derived from a single population in the United States, which may limit the generalizability of the findings to other populations. Lastly, the sample size in the subgroup analysis was relatively small, which could introduce some bias in the interpretation of the results.

Conclusion

This study indicates that the SHR index is a significant predictor of both all-cause and cardiovascular mortality risk in patients with diabetic nephropathy or chronic kidney disease. SHR demonstrates a J-shaped relationship with all-cause mortality and a U-shaped relationship with cardiovascular mortality, with inflection points at 0.923 and 1.026, respectively, marking thresholds for poor prognosis. In these patient cohorts, SHR serves as a critical risk transition point for mortality at these inflection values. To further evaluate the predictive capacity of SHR and explore the mechanisms underlying the observed J-shaped and U-shaped associations, large-scale, multi-center prospective studies are warranted.