The Prognostic Value of Stress Hyperglycemia Ratio in Hemodialysis Patients: A U-Shaped Association with Mortality

The Prognostic Value of Stress Hyperglycemia Ratio in Hemodialysis Patients: A U-Shaped Association with Mortality | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Prognostic Value of Stress Hyperglycemia Ratio in Hemodialysis Patients: A U-Shaped Association with Mortality Hongyu Zhou, Xiaodong Tang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7204612/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives: To evaluate the prognostic value of the stress hyperglycemia ratio (SHR) in hemodialysis (HD) patients, focusing on its association with all-cause and cardiovascular mortality. Methods: We conducted a retrospective, longitudinal cohort study involving 1,306 HD patients from July 2017 to July 2022. The primary outcome was all-cause mortality, while cardiovascular disease (CVD) mortality was assessed as a secondary outcome. Cox proportional hazards models and Kaplan–Meier survival curves were employed to evaluate the association between SHR and mortality. Additionally, restricted cubic spline (RCS) analyses were performed to explore the non-linear relationship, and an iterative algorithm was used to identify inflection points. Results: During a median follow-up of 62 months, 464 all-cause deaths (35.5%) and 192 CVD-related deaths were observed. A U-shaped association was identified between SHR and both all-cause and CVD mortality, with inflection points at 0.804 and 0.817, respectively. For all-cause mortality, the adjusted hazard ratios (HRs) were 0.63 (95% CI: 0.04–0.78) for SHR < 0.86 and 2.73 (95% CI: 1.78–4.18) for SHR ≥ 0.86. For CVD mortality, the corresponding HRs were 0.57 (95% CI: 0.04–0.87) and 2.70 (95% CI: 1.76–4.14). Subgroup analysis revealed a significant interaction between SHR and body mass index (BMI) in relation to cardiovascular mortality. Conclusions: A U-shaped association exists between SHR and both all-cause and cardiovascular mortality in HD patients. These findings suggest that SHR may serve as a useful prognostic biomarker for risk stratification and may inform individualized glycemic management strategies in the HD population. stress hyperglycemia ratio hemodialysis cardiovascular disease mortality all-cause mortality Figures Figure 1 Figure 2 Figure 3 Introduction In patients with end-stage kidney disease (ESKD), maintenance hemodialysis (HD) is essential for sustaining life[ 1 , 2 ]. However, improving long-term outcomes and mitigating systemic complications remain critical clinical challenges. Despite advances in dialysis technology and supportive care, this population continues to experience disproportionately high mortality rates, largely attributed to cardiovascular disease (CVD)[ 2 – 4 ]. Although routine clinical monitoring is standard practice, conventional risk stratification tools often fail to capture the multifactorial and dynamic nature of clinical risk in this complex population[ 5 ]. This highlights the urgent need for novel, accessible biomarkers that can reflect systemic stress and reliably identify individuals at elevated risk of adverse outcomes. The stress hyperglycemia ratio (SHR), characterized by a transient and significant elevation in blood glucose (BG) levels during acute physiological or pathological stress, is a well-documented marker of poor prognosis in a variety of clinical conditions[ 6 , 7 ]. This metabolic response involves a complex interplay between neuroendocrine activation, immune modulation, and altered insulin sensitivity[ 8 , 9 ]. To better quantify this phenomenon, the SHR was introduced as a standardized index, calculated by dividing the admission BG by the estimated average glucose derived from glycated hemoglobin (HbA1c)[ 10 ]. By accounting for chronic glycemic status, SHR provides a more individualized and clinically relevant measure of relative hyperglycemia during stress[ 11 ]. Accumulating evidence suggests that SHR is a robust prognostic indicator across a range of diseases. Elevated SHR has been associated with increased morbidity and mortality in patients with hospital-acquired pneumonia, acute coronary syndromes, thrombotic events, and ischemic stroke, where it correlates with the risk of hemorrhagic transformation[ 10 – 14 ]. Moreover, SHR has shown predictive value for all-cause mortality in patients with acute myocardial infarction and heart failure[ 15 , 16 ]. In the field of nephrology, recent studies have demonstrated the utility of SHR in predicting outcomes in patients with acute kidney injury, diabetic kidney disease, and progression to ESKD[ 17 , 18 ]. However, the prognostic value of SHR in the chronic hemodialysis population remains poorly defined. This study aims to investigate the prognostic significance of SHR in a large cohort of patients receiving maintenance HD, focusing on its association with all-cause and cardiovascular mortality. We hypothesize that higher SHR levels are independently associated with increased mortality risk and that SHR may serve as a simple, yet powerful tool for risk stratification in this high-risk population. Materials and methods Study Design and Population This retrospective cohort study included patients with HD who were admitted to blood purification center of Jiangyin People’s Hospital between July 2012 and June 2022. The study adhered to all relevant tenets of the Declaration of Helsinki and was approved by the Medical Ethics Committee of the Jiangyin People’s Hospital (reference number: 20240252). Patients whose research has been approved by a medical ethics committee agree to the exemption. The clinical data of all patients were derived entirely from the medical records of the hospital. The inclusion criteria for this study were as follows: (1) aged ≥ 18 years; (2) received regular hemodialysis treatment less than 3 months; (3) availability of both admission blood glucose (BG) level measured within 24 hours of hospital admission and glycated hemoglobin (HbA1c) data within 3 months prior to or during hospitalization; (4) hospitalization due to an acute medical condition (e.g., infection, cardiovascular event, or other acute illness); (5) complete follow-up data for mortality, including all-cause, infection-related, and cardiovascular-related deaths. Exclusion criteria were: (1) Patients on HD for less than 3 months; (2) lack of either admission BG or HbA1c data; (3) systemic infection, cancer, recent surgery or trauma, gout, or hospitalization for cardiovascular events within the last six months prior to enrollment; (4) metabolic encephalopathy, mental or emotional disorders, epilepsy, or dementia; (5) diagnosis of diabetic ketoacidosis or hyperosmolar hyperglycemic state at admission; (6) discharged or died within 24 hours of admission; (7) missing outcome or follow-up information. All patients were followed up until February 2024. Finally, a total of 1,306 patients with MHD were included in this study (Fig. 1 ). Definition of SHR The SHR was used as the primary exposure variable to quantify BG fluctuations under acute or subacute stress conditions. SHR reflects both the degree of glycemic variation experienced during hospitalization and the body's capacity to regulate these fluctuations. It was calculated using the formula: SHR = FPG / (1.59 × HbA1c – 2.59), where FPG represents fasting plasma glucose (mg/dL) and HbA1c indicates glycated hemoglobin (%), as previously described [ 17 ]. SHR was analyzed both as a continuous variable and categorically by dividing participants into quartiles (Q1–Q4) based on SHR values. Q1 served as the reference group for comparisons. This stratified approach enabled a more nuanced assessment of the relationship between SHR and clinical outcomes. Assessment of the covariates Comprehensive baseline demographic and clinical data were collected from all participants. This included: (i) sociodemographic variables such as age, sex, body mass index (BMI), education level, marital status, duration of dialysis, primary cause of kidney disease, smoking habits, and alcohol consumption; (ii) comorbidities, including albuminuria, hypertension, hyperlipidemia, diabetes mellitus, hypotension, and cardiovascular disease; and (iii) medication usage, specifically antihypertensive, antidiabetic, and lipid-lowering therapies. The estimated glomerular filtration rate (eGFR) was calculated using the CKD-EPI equation. Assessment of outcomes The primary outcome of this study was all-cause mortality, while the secondary outcome focused on CVD-specific mortality. CVD-related deaths were defined as those resulting from heart failure, pulmonary edema, ischemic heart disease, arrhythmia, valvular heart disease, cerebral infarction, cerebral hemorrhage, or subarachnoid hemorrhage. All clinical events were independently adjudicated by a panel of experts who were blinded to the study objectives and baseline SHR values. A team of clinical specialists conducted a thorough annual review of hospital discharge records and confirmed outcomes using hospital documentation, death certificates, and other original source materials. Participants were followed from the date of their baseline assessment—conducted three months after the initiation of maintenance hemodialysis—until death or the end of the study period in February 2024. Statistical Analysis Continuous variables were presented as means ± standard deviations (SD), while categorical variables were expressed as frequencies and percentages. Differences across SHR quartiles were assessed using t-tests or Kruskal–Wallis tests for continuous variables and chi-square tests for categorical variables. Kaplan–Meier survival curves were constructed to visualize cumulative incidence of outcomes, with comparisons evaluated via the log-rank test. The association between SHR and both all-cause and cardiovascular mortality was examined using Cox proportional hazards models, reporting hazard ratios (HRs) and 95% confidence intervals (CIs). Three models were developed: Model 1 was unadjusted; Model 2 adjusted for demographic and lifestyle factors (age, sex, marital status, education level, smoking and alcohol use, dialysis duration, and primary kidney disease); and Model 3 further adjusted for clinical comorbidities (hypertension, hyperlipidemia, diabetes mellitus, hypotension, cardiovascular disease), along with eGFR and albuminuria. Restricted cubic spline (RCS) analysis was used to explore nonlinear associations between SHR and mortality outcomes. Where nonlinearity was detected, inflection points were identified, and piecewise Cox regression was applied to assess risk on either side of the threshold. Subgroup analyses were conducted to evaluate effect modification and potential interactions across prespecified strata. A two-sided p-value < 0.05 was considered statistically significant. All analyses were conducted using standard statistical software packages. Results Baseline characteristics of the study population A total of 1,306 patients with HD were included in this study. The mean age of the participants was 60.5 years, with 52% of the patients being female. The average SHR values across the four quartiles (Q1, Q2, Q3, and Q4) were 0.76, 0.93, 1.05, and 1.30, respectively. Compared to patients in the Q4 group (the highest SHR quartile), those in the Q1 group were more likely to be older, obesity, and married. Diabetic nephropathy was more prevalent in the lowest SHR quartile, while glomerulonephritis and other etiologies were more common in higher quartiles ( P < 0.001). Higher SHR was also linked to increased prevalence of hypertension and diabetes (both p < 0.001), reduced use of antihypertensive and antidiabetic medications ( P < 0.001), and higher eGFR and albuminuria levels (both P < 0.001). These findings suggest that SHR reflects important clinical and metabolic differences in HD patients and may serve as a meaningful marker of disease burden (Table 1 ). Table 1 Baseline characteristics of the study population by SHR level in individuals with hemodialysis. 1 Characteristic Overall (n = 1,306) Q1(≤ 0.871) (n = 327) Q2(> 0.871, ≤ 0.988) (n = 326) Q3(> 0.988, ≤ 1.131) (n = 330) Q4 (> 1.131) (n = 323) P value SHR 1.01±(0.22) 0.76±(0.10) 0.93±(0.03) 1.05±(0.04) 1.30±(0.18) < 0.001 Age (years) 60.50±(16.54) 63.73±(14.05) 60.48±(16.78) 59.11±(17.13) 58.66±(17.55) 0.003 Sex 0.800 Female 678 (52%) 165 (50%) 177 (54%) 170 (52%) 166 (51%) Male 628 (48%) 162 (50%) 149 (46%) 160 (48%) 157 (49%) BMI 0.028 Normal(≥ 18.5, < 25) 292 (22%) 55 (17%) 69 (21%) 86 (26%) 82 (25%) Obese(≥ 30) 595 (46%) 166 (51%) 141 (43%) 143 (43%) 145 (45%) Overweight(≥ 25, < 30) 391 (30%) 100 (31%) 103 (32%) 97 (29%) 91 (28%) Underweight(< 18.5) 28 (2%) 6 (2%) 13 (4%) 4 (1%) 5 (2%) Education 0.400 Below high school 464 (36%) 121 (37%) 125 (38%) 114 (35%) 104 (32%) High school graduate or higher 842 (64%) 206 (63%) 201 (62%) 216 (65%) 219 (68%) Marital 0.014 Divorced 240 (18%) 59 (18%) 60 (18%) 72 (22%) 49 (15%) Married 868 (66%) 230 (70%) 223 (68%) 208 (63%) 207 (64%) Never married 198 (15%) 38 (12%) 43 (13%) 50 (15%) 67 (21%) Smoking status 0.500 Current 683 (52%) 181 (55%) 161 (49%) 175 (53%) 166 (51%) Former 623 (48%) 146 (45%) 165 (51%) 155 (47%) 157 (49%) Drinking.status 0.120 Current 579 (44%) 130 (40%) 140 (43%) 160 (48%) 149 (46%) Former 727 (56%) 197 (60%) 186 (57%) 170 (52%) 174 (54%) Time on dialysis (years) 10.12±(5.40) 10.02±(5.49) 10.53±(5.35) 9.83±(5.36) 10.10±(5.40) 0.400 Primary kidney disease < 0.001 Diabetic nephropathy 335 (26%) 116 (35%) 62 (19%) 65 (20%) 92 (28%) Glomerulonephritis 384 (29%) 87 (27%) 114 (35%) 101 (31%) 82 (25%) Others 587 (45%) 124 (38%) 150 (46%) 164 (50%) 149 (46%) Hypertension 948 (73%) 265 (81%) 229 (70%) 223 (68%) 231 (72%) < 0.001 Hyperlipidemia 707 (54%) 182 (56%) 179 (55%) 172 (52%) 174 (54%) 0.800 Diabetes 516 (40%) 161 (49%) 93 (29%) 105 (32%) 157 (49%) < 0.001 CVD 510 (39%) 146 (45%) 133 (41%) 121 (37%) 110 (34%) 0.031 Hypotension 294 (23%) 87 (27%) 76 (23%) 68 (21%) 63 (20%) 0.130 Anti hypertension 863 (66%) 245 (75%) 212 (65%) 198 (60%) 208 (64%) < 0.001 Hypolipidemic agents 614 (47%) 173 (53%) 150 (46%) 149 (45%) 142 (44%) 0.100 Anti diabetes 459 (35%) 145 (44%) 83 (25%) 89 (27%) 142 (44%) < 0.001 eGFR 63.49±(35.15) 55.05±(32.66) 65.35±(34.80) 67.80±(35.43) 65.74±(36.39) < 0.001 Albuminuria 494.17±(2,909.36) 907.98±(5,398.76) 265.49±(792.70) 319.52±(1,158.24) 484.47±(1,591.44) < 0.001 1 Mean±SD for continuous; n (%) for categorical Abbreviations: SHR, stress hyperglycemia ratio; BMI, body mass index; eGFR, estimated glomerular filtration rate; CVD, cardiovascular disease. SHR and risk of mortality in hemodialysis During a median follow-up of 62 months, 464 deaths (35.5%) were documented, including 192 due to CVD. All-cause mortality across the SHR quartiles was in 108 (23.3%) Q1, 101 (21.8%) in Q2, 122 (26.3%) in Q3, and 133 (28.7%) in Q4. Corresponding CVD mortality cases were 55 (11.9%), 38 (8.2%), 42 (9.1%), and 57 (12.3%), respectively. Kaplan–Meier survival analysis (Fig. 2 ) demonstrated significant differences in both all-cause and CVD mortality across the SHR groups (log-rank test, P < 0.001 for both). These findings suggest a potential association between elevated SHR and increased mortality risk in patients undergoing hemodialysis (Fig. 2 ). Table 2 presents the results of three Cox regression models evaluating the association between SHR and mortality. For all-cause mortality, higher SHR was consistently associated with increased risk across models. In the fully adjusted model (Model 3) after adjusting for age, sex, education, marital status, smoking status, BMI, drinking status, time on dialysis, primary kidney disease, hypertension, diabetes, hyperlipidemia, cardiovascular disease, eGFR, and albuminuria, participants in the highest SHR quartile exhibited a more than twofold increased risk of death compared to the lowest quartile (HR = 2.05, 95% CI: 1.57–2.68, P < 0.001). Interestingly, individuals in the second quartile demonstrated a modest but statistically significant reduction in mortality risk (HR = 0.90, 95% CI: 0.74–0.97, P < 0.001), suggesting a potential U-shaped relationship. Similar patterns were observed for cardiovascular mortality: in Model 3, the highest SHR quartile was strongly associated with elevated cardiovascular death risk (HR = 2.96, 95% CI: 1.87–4.67, P < 0.001), whereas the second quartile remained protective (HR = 0.92, 95% CI: 0.72–0.95, P < 0.001). These findings indicate a robust and independent association between elevated SHR and both all-cause and cardiovascular mortality, even after comprehensive adjustment for demographic, clinical, and biochemical factors. Table 2 Multivariate Cox regression models for the association between SHR and mortality risk in HD patients. Mortality risk Q1(≤ 0.871) P value Q2(> 0.871, ≤ 0.988) P value Q3(> 0.988, ≤ 1.131) P value Q4 (> 1.131) P value P for trend HR (95%CI) HR (95%CI) HR (95%CI) HR (95%CI) All-cause mortality Model1 Ref Ref 0.84 (0.74, 0.93) 0.007 1.07 (1.04, 1.51) 0.032 1.59 (1.22, 2.07) < 0.001 < 0.001 Model2 Ref Ref 0.88 (0.72, 0.95) 0.012 1.02 (0.98, 1.54) 0.223 1.98 (1.51, 2.58) < 0.001 < 0.001 Model3 Ref Ref 0.90 (0.74, 0.97) 0.013 1.01 (0.96, 1.74) 0.278 2.05 (1.57, 2.68) < 0.001 < 0.001 CVD mortality Model1 Ref Ref 0.85 (0.71, 0.91) 0.004 0.87 (0.92, 1.30) 0.242 2.18 (1.40, 3.41) < 0.001 < 0.001 Model2 Ref Ref 0.90 (0.85, 0.97) 0.008 0.89 (0.80, 1.53) 0.322 2.81 (1.79, 4.41) < 0.001 < 0.001 Model3 Ref Ref 0.92 (0.72, 0.95) 0.012 0.96 (0.90, 1.84) 0.342 2.96 (1.87, 4.67) < 0.001 < 0.001 Multiple Cox regression model: Model 1: Not adjusted. Model 2: Adjusted for Age, sex, BMI, Education, Marital, Smoking status, drinking status, Time on dialysis, Primary kidney disease. Model 3: Adjusted for Age, sex, BMI, Education, Marital, Smoking status, drinking status, Time on dialysis, Primary kidney disease, hypertension, diabetes, hyperlipidemia, CVD, hypotension, eGFR, Albuminuria. Abbreviations: SHR, stress-induced hyperglycemia ratio; BMI, body mass index; eGFR, estimated glomerular filtration rate; CVD, cardiovascular disease. The non-linear relationship between SHR and mortality rate in HD patients Restricted cubic spline (RCS) analysis demonstrated a significant nonlinear association between the stress hyperglycemia ratio (SHR) and mortality among hemodialysis patients (Fig. 3 ). A distinct U-shaped relationship was identified for both all-cause and cardiovascular mortality. For all-cause mortality, the lowest risk was observed at an SHR of approximately 0.8, with mortality risk rising at both lower and higher SHR levels ( P < 0.001 for overall association; P = 0.007 for nonlinearity). Similarly, cardiovascular mortality exhibited a U-shaped pattern ( P < 0.001 for overall association; P = 0.008 for nonlinearity). These findings highlight SHR as a potentially valuable prognostic biomarker for adverse outcomes in the hemodialysis population. To further explore the relationship between SHR and mortality, we employed a piecewise Cox proportional hazards regression model. In the HD cohort, using the RCS-derived inflection points (0.804 and 0.817) as thresholds, and adjusting for potential confounders—including age, sex, education, marital status, smoking status, BMI, drinking status, time on dialysis, primary kidney disease, hypertension, diabetes, hyperlipidemia, cardiovascular disease, eGFR, and albuminuria—the risk of all-cause mortality, and cardiovascular mortality was lowest at SHR values approaching these inflection points. Specifically, at SHR levels near the identified inflection points, the risk of all-cause mortality was reduced by 37% (HR = 0.63, 95% CI: 0.04–0.78, P = 0.007), while cardiovascular mortality risk declined by 43% (HR = 0.57, 95% CI: 0.04–0.87, P = 0.004). However, beyond these thresholds, mortality risks increased markedly. SHR levels above the inflection point were associated with a significantly higher risk of all-cause mortality (HR = 2.73, 95% CI: 1.78–4.18, P < 0.001) and cardiovascular mortality (HR = 2.70, 95% CI: 1.76–4.14, P < 0.001), indicating a sharp escalation in adverse outcomes with rising SHR (Table 3 ). Table 3 Threshold effect analysis of the SHR on all-cause and CVD mortality in patients with HD. Mortality risk Mortality Adjusted HR (95%CI) 1 P value All-cause mortality Total 464 Fitting by two-piecewise Cox pro 0.804 portional risk model inflection point SHR ≤ 0.804 61 0.63 (0.04, 0.78) 0.007 SHR > 0.804 403 2.73 (1.78, 4.18) 0.817 169 2.70 (1.76, 4.14) < 0.001 Multiple Cox regression model adjusted for Age, sex, BMI, Education, Marital, Smoking status, drinking status, Time on dialysis, Primary kidney disease, hypertension, diabetes, hyperlipidemia, CVD, hypotension, eGFR, Albuminuria. Abbreviations: SHR, stress-induced hyperglycemia ratio; BMI, body mass index; eGFR, estimated glomerular filtration rate; CVD, cardiovascular disease. Subgroup analysis Subgroup analyses were conducted to evaluate the consistency of the association between SHR and mortality outcomes across various population strata (Table 4 ). A significant interaction was observed between SHR and BMI in relation to cardiovascular mortality ( P interaction = 0.016), suggesting that BMI may modulate the effect of SHR on cardiovascular risk. For all other stratified variables, no significant interactions were detected, indicating that the associations between SHR and mortality remained broadly consistent across subgroups. Table 4 Subgroup analyses of SHR levels with the risks of all-cause and CVD mortality among individuals with HD. Subgroup All-cause mortality p -value P interaction value CVD mortality p -value P interaction value SHR ≤ 0.804 SHR > 0.804 SHR ≤ 0.817 SHR > 0.817 Age 0.517 0.633 <60 Ref 1.10 (0.60, 1.99) 0.758 Ref 1.21 (0.48, 3.01) 0.685 ≥60 Ref 1.51 (1.10, 2.08) 0.007 Ref 1.75 (1.05, 2.93) 0.033 Sex 0.412 0.817 Female Ref 1.36 (0.89, 2.09) 0.142 Ref 1.76 (0.84, 3.71) 0.135 Male Ref 1.53 (1.06, 2.20) 0.019 Ref 1.76 (1.01, 3.09) 0.047 Education 0.624 0.938 Below high school Ref 1.22 (0.85, 1.75) 0.288 Ref 1.86 (0.91, 3.80) 0.088 High school graduate or higher Ref 1.37 (1.03, 1.81) 0.030 Ref 1.65 (0.93, 2.94) 0.086 Marital 0.664 0.966 Divorced Ref 1.02 (0.54, 1.91) 0.957 Ref 2.15 (0.72, 6.43) 0.173 Married Ref 1.33 0.034 Ref 1.74 (1.03, 2.92) 0.038 (1.02, 1.72) Never married Ref 1.84 (1.00, 3.40) 0.051 Ref 0.98 (0.18, 5.46) 0.980 Smoking status 0.289 0.915 Current Ref 1.48 (1.11, 1.97) 0.007 Ref 1.81 (1.02, 3.22) 0.044 Former Ref 1.32 (0.93, 1.87) 0.125 Ref 1.84 (0.90, 3.75) 0.096 Drinking status 0.728 0.220 Current Ref 1.36 (0.94, 1.95) 0.099 Ref 1.19 (0.61, 2.32) 0.617 Former Ref 1.30 (0.98, 1.73) 0.067 Ref 2.28 (1.22, 4.27) 0.010 BMI 0.191 0.016 ​Underweight < 18.5 Ref 0.35 (0.10, 1.32) 0.123 Ref 0.08 (0.00, 1.97) 0.126 Normal (≥ 18.5, < 25) Ref 1.97 (0.96, 4.02) 0.064 Ref 1.69 (0.75, 3.82) 0.205 Obese (≥ 30) Ref 1.36 (0.94, 1.98) 0.107 Ref 1.94 (1.09, 3.47) 0.025 Overweight (≥ 25, < 30) Ref 1.96 (1.06, 3.64) 0.032 Ref 1.58 (0.69, 3.61) 0.277 Hypertension 0.464 0.059 Yes Ref 1.47 (1.10, 1.98) 0.010 Ref 1.97 (1.20, 3.24) 0.008 No Ref 1.21 (0.56, 2.59) 0.629 Ref 0.97 (0.33, 2.83) 0.953 Hyperlipidemia 0.876 0.915 Yes Ref 1.46 (1.01, 2.12) 0.035 Ref 1.62 (0.93, 2.84) 0.089 No Ref 1.36 (0.89, 2.09) 0.029 Ref 1.76 (0.83, 3.73) 0.142 Diabetes 0.251 0.958 Yes Ref 1.34 (0.95, 1.87) 0.043 Ref 1.69 (0.98, 2.92) 0.061 No Ref 1.81 (1.07, 3.08) 0.012 Ref 1.74 (0.80, 3.81) 0.164 CVD 0.754 0.068 Yes Ref 1.57 (1.06, 2.33) 0.003 Ref 1.23 (0.75, 2.00) 0.408 No Ref 1.58 (1.06, 2.37) 0.035 Ref 2.28 (1.40, 3.71) 0.001 Hypotension 0.944 0.667 Yes Ref 1.48 (1.06, 2.06) 0.001 Ref 1.18 (0.52, 2.67) 0.685 No Ref 1.36 (0.79, 2.35) 0.571 Ref 1.91 (1.11, 3.28) 0.019 1 Multiple Cox regression model adjusted for Age, sex, BMI, Education, Marital, Smoking status, drinking status, Time on dialysis, Primary kidney disease, hypertension, diabetes, hyperlipidemia, CVD, hypotension, eGFR, Albuminuria. Abbreviations: SHR, stress-induced hyperglycemia ratio; BMI, body mass index; eGFR, estimated glomerular filtration rate; CVD, cardiovascular disease. Discussion In this study, we identified a robust, independent association between the SHR, a composite indicator of acute glycemic response relative to chronic glycemia, and both all-cause and cardiovascular mortality in patients undergoing maintenance hemodialysis. After adjustment for a comprehensive set of demographic, clinical, and biochemical factors, SHR demonstrated a U-shaped relationship with mortality outcomes. Specifically, mortality risk increased markedly when SHR exceeded 0.804 for all-cause mortality and 0.817 for cardiovascular mortality. These nonlinear associations underscore the prognostic utility of SHR and suggest that deviations from an optimal glycemic range, whether hypo- or hyperglycemic, may portend worse outcomes in this high-risk population. Our findings support the integration of SHR into routine risk stratification strategies, offering a pragmatic biomarker to guide clinical decision-making in the management of hemodialysis patients. Stress-induced hyperglycemia represents a transient metabolic response to acute physiological stress, driven predominantly by surges in counter-regulatory hormones such as cortisol and catecholamines[ 19 , 20 ]. While this adaptive response may serve to ensure adequate energy supply during critical illness, mounting evidence suggests that stress-induced hyperglycemia exerts numerous deleterious effects, including promotion of a pro-inflammatory state, destabilization of atherosclerotic plaques, and activation of the sympathoadrenal axis, characterized by elevated epinephrine and norepinephrine levels[ 21 – 25 ]. Consistent with these pathophysiological mechanisms, the SHR—a refined index capturing acute glycemic excursions relative to chronic glycemic status—has emerged as a robust prognostic marker. Prior studies have linked elevated SHR with increased risks of pulmonary infection, advanced coronary artery disease, and thrombotic burden. More importantly, SHR has demonstrated predictive value for all-cause mortality in patients with acute myocardial infarction and acute decompensated heart failure, irrespective of baseline diabetes status[ 26 – 28 ]. These findings highlight the clinical utility of SHR as a simple yet powerful tool for early identification of high-risk individuals in a variety of acute care settings. Currently, studies investigating the relationship between SHR and mortality in patients with HD are limited. In our HD cohort, we identified a significant U-shaped association between the SHR and all-cause mortality. Using RCS analysis, the lowest mortality risk was observed at an SHR of approximately 0.804. Deviations from this threshold—both lower and higher SHR values—were associated with a marked increase in mortality risk. A low SHR may reflect impaired physiological stress response or underlying metabolic compromise, whereas a high SHR likely indicates stress-induced hyperglycemia and chronic metabolic dysregulation, both contributing to adverse outcomes. This non-linear association underscores the potential clinical utility of maintaining SHR within an optimal range to reduce mortality risk in HD patients. These findings are consistent with prior research in other clinical populations. For instance, Li et al. reported a U-shaped relationship between SHR and both in-hospital and 1-year mortality in critically ill patients in a cardiac intensive care unit, with the lowest risk observed in the SHR range of 0.75–0.99 [ 29 ]. Similarly, Yan et al. demonstrated a U-shaped association between SHR and all-cause mortality in female patients with diabetes mellitus [ 10 ]. In addition, Gregory et al. found that both low ( 9.0%) hemoglobin A1c levels were linked to increased mortality, further supporting the importance of maintaining glycemic indices within a physiological range [ 30 ]. Collectively, these findings highlight SHR as a valuable biomarker for risk stratification and personalized management in the HD population. In terms of cardiovascular mortality, we identified a distinct U-shaped association between the SHR and cardiovascular mortality in patients with hemodialysis. Moderate elevations in SHR, typically indicative of a physiological stress response, were associated with the lowest cardiovascular risk—potentially reflecting an adequate metabolic adaptation to acute stress. In contrast, both low and high extremes of SHR were linked to significantly elevated cardiovascular mortality, with risk rising sharply when SHR exceeded 0.817. This may be attributed to the deleterious cardiovascular effects of persistent hyperglycemia, including endothelial dysfunction, pro-inflammatory states, and accelerated atherosclerosis, particularly in patients with underlying cardiovascular disease[ 9 , 31 , 32 ]. Our findings align with those of Yang et al., who reported a U-shaped relationship between SHR and major adverse cardiovascular events in patients with acute coronary syndrome[ 15 ]. Similarly, a meta-analysis by Karakasis et al. demonstrated a strong association between elevated SHR and both major adverse cardiovascular and cerebrovascular events and overall mortality across diverse clinical populations, reinforcing SHR’s prognostic utility [ 33 ]. Additionally, prior studies in patients with chronic kidney disease (CKD) and diabetic kidney disease have reported comparable nonlinear associations[ 17 ]. Collectively, these findings underscore the importance of recognizing SHR as a dynamic and nonlinear risk marker in HD patients. Maintaining SHR within an optimal range may offer a novel approach to improving cardiovascular outcomes in this high-risk population. The SHR may influence the clinical prognosis of HD patients through multiple complex biological mechanisms. An elevated SHR typically reflects significant blood glucose fluctuations under stress conditions, indicating impaired glycemic control[ 18 ]. More importantly, it serves as a marker of heightened metabolic and immune system dysregulation, both of which are closely associated with poor outcomes in HD patients[ 14 , 33 , 34 ]. Firstly, persistent hyperglycemia impairs insulin sensitivity and is closely linked to increased secretion of stress hormones such as catecholamines, cortisol, and glucagon[ 21 , 23 , 35 ]. These hormones not only raise blood glucose levels but also enhance oxidative stress, promote atherosclerosis, and cause endothelial dysfunction—all of which accelerate the development of CVD[ 23 , 24 ]. Moreover, elevated SHR may activate the renin–angiotensin–aldosterone system (RAAS) and trigger the release of pro-inflammatory cytokines, thereby intensifying systemic inflammation and contributing to further injury to the cardiovascular and renal systems[ 25 ]. In addition, hyperglycemia has been associated with impaired fibrinolysis due to increased levels of plasminogen activator inhibitor-1 (PAI-1), which reduces fibrinolytic activity and heightens the risk of thrombotic events[ 36 , 37 ]. In HD patients, these mechanisms are particularly detrimental. Given their already high cardiovascular burden, acute blood glucose fluctuations may trigger vascular wall stress, aggravate myocardial ischemia, and precipitate arrhythmias, leading to an increased risk of cardiovascular mortality[ 38 , 39 ]. Compounding this issue, HD patients often exhibit immunosuppression, and elevated SHR may further impair immune defense mechanisms, increasing susceptibility to infections—a leading cause of all-cause mortality in this population[ 40 – 42 ]. Furthermore, elevated SHR is frequently associated with malnutrition, chronic inflammation, and metabolic derangements, all of which are known predictors of poor prognosis in HD patients. Therefore, precise glycemic control and mitigation of glucose variability are crucial for improving survival and reducing cardiovascular mortality in this vulnerable group. This study highlights the clinical relevance of the stress hyperglycemia ratio (SHR) as a novel and independent prognostic biomarker in hemodialysis (HD) patients. Elevated SHR was strongly associated with increased risks of all-cause and cardiovascular mortality, even after adjusting for multiple confounders. The observed U-shaped relationship suggests that both low and high SHR levels may be detrimental, underscoring the importance of maintaining optimal glucose regulation during stress. SHR may offer superior prognostic value compared to traditional glycemic markers in this population, reflecting underlying metabolic instability, inflammation, and cardiovascular burden. Its association with key risk factors such as hypertension, diabetes, and albuminuria further supports its clinical utility. Integrating SHR into routine assessment could enhance risk stratification and guide individualized glycemic management to improve outcomes in HD patients. This study has several limitations that should be acknowledged. First, the observational design precludes causal inference; although SHR was significantly associated with mortality, we cannot determine a direct cause-effect relationship. Second, SHR was calculated using a single FPG and HbA1c measurement at baseline, which may not fully capture dynamic changes in glucose levels over time. Third, residual confounding is possible despite adjustments for numerous clinical and biochemical variables, particularly regarding unmeasured factors such as nutritional status, inflammation markers, and insulin therapy details. Fourth, the study population was limited to a single cohort of hemodialysis patients, which may restrict the generalizability of findings to other dialysis modalities or non-dialysis CKD populations. Finally, the retrospective nature of the analysis may introduce selection bias. Future prospective and multicenter studies are needed to validate these findings and explore whether interventions targeting SHR can improve clinical outcomes. Conclusion In conclusion, the stress SHR is a valuable and independent predictor of both all-cause and cardiovascular mortality in patients undergoing hemodialysis. Our findings demonstrate a significant U-shaped association between SHR and mortality risk, suggesting that both inadequate and excessive glycemic responses to stress may adversely affect patient outcomes. SHR not only reflects glycemic fluctuations under stress but also captures broader metabolic and inflammatory disturbances common in HD patients. As such, it may serve as a practical and informative biomarker for risk assessment and clinical decision-making. Incorporating SHR into routine clinical evaluation could improve prognostic accuracy and support more targeted glycemic control strategies, ultimately contributing to better survival and cardiovascular outcomes in this high-risk population. Declarations Acknowledgements We would like to thank all those who helped with this study. Authors contributions ZH and TX were responsible for conducting the study, collecting data, and drafting the initial manuscript. They also jointly performed the statistical analyses and contributed to the study design. Additionally, both authors were involved in data acquisition, analysis, interpretation, and the preparation of the final manuscript. All authors reviewed and approved the final version. Funding No funding. Data availability statement Access can be granted upon reasonable request and approval by the correspondence author. Competing interests No conflicts of interest. Ethics declarations Human Ethics and Consent to Participate declarations Ethics Committee of Jiangyin People’s Hospital approved the study (20240252), Written informed consent was obtained from all study subjects in accordance with the Declaration of Helsinki. Consent for publication Not applicable. Authors contributions Hongyu Zhou and Xiaodong Tang contributed substantially to the conception and design of the study, data acquisition and analysis, drafting of the manuscript, and critical revision for important intellectual content. All authors reviewed and approved the final version of the manuscript for publication. Corresponding author Correspondence to Xiaodong Tang. References Takahashi S, Tanaka T, Suzuki Y, Yoshida N, Hitaka M, Ishii S, Yamazaki K, Masai M, Yamada Y, Ohashi Y: Association Between Malnutrition, Low Muscle Mass, Elevated NT-ProBNP Levels, and Mortality in Hemodialysis Patients . Nutrients 2025, 17 (11). 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Shamoon H, Hendler R, Sherwin RS: Synergistic interactions among antiinsulin hormones in the pathogenesis of stress hyperglycemia in humans . The Journal of clinical endocrinology and metabolism 1981, 52 (6):1235-1241. Monnier L, Mas E, Ginet C, Michel F, Villon L, Cristol JP, Colette C: Activation of oxidative stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes . Jama 2006, 295 (14):1681-1687. Yamamoto Y, Yamamoto H: RAGE-Mediated Inflammation, Type 2 Diabetes, and Diabetic Vascular Complication . Frontiers in endocrinology 2013, 4 :105. Duran-Salgado MB, Rubio-Guerra AF: Diabetic nephropathy and inflammation . World journal of diabetes 2014, 5 (3):393-398. Liu J, Zhou Y, Huang H, Liu R, Kang Y, Zhu T, Wu J, Gao Y, Li Y, Wang C et al : Impact of stress hyperglycemia ratio on mortality in patients with critical acute myocardial infarction: insight from american MIMIC-IV and the chinese CIN-II study . Cardiovascular diabetology 2023, 22 (1):281. Qiao Z, Bian X, Song C, Zhang R, Yuan S, Lin Z, Zhu C, Liu Q, Ma W, Dou K: High stress hyperglycemia ratio predicts adverse clinical outcome in patients with coronary three-vessel disease: a large-scale cohort study . Cardiovascular diabetology 2024, 23 (1):190. Li L, Zhou L, Peng X, Zhang Z, Zhang Z, Xiong Y, Hu Z, Yao Y: Association of stress hyperglycemia ratio and mortality in patients with sepsis: results from 13,199 patients . Infection 2024, 52 (5):1973-1982. Li L, Zhao M, Zhang Z, Zhou L, Zhang Z, Xiong Y, Hu Z, Yao Y: Prognostic significance of the stress hyperglycemia ratio in critically ill patients . Cardiovascular diabetology 2023, 22 (1):275. Nichols GA, Joshua-Gotlib S, Parasuraman S: Glycemic control and risk of cardiovascular disease hospitalization and all-cause mortality . Journal of the American College of Cardiology 2013, 62 (2):121-127. An Y, Xu BT, Wan SR, Ma XM, Long Y, Xu Y, Jiang ZZ: The role of oxidative stress in diabetes mellitus-induced vascular endothelial dysfunction . Cardiovascular diabetology 2023, 22 (1):237. Faro DC, Di Pino FL, Monte IP: Inflammation, Oxidative Stress, and Endothelial Dysfunction in the Pathogenesis of Vascular Damage: Unraveling Novel Cardiovascular Risk Factors in Fabry Disease . International journal of molecular sciences 2024, 25 (15). Karakasis P, Stalikas N, Patoulias D, Pamporis K, Karagiannidis E, Sagris M, Stachteas P, Bougioukas KI, Anastasiou V, Daios S et al : Prognostic value of stress hyperglycemia ratio in patients with acute myocardial infarction: A systematic review with Bayesian and frequentist meta-analysis . Trends in cardiovascular medicine 2024, 34 (7):453-465. Esdaile H, Khan S, Mayet J, Oliver N, Reddy M, Shah ASV: The association between the stress hyperglycaemia ratio and mortality in cardiovascular disease: a meta-analysis and systematic review . Cardiovascular diabetology 2024, 23 (1):412. Dünser MW, Hasibeder WR: Sympathetic overstimulation during critical illness: adverse effects of adrenergic stress . Journal of intensive care medicine 2009, 24 (5):293-316. Alsharidah AS: Diabetes mellitus and diabetic nephropathy: a review of the literature on hemostatic changes in coagulation and thrombosis . Blood research 2022, 57 (2):101-105. Violi F, Pastori D, Pignatelli P, Carnevale R: Nutrition, Thrombosis, and Cardiovascular Disease . Circulation research 2020, 126 (10):1415-1442. Jia Y, Long D, Song W, Wang Q, Zhang Q: Association between continuous glucose monitoring metrics and cardiovascular autonomic neuropathy in diabetic patients: a systematic review . Reviews in endocrine & metabolic disorders 2025. Chia CW, Egan JM, Ferrucci L: Age-Related Changes in Glucose Metabolism, Hyperglycemia, and Cardiovascular Risk . Circulation research 2018, 123 (7):886-904. Chen YH, Chen YY, Fang YW, Liou HH, Wang JT, Tsai MH: Long-Term Outcome Analysis of Peritoneal Dialysis and Hemodialysis in Patients With End-Stage Kidney Disease: A Real-World Data Analysis . Hemodialysis international International Symposium on Home Hemodialysis 2025. Puca E, Puca E, Vrekaj K, Puca D: Severe hypoglycemia induced by Tigecycline in a diabetic and hemodialysis patient . Journal of infection in developing countries 2024, 18 (7):1157-1160. Libetta C, Sepe V, Esposito P, Galli F, Dal Canton A: Oxidative stress and inflammation: Implications in uremia and hemodialysis . Clinical biochemistry 2011, 44 (14-15):1189-1198. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7204612","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":511520650,"identity":"83d12f1c-b806-4bf9-8d71-b98e86bdf823","order_by":0,"name":"Hongyu Zhou","email":"","orcid":"","institution":"Jiangyin People’s Hospital，Medical College of Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Hongyu","middleName":"","lastName":"Zhou","suffix":""},{"id":511520651,"identity":"426c5109-ef7b-406d-af9e-ed0f27f1c952","order_by":1,"name":"Xiaodong Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIie3PsQrCMBCA4ZNCdDjtmoLoKwSETgVfJUHIVMGxg0OhooOKs2/h6NgQiEvcOyo+gbg4OOiumLo55Jsy3E/uADzvD5GwKMvrI8GwWagTz6bupEONUNtcdqOVGbGTNe6kB+lAt3OdsCqNo/MsqLEYGF629xKhkjITOYFwseTfk+B1S2QTbKwuphL7LlB73Ll/YURi0JSyEpYAo2NXkrKSE42vRzwR86BmouYasZXGUC+hhqvcSqRoRpRbg85b+ptC3x5ZMhweCnW9Z9NeuFh/T97gb+Oe53neR0+TgFCGPKdLjAAAAABJRU5ErkJggg==","orcid":"","institution":"Jiangyin People’s Hospital，Medical College of Southeast University","correspondingAuthor":true,"prefix":"","firstName":"Xiaodong","middleName":"","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2025-07-24 10:38:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7204612/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7204612/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90982943,"identity":"7bd60d35-a5b3-4c2c-bd16-fccbae13f8ac","added_by":"auto","created_at":"2025-09-10 09:32:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1230891,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study population.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7204612/v1/2d16b9c6851f9274260a3d2e.png"},{"id":90981590,"identity":"6cb6a5cd-01af-4d56-b81c-9ac240691d6c","added_by":"auto","created_at":"2025-09-10 09:24:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":259780,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival curves for all-cause (A) and cardiovascular mortality (B) by SHR quartiles in HD patients.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7204612/v1/0337dda7f514c15538f5a06f.png"},{"id":90982941,"identity":"443d27d7-dcbe-4ce6-8e1d-fa7983ebcf47","added_by":"auto","created_at":"2025-09-10 09:32:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":208993,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline analysis of the SHR index and its association with mortality. (A) All-cause mortality. (B) Cardiovascular mortality\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7204612/v1/1fd08b0ab15dbe56982b0db3.png"},{"id":90984903,"identity":"13ca3d54-3dc7-40ec-8593-768dc978310b","added_by":"auto","created_at":"2025-09-10 09:49:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4820943,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7204612/v1/ddc23544-c9d1-4d9f-b42d-ab0865c5073e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe Prognostic Value of Stress Hyperglycemia Ratio in Hemodialysis Patients: A U-Shaped Association with Mortality\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn patients with end-stage kidney disease (ESKD), maintenance hemodialysis (HD) is essential for sustaining life[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, improving long-term outcomes and mitigating systemic complications remain critical clinical challenges. Despite advances in dialysis technology and supportive care, this population continues to experience disproportionately high mortality rates, largely attributed to cardiovascular disease (CVD)[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Although routine clinical monitoring is standard practice, conventional risk stratification tools often fail to capture the multifactorial and dynamic nature of clinical risk in this complex population[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This highlights the urgent need for novel, accessible biomarkers that can reflect systemic stress and reliably identify individuals at elevated risk of adverse outcomes.\u003c/p\u003e\u003cp\u003eThe stress hyperglycemia ratio (SHR), characterized by a transient and significant elevation in blood glucose (BG) levels during acute physiological or pathological stress, is a well-documented marker of poor prognosis in a variety of clinical conditions[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This metabolic response involves a complex interplay between neuroendocrine activation, immune modulation, and altered insulin sensitivity[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. To better quantify this phenomenon, the SHR was introduced as a standardized index, calculated by dividing the admission BG by the estimated average glucose derived from glycated hemoglobin (HbA1c)[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. By accounting for chronic glycemic status, SHR provides a more individualized and clinically relevant measure of relative hyperglycemia during stress[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Accumulating evidence suggests that SHR is a robust prognostic indicator across a range of diseases. Elevated SHR has been associated with increased morbidity and mortality in patients with hospital-acquired pneumonia, acute coronary syndromes, thrombotic events, and ischemic stroke, where it correlates with the risk of hemorrhagic transformation[\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Moreover, SHR has shown predictive value for all-cause mortality in patients with acute myocardial infarction and heart failure[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In the field of nephrology, recent studies have demonstrated the utility of SHR in predicting outcomes in patients with acute kidney injury, diabetic kidney disease, and progression to ESKD[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, the prognostic value of SHR in the chronic hemodialysis population remains poorly defined.\u003c/p\u003e\u003cp\u003eThis study aims to investigate the prognostic significance of SHR in a large cohort of patients receiving maintenance HD, focusing on its association with all-cause and cardiovascular mortality. We hypothesize that higher SHR levels are independently associated with increased mortality risk and that SHR may serve as a simple, yet powerful tool for risk stratification in this high-risk population.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003eStudy Design and Population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis retrospective cohort study included patients with HD who were admitted to blood purification center of Jiangyin People\u0026rsquo;s Hospital between July 2012 and June 2022. The study adhered to all relevant tenets of the Declaration of Helsinki and was approved by the Medical Ethics Committee of the Jiangyin People\u0026rsquo;s Hospital (reference number: 20240252). Patients whose research has been approved by a medical ethics committee agree to the exemption. The clinical data of all patients were derived entirely from the medical records of the hospital. The inclusion criteria for this study were as follows: (1) aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (2) received regular hemodialysis treatment less than 3 months; (3) availability of both admission blood glucose (BG) level measured within 24 hours of hospital admission and glycated hemoglobin (HbA1c) data within 3 months prior to or during hospitalization; (4) hospitalization due to an acute medical condition (e.g., infection, cardiovascular event, or other acute illness); (5) complete follow-up data for mortality, including all-cause, infection-related, and cardiovascular-related deaths. Exclusion criteria were: (1) Patients on HD for less than 3 months; (2) lack of either admission BG or HbA1c data; (3) systemic infection, cancer, recent surgery or trauma, gout, or hospitalization for cardiovascular events within the last six months prior to enrollment; (4) metabolic encephalopathy, mental or emotional disorders, epilepsy, or dementia; (5) diagnosis of diabetic ketoacidosis or hyperosmolar hyperglycemic state at admission; (6) discharged or died within 24 hours of admission; (7) missing outcome or follow-up information. All patients were followed up until February 2024. Finally, a total of 1,306 patients with MHD were included in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eDefinition of SHR\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe SHR was used as the primary exposure variable to quantify BG fluctuations under acute or subacute stress conditions. SHR reflects both the degree of glycemic variation experienced during hospitalization and the body's capacity to regulate these fluctuations. It was calculated using the formula: SHR\u0026thinsp;=\u0026thinsp;FPG / (1.59 \u0026times; HbA1c \u0026ndash; 2.59), where FPG represents fasting plasma glucose (mg/dL) and HbA1c indicates glycated hemoglobin (%), as previously described [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. SHR was analyzed both as a continuous variable and categorically by dividing participants into quartiles (Q1\u0026ndash;Q4) based on SHR values. Q1 served as the reference group for comparisons. This stratified approach enabled a more nuanced assessment of the relationship between SHR and clinical outcomes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssessment of the covariates\u003c/b\u003e\u003c/p\u003e\u003cp\u003eComprehensive baseline demographic and clinical data were collected from all participants. This included: (i) sociodemographic variables such as age, sex, body mass index (BMI), education level, marital status, duration of dialysis, primary cause of kidney disease, smoking habits, and alcohol consumption; (ii) comorbidities, including albuminuria, hypertension, hyperlipidemia, diabetes mellitus, hypotension, and cardiovascular disease; and (iii) medication usage, specifically antihypertensive, antidiabetic, and lipid-lowering therapies. The estimated glomerular filtration rate (eGFR) was calculated using the CKD-EPI equation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssessment of outcomes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe primary outcome of this study was all-cause mortality, while the secondary outcome focused on CVD-specific mortality. CVD-related deaths were defined as those resulting from heart failure, pulmonary edema, ischemic heart disease, arrhythmia, valvular heart disease, cerebral infarction, cerebral hemorrhage, or subarachnoid hemorrhage. All clinical events were independently adjudicated by a panel of experts who were blinded to the study objectives and baseline SHR values. A team of clinical specialists conducted a thorough annual review of hospital discharge records and confirmed outcomes using hospital documentation, death certificates, and other original source materials. Participants were followed from the date of their baseline assessment\u0026mdash;conducted three months after the initiation of maintenance hemodialysis\u0026mdash;until death or the end of the study period in February 2024.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eContinuous variables were presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SD), while categorical variables were expressed as frequencies and percentages. Differences across SHR quartiles were assessed using t-tests or Kruskal\u0026ndash;Wallis tests for continuous variables and chi-square tests for categorical variables. Kaplan\u0026ndash;Meier survival curves were constructed to visualize cumulative incidence of outcomes, with comparisons evaluated via the log-rank test.\u003c/p\u003e\u003cp\u003eThe association between SHR and both all-cause and cardiovascular mortality was examined using Cox proportional hazards models, reporting hazard ratios (HRs) and 95% confidence intervals (CIs). Three models were developed: Model 1 was unadjusted; Model 2 adjusted for demographic and lifestyle factors (age, sex, marital status, education level, smoking and alcohol use, dialysis duration, and primary kidney disease); and Model 3 further adjusted for clinical comorbidities (hypertension, hyperlipidemia, diabetes mellitus, hypotension, cardiovascular disease), along with eGFR and albuminuria.\u003c/p\u003e\u003cp\u003eRestricted cubic spline (RCS) analysis was used to explore nonlinear associations between SHR and mortality outcomes. Where nonlinearity was detected, inflection points were identified, and piecewise Cox regression was applied to assess risk on either side of the threshold. Subgroup analyses were conducted to evaluate effect modification and potential interactions across prespecified strata. A two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All analyses were conducted using standard statistical software packages.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eBaseline characteristics of the study population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 1,306 patients with HD were included in this study. The mean age of the participants was 60.5 years, with 52% of the patients being female. The average SHR values across the four quartiles (Q1, Q2, Q3, and Q4) were 0.76, 0.93, 1.05, and 1.30, respectively. Compared to patients in the Q4 group (the highest SHR quartile), those in the Q1 group were more likely to be older, obesity, and married. Diabetic nephropathy was more prevalent in the lowest SHR quartile, while glomerulonephritis and other etiologies were more common in higher quartiles (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Higher SHR was also linked to increased prevalence of hypertension and diabetes (both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), reduced use of antihypertensive and antidiabetic medications (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and higher eGFR and albuminuria levels (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings suggest that SHR reflects important clinical and metabolic differences in HD patients and may serve as a meaningful marker of disease burden (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of the study population by SHR level in individuals with hemodialysis.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall (n\u0026thinsp;=\u0026thinsp;1,306)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ1(\u0026le;\u0026thinsp;0.871)\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;327)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ2(\u0026gt;\u0026thinsp;0.871, \u0026le;\u0026thinsp;0.988) (n\u0026thinsp;=\u0026thinsp;326)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQ3(\u0026gt;\u0026thinsp;0.988, \u0026le;\u0026thinsp;1.131)\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;330)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eQ4 (\u0026gt;\u0026thinsp;1.131)\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;323)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.01\u0026plusmn;(0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.76\u0026plusmn;(0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.93\u0026plusmn;(0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.05\u0026plusmn;(0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.30\u0026plusmn;(0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60.50\u0026plusmn;(16.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.73\u0026plusmn;(14.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.48\u0026plusmn;(16.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e59.11\u0026plusmn;(17.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e58.66\u0026plusmn;(17.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.800\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e678 (52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e165 (50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e177 (54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e170 (52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e166 (51%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e628 (48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e162 (50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e149 (46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e160 (48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e157 (49%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal(\u0026ge;\u0026thinsp;18.5, \u0026lt;\u0026thinsp;25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e292 (22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55 (17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69 (21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e86 (26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e82 (25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObese(\u0026ge;\u0026thinsp;30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e595 (46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e166 (51%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e141 (43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e143 (43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e145 (45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverweight(\u0026ge;\u0026thinsp;25, \u0026lt;\u0026thinsp;30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e391 (30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100 (31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e103 (32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e97 (29%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e91 (28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight(\u0026lt;\u0026thinsp;18.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28 (2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5 (2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.400\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelow high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e464 (36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e121 (37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e125 (38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e114 (35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e104 (32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school graduate or higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e842 (64%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e206 (63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e201 (62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e216 (65%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e219 (68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDivorced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e240 (18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59 (18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60 (18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e72 (22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e49 (15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e868 (66%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e230 (70%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e223 (68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e208 (63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e207 (64%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e198 (15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38 (12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43 (13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50 (15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e67 (21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.500\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e683 (52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e181 (55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e161 (49%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e175 (53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e166 (51%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e623 (48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e146 (45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e165 (51%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e155 (47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e157 (49%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrinking.status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.120\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e579 (44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e130 (40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e140 (43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e160 (48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e149 (46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e727 (56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e197 (60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e186 (57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e170 (52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e174 (54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime on dialysis (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.12\u0026plusmn;(5.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.02\u0026plusmn;(5.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.53\u0026plusmn;(5.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.83\u0026plusmn;(5.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.10\u0026plusmn;(5.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.400\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary kidney disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetic nephropathy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e335 (26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e116 (35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62 (19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65 (20%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e92 (28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlomerulonephritis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e384 (29%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87 (27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e114 (35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e101 (31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e82 (25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e587 (45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e124 (38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e150 (46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e164 (50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e149 (46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e948 (73%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e265 (81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e229 (70%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e223 (68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e231 (72%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHyperlipidemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e707 (54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e182 (56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e179 (55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e172 (52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e174 (54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.800\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e516 (40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e161 (49%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93 (29%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e105 (32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e157 (49%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCVD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e510 (39%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e146 (45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e133 (41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e121 (37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e110 (34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypotension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e294 (23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87 (27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76 (23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e68 (21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e63 (20%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnti hypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e863 (66%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e245 (75%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e212 (65%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e198 (60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e208 (64%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypolipidemic agents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e614 (47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e173 (53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e150 (46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e149 (45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e142 (44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnti diabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e459 (35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e145 (44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e83 (25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e89 (27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e142 (44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eeGFR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63.49\u0026plusmn;(35.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55.05\u0026plusmn;(32.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65.35\u0026plusmn;(34.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e67.80\u0026plusmn;(35.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e65.74\u0026plusmn;(36.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbuminuria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e494.17\u0026plusmn;(2,909.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e907.98\u0026plusmn;(5,398.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e265.49\u0026plusmn;(792.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e319.52\u0026plusmn;(1,158.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e484.47\u0026plusmn;(1,591.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e1\u003c/sup\u003eMean\u0026plusmn;SD for continuous; n (%) for categorical\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAbbreviations: SHR, stress hyperglycemia ratio; BMI, body mass index; eGFR, estimated glomerular filtration rate; CVD, cardiovascular disease.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSHR and risk of mortality in hemodialysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDuring a median follow-up of 62 months, 464 deaths (35.5%) were documented, including 192 due to CVD. All-cause mortality across the SHR quartiles was in 108 (23.3%) Q1, 101 (21.8%) in Q2, 122 (26.3%) in Q3, and 133 (28.7%) in Q4. Corresponding CVD mortality cases were 55 (11.9%), 38 (8.2%), 42 (9.1%), and 57 (12.3%), respectively. Kaplan\u0026ndash;Meier survival analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) demonstrated significant differences in both all-cause and CVD mortality across the SHR groups (log-rank test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both). These findings suggest a potential association between elevated SHR and increased mortality risk in patients undergoing hemodialysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of three Cox regression models evaluating the association between SHR and mortality. For all-cause mortality, higher SHR was consistently associated with increased risk across models. In the fully adjusted model (Model 3) after adjusting for age, sex, education, marital status, smoking status, BMI, drinking status, time on dialysis, primary kidney disease, hypertension, diabetes, hyperlipidemia, cardiovascular disease, eGFR, and albuminuria, participants in the highest SHR quartile exhibited a more than twofold increased risk of death compared to the lowest quartile (HR\u0026thinsp;=\u0026thinsp;2.05, 95% CI: 1.57\u0026ndash;2.68, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Interestingly, individuals in the second quartile demonstrated a modest but statistically significant reduction in mortality risk (HR\u0026thinsp;=\u0026thinsp;0.90, 95% CI: 0.74\u0026ndash;0.97, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting a potential U-shaped relationship. Similar patterns were observed for cardiovascular mortality: in Model 3, the highest SHR quartile was strongly associated with elevated cardiovascular death risk (HR\u0026thinsp;=\u0026thinsp;2.96, 95% CI: 1.87\u0026ndash;4.67, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas the second quartile remained protective (HR\u0026thinsp;=\u0026thinsp;0.92, 95% CI: 0.72\u0026ndash;0.95, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings indicate a robust and independent association between elevated SHR and both all-cause and cardiovascular mortality, even after comprehensive adjustment for demographic, clinical, and biochemical factors.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate Cox regression models for the association between SHR and mortality risk in HD patients.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMortality risk\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1(\u0026le;\u0026thinsp;0.871)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ2(\u0026gt;\u0026thinsp;0.871, \u0026le;\u0026thinsp;0.988)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eQ3(\u0026gt;\u0026thinsp;0.988, \u0026le;\u0026thinsp;1.131)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eQ4 (\u0026gt;\u0026thinsp;1.131)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAll-cause mortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.84 (0.74, 0.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.07 (1.04, 1.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.59 (1.22, 2.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.88 (0.72, 0.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.02 (0.98, 1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.98 (1.51, 2.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.90 (0.74, 0.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.01 (0.96, 1.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.05 (1.57, 2.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCVD mortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.85 (0.71, 0.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.87 (0.92, 1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.18 (1.40, 3.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.90 (0.85, 0.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.89 (0.80, 1.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.81 (1.79, 4.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.92 (0.72, 0.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.96 (0.90, 1.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.96 (1.87, 4.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eMultiple Cox regression model: Model 1: Not adjusted. Model 2: Adjusted for Age, sex, BMI, Education, Marital, Smoking status, drinking status, Time on dialysis, Primary kidney disease. Model 3: Adjusted for Age, sex, BMI, Education, Marital, Smoking status, drinking status, Time on dialysis, Primary kidney disease, hypertension, diabetes, hyperlipidemia, CVD, hypotension, eGFR, Albuminuria.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eAbbreviations: SHR, stress-induced hyperglycemia ratio; BMI, body mass index; eGFR, estimated glomerular filtration rate; CVD, cardiovascular disease.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe non-linear relationship between SHR and mortality rate in HD patients\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRestricted cubic spline (RCS) analysis demonstrated a significant nonlinear association between the stress hyperglycemia ratio (SHR) and mortality among hemodialysis patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A distinct U-shaped relationship was identified for both all-cause and cardiovascular mortality. For all-cause mortality, the lowest risk was observed at an SHR of approximately 0.8, with mortality risk rising at both lower and higher SHR levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for overall association; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007 for nonlinearity). Similarly, cardiovascular mortality exhibited a U-shaped pattern (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for overall association; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008 for nonlinearity). These findings highlight SHR as a potentially valuable prognostic biomarker for adverse outcomes in the hemodialysis population.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo further explore the relationship between SHR and mortality, we employed a piecewise Cox proportional hazards regression model. In the HD cohort, using the RCS-derived inflection points (0.804 and 0.817) as thresholds, and adjusting for potential confounders\u0026mdash;including age, sex, education, marital status, smoking status, BMI, drinking status, time on dialysis, primary kidney disease, hypertension, diabetes, hyperlipidemia, cardiovascular disease, eGFR, and albuminuria\u0026mdash;the risk of all-cause mortality, and cardiovascular mortality was lowest at SHR values approaching these inflection points. Specifically, at SHR levels near the identified inflection points, the risk of all-cause mortality was reduced by 37% (HR\u0026thinsp;=\u0026thinsp;0.63, 95% CI: 0.04\u0026ndash;0.78, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), while cardiovascular mortality risk declined by 43% (HR\u0026thinsp;=\u0026thinsp;0.57, 95% CI: 0.04\u0026ndash;0.87, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004). However, beyond these thresholds, mortality risks increased markedly. SHR levels above the inflection point were associated with a significantly higher risk of all-cause mortality (HR\u0026thinsp;=\u0026thinsp;2.73, 95% CI: 1.78\u0026ndash;4.18, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and cardiovascular mortality (HR\u0026thinsp;=\u0026thinsp;2.70, 95% CI: 1.76\u0026ndash;4.14, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating a sharp escalation in adverse outcomes with rising SHR (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThreshold effect analysis of the SHR on all-cause and CVD mortality in patients with HD.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMortality risk\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMortality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdjusted HR (95%CI)\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAll-cause mortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFitting by two-piecewise Cox pro\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eportional risk model inflection point\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSHR\u0026thinsp;\u0026le;\u0026thinsp;0.804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.63 (0.04, 0.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSHR\u0026thinsp;\u0026gt;\u0026thinsp;0.804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.73 (1.78, 4.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiovascular mortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFitting by two-piecewise Cox pro\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eportional risk model inflection point\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSHR\u0026thinsp;\u0026le;\u0026thinsp;0.817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.57 (0.04, 0.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSHR\u0026thinsp;\u0026gt;\u0026thinsp;0.817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.70 (1.76, 4.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eMultiple Cox regression model adjusted for Age, sex, BMI, Education, Marital, Smoking status, drinking status, Time on dialysis, Primary kidney disease, hypertension, diabetes, hyperlipidemia, CVD, hypotension, eGFR, Albuminuria.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: SHR, stress-induced hyperglycemia ratio; BMI, body mass index; eGFR, estimated glomerular filtration rate; CVD, cardiovascular disease.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSubgroup analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSubgroup analyses were conducted to evaluate the consistency of the association between SHR and mortality outcomes across various population strata (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A significant interaction was observed between SHR and BMI in relation to cardiovascular mortality (\u003cem\u003eP\u003c/em\u003e interaction\u0026thinsp;=\u0026thinsp;0.016), suggesting that BMI may modulate the effect of SHR on cardiovascular risk. For all other stratified variables, no significant interactions were detected, indicating that the associations between SHR and mortality remained broadly consistent across subgroups.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSubgroup analyses of SHR levels with the risks of all-cause and CVD mortality among individuals with HD.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSubgroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eAll-cause mortality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e interaction value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eCVD mortality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e interaction value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSHR\u0026thinsp;\u0026le;\u0026thinsp;0.804\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSHR\u0026thinsp;\u0026gt;\u0026thinsp;0.804\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSHR\u0026thinsp;\u0026le;\u0026thinsp;0.817\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSHR\u0026thinsp;\u0026gt;\u0026thinsp;0.817\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.633\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.10 (0.60, 1.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.758\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.21 (0.48, 3.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.685\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.51 (1.10, 2.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.75 (1.05, 2.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.817\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.36 (0.89, 2.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.76 (0.84, 3.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.53 (1.06, 2.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.76 (1.01, 3.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.624\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.938\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelow high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.22 (0.85, 1.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.86 (0.91, 3.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school graduate or higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.37 (1.03, 1.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.65 (0.93, 2.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.966\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDivorced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.02 (0.54, 1.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.957\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.15 (0.72, 6.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1.74 (1.03, 2.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.02, 1.72)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.84 (1.00, 3.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.98 (0.18, 5.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.915\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.48 (1.11, 1.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.81 (1.02, 3.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.32 (0.93, 1.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.84 (0.90, 3.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrinking status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.220\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.36 (0.94, 1.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.19 (0.61, 2.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.30 (0.98, 1.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.28 (1.22, 4.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e​Underweight\u0026thinsp;\u0026lt;\u0026thinsp;18.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.35 (0.10, 1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.08 (0.00, 1.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal (\u0026ge;\u0026thinsp;18.5, \u0026lt;\u0026thinsp;25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.97 (0.96, 4.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.69 (0.75, 3.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObese (\u0026ge;\u0026thinsp;30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.36 (0.94, 1.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.94 (1.09, 3.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverweight (\u0026ge;\u0026thinsp;25, \u0026lt;\u0026thinsp;30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.96 (1.06, 3.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.58 (0.69, 3.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.47 (1.10, 1.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.97 (1.20, 3.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.21 (0.56, 2.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.97 (0.33, 2.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.953\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHyperlipidemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.876\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.915\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.46 (1.01, 2.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.62 (0.93, 2.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.36 (0.89, 2.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.76 (0.83, 3.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.958\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.34 (0.95, 1.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.69 (0.98, 2.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.81 (1.07, 3.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.74 (0.80, 3.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCVD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.754\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.57 (1.06, 2.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.23 (0.75, 2.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.58 (1.06, 2.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.28 (1.40, 3.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypotension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.944\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.667\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.48 (1.06, 2.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.18 (0.52, 2.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.685\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.36 (0.79, 2.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.91 (1.11, 3.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003e1\u003c/sup\u003eMultiple Cox regression model adjusted for Age, sex, BMI, Education, Marital, Smoking status, drinking status, Time on dialysis, Primary kidney disease, hypertension, diabetes, hyperlipidemia, CVD, hypotension, eGFR, Albuminuria.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eAbbreviations: SHR, stress-induced hyperglycemia ratio; BMI, body mass index; eGFR, estimated glomerular filtration rate; CVD, cardiovascular disease.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we identified a robust, independent association between the SHR, a composite indicator of acute glycemic response relative to chronic glycemia, and both all-cause and cardiovascular mortality in patients undergoing maintenance hemodialysis. After adjustment for a comprehensive set of demographic, clinical, and biochemical factors, SHR demonstrated a U-shaped relationship with mortality outcomes. Specifically, mortality risk increased markedly when SHR exceeded 0.804 for all-cause mortality and 0.817 for cardiovascular mortality. These nonlinear associations underscore the prognostic utility of SHR and suggest that deviations from an optimal glycemic range, whether hypo- or hyperglycemic, may portend worse outcomes in this high-risk population. Our findings support the integration of SHR into routine risk stratification strategies, offering a pragmatic biomarker to guide clinical decision-making in the management of hemodialysis patients.\u003c/p\u003e\u003cp\u003eStress-induced hyperglycemia represents a transient metabolic response to acute physiological stress, driven predominantly by surges in counter-regulatory hormones such as cortisol and catecholamines[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. While this adaptive response may serve to ensure adequate energy supply during critical illness, mounting evidence suggests that stress-induced hyperglycemia exerts numerous deleterious effects, including promotion of a pro-inflammatory state, destabilization of atherosclerotic plaques, and activation of the sympathoadrenal axis, characterized by elevated epinephrine and norepinephrine levels[\u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Consistent with these pathophysiological mechanisms, the SHR\u0026mdash;a refined index capturing acute glycemic excursions relative to chronic glycemic status\u0026mdash;has emerged as a robust prognostic marker. Prior studies have linked elevated SHR with increased risks of pulmonary infection, advanced coronary artery disease, and thrombotic burden. More importantly, SHR has demonstrated predictive value for all-cause mortality in patients with acute myocardial infarction and acute decompensated heart failure, irrespective of baseline diabetes status[\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These findings highlight the clinical utility of SHR as a simple yet powerful tool for early identification of high-risk individuals in a variety of acute care settings.\u003c/p\u003e\u003cp\u003eCurrently, studies investigating the relationship between SHR and mortality in patients with HD are limited. In our HD cohort, we identified a significant U-shaped association between the SHR and all-cause mortality. Using RCS analysis, the lowest mortality risk was observed at an SHR of approximately 0.804. Deviations from this threshold\u0026mdash;both lower and higher SHR values\u0026mdash;were associated with a marked increase in mortality risk. A low SHR may reflect impaired physiological stress response or underlying metabolic compromise, whereas a high SHR likely indicates stress-induced hyperglycemia and chronic metabolic dysregulation, both contributing to adverse outcomes. This non-linear association underscores the potential clinical utility of maintaining SHR within an optimal range to reduce mortality risk in HD patients. These findings are consistent with prior research in other clinical populations. For instance, Li et al. reported a U-shaped relationship between SHR and both in-hospital and 1-year mortality in critically ill patients in a cardiac intensive care unit, with the lowest risk observed in the SHR range of 0.75\u0026ndash;0.99 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Similarly, Yan et al. demonstrated a U-shaped association between SHR and all-cause mortality in female patients with diabetes mellitus [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In addition, Gregory et al. found that both low (\u0026lt;\u0026thinsp;6.0%) and high (\u0026gt;\u0026thinsp;9.0%) hemoglobin A1c levels were linked to increased mortality, further supporting the importance of maintaining glycemic indices within a physiological range [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Collectively, these findings highlight SHR as a valuable biomarker for risk stratification and personalized management in the HD population.\u003c/p\u003e\u003cp\u003eIn terms of cardiovascular mortality, we identified a distinct U-shaped association between the SHR and cardiovascular mortality in patients with hemodialysis. Moderate elevations in SHR, typically indicative of a physiological stress response, were associated with the lowest cardiovascular risk\u0026mdash;potentially reflecting an adequate metabolic adaptation to acute stress. In contrast, both low and high extremes of SHR were linked to significantly elevated cardiovascular mortality, with risk rising sharply when SHR exceeded 0.817. This may be attributed to the deleterious cardiovascular effects of persistent hyperglycemia, including endothelial dysfunction, pro-inflammatory states, and accelerated atherosclerosis, particularly in patients with underlying cardiovascular disease[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Our findings align with those of Yang et al., who reported a U-shaped relationship between SHR and major adverse cardiovascular events in patients with acute coronary syndrome[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Similarly, a meta-analysis by Karakasis et al. demonstrated a strong association between elevated SHR and both major adverse cardiovascular and cerebrovascular events and overall mortality across diverse clinical populations, reinforcing SHR\u0026rsquo;s prognostic utility [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Additionally, prior studies in patients with chronic kidney disease (CKD) and diabetic kidney disease have reported comparable nonlinear associations[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Collectively, these findings underscore the importance of recognizing SHR as a dynamic and nonlinear risk marker in HD patients. Maintaining SHR within an optimal range may offer a novel approach to improving cardiovascular outcomes in this high-risk population.\u003c/p\u003e\u003cp\u003eThe SHR may influence the clinical prognosis of HD patients through multiple complex biological mechanisms. An elevated SHR typically reflects significant blood glucose fluctuations under stress conditions, indicating impaired glycemic control[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. More importantly, it serves as a marker of heightened metabolic and immune system dysregulation, both of which are closely associated with poor outcomes in HD patients[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Firstly, persistent hyperglycemia impairs insulin sensitivity and is closely linked to increased secretion of stress hormones such as catecholamines, cortisol, and glucagon[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. These hormones not only raise blood glucose levels but also enhance oxidative stress, promote atherosclerosis, and cause endothelial dysfunction\u0026mdash;all of which accelerate the development of CVD[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Moreover, elevated SHR may activate the renin\u0026ndash;angiotensin\u0026ndash;aldosterone system (RAAS) and trigger the release of pro-inflammatory cytokines, thereby intensifying systemic inflammation and contributing to further injury to the cardiovascular and renal systems[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In addition, hyperglycemia has been associated with impaired fibrinolysis due to increased levels of plasminogen activator inhibitor-1 (PAI-1), which reduces fibrinolytic activity and heightens the risk of thrombotic events[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In HD patients, these mechanisms are particularly detrimental. Given their already high cardiovascular burden, acute blood glucose fluctuations may trigger vascular wall stress, aggravate myocardial ischemia, and precipitate arrhythmias, leading to an increased risk of cardiovascular mortality[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Compounding this issue, HD patients often exhibit immunosuppression, and elevated SHR may further impair immune defense mechanisms, increasing susceptibility to infections\u0026mdash;a leading cause of all-cause mortality in this population[\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Furthermore, elevated SHR is frequently associated with malnutrition, chronic inflammation, and metabolic derangements, all of which are known predictors of poor prognosis in HD patients. Therefore, precise glycemic control and mitigation of glucose variability are crucial for improving survival and reducing cardiovascular mortality in this vulnerable group.\u003c/p\u003e\u003cp\u003eThis study highlights the clinical relevance of the stress hyperglycemia ratio (SHR) as a novel and independent prognostic biomarker in hemodialysis (HD) patients. Elevated SHR was strongly associated with increased risks of all-cause and cardiovascular mortality, even after adjusting for multiple confounders. The observed U-shaped relationship suggests that both low and high SHR levels may be detrimental, underscoring the importance of maintaining optimal glucose regulation during stress. SHR may offer superior prognostic value compared to traditional glycemic markers in this population, reflecting underlying metabolic instability, inflammation, and cardiovascular burden. Its association with key risk factors such as hypertension, diabetes, and albuminuria further supports its clinical utility. Integrating SHR into routine assessment could enhance risk stratification and guide individualized glycemic management to improve outcomes in HD patients.\u003c/p\u003e\u003cp\u003eThis study has several limitations that should be acknowledged. First, the observational design precludes causal inference; although SHR was significantly associated with mortality, we cannot determine a direct cause-effect relationship. Second, SHR was calculated using a single FPG and HbA1c measurement at baseline, which may not fully capture dynamic changes in glucose levels over time. Third, residual confounding is possible despite adjustments for numerous clinical and biochemical variables, particularly regarding unmeasured factors such as nutritional status, inflammation markers, and insulin therapy details. Fourth, the study population was limited to a single cohort of hemodialysis patients, which may restrict the generalizability of findings to other dialysis modalities or non-dialysis CKD populations. Finally, the retrospective nature of the analysis may introduce selection bias. Future prospective and multicenter studies are needed to validate these findings and explore whether interventions targeting SHR can improve clinical outcomes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the stress SHR is a valuable and independent predictor of both all-cause and cardiovascular mortality in patients undergoing hemodialysis. Our findings demonstrate a significant U-shaped association between SHR and mortality risk, suggesting that both inadequate and excessive glycemic responses to stress may adversely affect patient outcomes. SHR not only reflects glycemic fluctuations under stress but also captures broader metabolic and inflammatory disturbances common in HD patients. As such, it may serve as a practical and informative biomarker for risk assessment and clinical decision-making. Incorporating SHR into routine clinical evaluation could improve prognostic accuracy and support more targeted glycemic control strategies, ultimately contributing to better survival and cardiovascular outcomes in this high-risk population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe would like to thank all those who helped with this study.\u003c/p\u003e\n\u003cp\u003eAuthors contributions\u003c/p\u003e\n\u003cp\u003eZH and TX were responsible for conducting the study, collecting data, and drafting the initial manuscript. They also jointly performed the statistical analyses and contributed to the study design. Additionally, both authors were involved in data acquisition, analysis, interpretation, and the preparation of the final manuscript. All authors reviewed and approved the final version.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eNo funding.\u003c/p\u003e\n\u003cp\u003eData availability statement\u003c/p\u003e\n\u003cp\u003eAccess can be granted upon reasonable request and approval by the correspondence author.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eNo conflicts of interest.\u003c/p\u003e\n\u003cp\u003eEthics declarations\u003c/p\u003e\n\u003cp\u003eHuman Ethics and Consent to Participate declarations\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthics Committee of\u0026nbsp;Jiangyin People\u0026rsquo;s Hospital approved the study (20240252), Written informed consent was obtained from all study subjects in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAuthors contributions\u003c/p\u003e\n\u003cp\u003eHongyu Zhou and Xiaodong Tang contributed substantially to the conception and design of the study, data acquisition and analysis, drafting of the manuscript, and critical revision for important intellectual content. All authors reviewed and approved the final version of the manuscript for publication.\u003c/p\u003e\n\u003cp\u003eCorresponding author\u003c/p\u003e\n\u003cp\u003eCorrespondence to Xiaodong Tang.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTakahashi S, Tanaka T, Suzuki Y, Yoshida N, Hitaka M, Ishii S, Yamazaki K, Masai M, Yamada Y, Ohashi Y: \u003cstrong\u003eAssociation Between Malnutrition, Low Muscle Mass, Elevated NT-ProBNP Levels, and Mortality in Hemodialysis Patients\u003c/strong\u003e. \u003cem\u003eNutrients \u003c/em\u003e2025, \u003cstrong\u003e17\u003c/strong\u003e(11).\u003c/li\u003e\n\u003cli\u003eLe D, Singh R, Kim B, Greer RC, Grams ME, Jaar BG: \u003cstrong\u003ePrimary Care Use and Clinical Outcomes Among Patients Initiating Hemodialysis\u003c/strong\u003e. \u003cem\u003eAmerican journal of kidney diseases : the official journal of the National Kidney Foundation \u003c/em\u003e2025.\u003c/li\u003e\n\u003cli\u003eLim JH, Seo YJ, 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patient\u003c/strong\u003e. \u003cem\u003eJournal of infection in developing countries \u003c/em\u003e2024, \u003cstrong\u003e18\u003c/strong\u003e(7):1157-1160.\u003c/li\u003e\n\u003cli\u003eLibetta C, Sepe V, Esposito P, Galli F, Dal Canton A: \u003cstrong\u003eOxidative stress and inflammation: Implications in uremia and hemodialysis\u003c/strong\u003e. \u003cem\u003eClinical biochemistry \u003c/em\u003e2011, \u003cstrong\u003e44\u003c/strong\u003e(14-15):1189-1198.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"stress hyperglycemia ratio, hemodialysis, cardiovascular disease mortality, all-cause mortality","lastPublishedDoi":"10.21203/rs.3.rs-7204612/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7204612/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives:\u003c/h2\u003e\u003cp\u003eTo evaluate the prognostic value of the stress hyperglycemia ratio (SHR) in hemodialysis (HD) patients, focusing on its association with all-cause and cardiovascular mortality.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective, longitudinal cohort study involving 1,306 HD patients from July 2017 to July 2022. The primary outcome was all-cause mortality, while cardiovascular disease (CVD) mortality was assessed as a secondary outcome. Cox proportional hazards models and Kaplan\u0026ndash;Meier survival curves were employed to evaluate the association between SHR and mortality. Additionally, restricted cubic spline (RCS) analyses were performed to explore the non-linear relationship, and an iterative algorithm was used to identify inflection points.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eDuring a median follow-up of 62 months, 464 all-cause deaths (35.5%) and 192 CVD-related deaths were observed. A U-shaped association was identified between SHR and both all-cause and CVD mortality, with inflection points at 0.804 and 0.817, respectively. For all-cause mortality, the adjusted hazard ratios (HRs) were 0.63 (95% CI: 0.04\u0026ndash;0.78) for SHR\u0026thinsp;\u0026lt;\u0026thinsp;0.86 and 2.73 (95% CI: 1.78\u0026ndash;4.18) for SHR\u0026thinsp;\u0026ge;\u0026thinsp;0.86. For CVD mortality, the corresponding HRs were 0.57 (95% CI: 0.04\u0026ndash;0.87) and 2.70 (95% CI: 1.76\u0026ndash;4.14). Subgroup analysis revealed a significant interaction between SHR and body mass index (BMI) in relation to cardiovascular mortality.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e\u003cp\u003eA U-shaped association exists between SHR and both all-cause and cardiovascular mortality in HD patients. These findings suggest that SHR may serve as a useful prognostic biomarker for risk stratification and may inform individualized glycemic management strategies in the HD population.\u003c/p\u003e","manuscriptTitle":"The Prognostic Value of Stress Hyperglycemia Ratio in Hemodialysis Patients: A U-Shaped Association with Mortality","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-10 09:24:51","doi":"10.21203/rs.3.rs-7204612/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"af7ed3b9-38c4-4cef-8ef7-cafe0861c4c3","owner":[],"postedDate":"September 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-10T09:24:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-10 09:24:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7204612","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7204612","identity":"rs-7204612","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}