Associations between age and the hemoglobin glycation index and 30-day and 1-year mortality in ischemic stroke patients: Mediation analyses and machine learning in a cohort study

Associations between age and the hemoglobin glycation index and 30-day and 1-year mortality in ischemic stroke patients: Mediation analyses and machine learning in a cohort study | 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 Article Associations between age and the hemoglobin glycation index and 30-day and 1-year mortality in ischemic stroke patients: Mediation analyses and machine learning in a cohort study Xinyu Tong, Jianxiong Gu, Chuxin Lyu, Yichun Zhao, Ying Rui, Minjie Guo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6680914/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Objective To investigate the associations between both age and the hemoglobin glycation index (HGI) and the 30-day and 1-year mortality in ischemic stroke (IS) patients and to analyze the mediating effect of the HGI on the relationship between age and mortality. Methods A total of 3269 hospitalized patients with IS included in the Medical Information Mart for Intensive Care (MIMIC)-IV database were included in this study. The effects of age and HGI on short- (30 days) and long-term (1 year) mortality were analyzed with logistic, Cox, and least absolute shrinkage and selection operator (LASSO) regression analysis. The nonlinear relationship among the variables was further investigated via restriction cubic spline (RCS) analysis, and the mediating effects of HGI on the age-mortality relationship were confirmed via mediation analysis. Kaplan–Meier (K–M) survival curves and restricted mean survival time (RMST) analyses were used to evaluate the differences in survival among patients with different HGI levels. Finally, multiple machine learning (ML) models were constructed and subsequently evaluated in terms of predictive performance. Results Logistic and Cox regression analyses revealed that a lower HGI and a greater age were significantly associated with higher risks of 30-day and 1-year mortality (both P < 0.001). RCS analysis revealed a J-shaped relationship between HGI and mortality risk. Mediation analysis revealed that HGI had a negative mediating effect on the relationship between age and mortality. K–M curve and RMST analyses further revealed that patients with higher HGIs had greater probabilities of survival. ML models also confirmed the importance of HGI in predicting the risk of mortality. Conclusion Age and HGI are correlated with both the 30-day and 1-year risks of mortality in IS patients. The HGI may play a partial mediating role between age and the risk of mortality. Biological sciences/Neuroscience Health sciences/Neurology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Ischemic stroke (IS) is the most common type of stroke worldwide, accounting for 68% of all cases. According to the 2020 Global Burden of Disease study, IS affects 7.59 million individuals and is a leading cause of death, accounting for 49% of stroke deaths worldwide [ 1 ] . Although preventive measures and treatments for the disease continue to be updated, the high morbidity and mortality of IS position it as a major threat to human health. Therefore, greater attention should be paid to the prognostic management of IS patients in clinical research. The main risk factors for IS include hypertension (HTN), atrial fibrillation, smoking, drinking, hyperlipidemia (HLD), and most importantly, diabetes. Controlling blood glucose levels and improving the status of other related risk factors (such as blood pressure and blood lipids) are key to preventing IS and improving patient outcomes [ 2 ] . The concept of the hemoglobin glycation index (HGI) was first proposed in 2002 by Hempe et al. [ 3 ] , who defined it as the difference between actual glycated hemoglobin (HbA1c) and the HbA1c predicted from linear regression analysis of the fasting plasma glucose (FPG) (HGI = actual HbA1c - predicted HbA1c). This index can more accurately evaluate an individual’s blood glucose control than other methods. A high HGI has been shown to be a risk factor for adverse cardiovascular diseases [ 4 – 6 ] . In the ACCORD trial, patients with high HGI were predicted to be at increased cardiovascular disease risk due to excessive hypoglycemia, whereas patients with low HGI were expected to benefit more from the use of treatment regimens for cardiovascular diseases [ 7 ] . This difference suggests that the HGI not only is a sign of blood glucose control but also may be closely related to the development of other complications. A study of patients with type 2 diabetes mellitus (T2DM) and acute IS revealed a U-shaped association between HGI and patient outcomes; that is, both low and high HGI levels were associated with increased risks of developing adverse outcomes [ 8 ] . However, another study based on data from the Medical Information Mart for Intensive Care (MIMIC)-IV database revealed that a low HGI was always associated with poor outcomes in patients with severe IS, whereas a high HGI may be a protective factor in the short term but may increase the risk of death in the long term [ 9 ] . The HGI could serve as a new predictor of all-cause mortality in IS patients by integrating the quality of blood glucose control, the extent of insulin resistance and individual biological differences. However, most studies on the HGI have focused on critically ill patients, and studies on the relationship between the HGI and the outcomes of IS patients are still rare. Therefore, this study used the data from the MIMIC-IV database to construct a linear regression equation to calculate the HGI and analyzed the associations between this index and mortality in hospitalized IS patients with the aim of providing new insights for prognostic assessment and improved clinical management of IS patients. Methods Data source In this study, data from the MIMIC-IV (version 3.1) were used [ 10 ] . MIMIC-IV is an extensive and deidentified collection of health records containing information on more than 65,000 intensive care unit (ICU) patients and more than 200,000 emergency department visits in Boston, Massachusetts, USA, between 2008 and 2022 [ 11 ] . After successful application, the author (Chuxin Lyu) obtained access to the MIMIC-IV database and completed the training initiative project at the partner institution (Certification Number: 61738903). Study population Inpatients diagnosed with IS were identified from the MIMIC-IV database according to International Classification of Diseases (ICD) codes [ 12 ] (see Supplementary Table S1 for the ICD-9 and ICD-10 codes of the diseases of interest). Notably, the included IS patients were not necessarily newly diagnosed at the time of hospitalization, and IS was not always the main reason for their hospitalization. Some patients, for example, may have been admitted to the hospital for other reasons and been diagnosed with IS during hospitalization. For patients with multiple admissions, only the data from the first hospitalization were included for analysis. The inclusion criteria were as follows (Fig. 1): Age ≥ 18 years on first admission to the hospital; ICU stay ≥ 48 hours The exclusion criteria were as follows: <80% data completeness; Missing FPG or HbA1c data; Extreme data values. Data extraction and definitions The baseline characteristics of the patients were extracted from the database with PostgreSQL software. Potential confounding variables included the following: 1. Demographic characteristics: age and sex; 2. Complications: HTN, chronic kidney disease (CKD), HLD, T2DM, ischemic heart disease (IHD), and chronic obstructive pulmonary disease (COPD); 3. Laboratory test data: levels of urea nitrogen (BUN), creatinine (Cr), glycerol triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), FBG, and HbA1c; white blood cell (WBC) count and platelet (PLT) count; and hemoglobin (Hb) level. For laboratory data, the first recorded values after admission were considered for analysis. The HGI was calculated via the method proposed by Hempe et al. [ 7 ] , in which the baseline FBG and HbA1c data of all individuals were used to assess the linear relationship between FBG and HbA1c in the study group. The predicted HbA1c was calculated from the included samples (predicted HbA1c = 0.0082*FPG + 4.8386), and the difference between the observed HbA1c and predicted HbA1c levels was subsequently calculated as the HGI. The relationship between the HGI and HbA1c level is shown in Fig. 2. The mortality rate was calculated from the first day of hospitalization. The primary endpoint of this study was the 30-day and 1-year mortality rates of IS patients. Statistical analysis Categorical variables are presented as percentages. Continuous variables were first subjected to a normality test; data conforming to a normal distribution are expressed as the mean and standard deviation (SD), whereas for data that did not conform to a normal distribution, the median and interquartile range (IQR) are used to describe the central tendency and variability. Appropriate statistical tests, such as Student's t test, the Mann-Whitney U test, the chi-square test, and Fisher's exact test, were used for between-group comparisons. Logistic regression analysis and Cox proportional hazards regression analysis were used to investigate the associations between age and the HGI and mortality. First, univariable analyses were performed for each potential predictive factor. Bonferroni correction was performed for the univariable analyses. Clinically important variables with significant differences after Bonferroni correction were subsequently included in the multivariable analysis. The least absolute shrinkage and selection operator (LASSO) regression method was used to identify factors significantly associated with an outcome of death in IS patients. The factors identified with LASSO regression were used as independent variables in multivariable logistic regression and Cox proportional hazards regression models. The performance of these two multivariable models was evaluated via receiver operating characteristic (ROC) curve analysis. For survival analysis, Kaplan–Meier (K–M) curves and the restricted mean survival time (RMST) were plotted. Restriction cubic spline (RCS) curves were used to study potential nonlinear relationships among age, the HGI and mortality. Mediation analysis was performed to determine whether HGI had a mediating role in the relationship between the exposure variable (age) and mortality. To increase the robustness of the analysis, bootstrapping with 1000 resamplings was performed. The results included the size of the indirect path effect, the proportion of the mediating effect, and the related p values. Owing to missing values for some variables and the nonnormal distribution of many variables, the median imputation method was used to process missing data. Variables with excessive missing data were excluded from the analysis. All the statistical analyses were performed with R software (version 4.4.1). A P value of less than 0.05 (two-sided) was considered to indicate statistical significance. The results of the multivariable logistic regression and Cox regression analyses are expressed as odds ratios (ORs) or hazard ratios (HRs) and 95% confidence intervals (CIs). Machine learning (ML) Variables selected by LASSO analysis were incorporated into a machine learning (ML) algorithm. The dataset was divided into a training set and a test set at a ratio of 7:3. The training set was used for model construction, and the test set was used for model evaluation. The support vector machine (SVM), extreme gradient boosting (XGB), random forest (RF) and decision tree (DT) algorithms were used to construct models on the basis of the selected variables and predict the 30-day mortality risk of IS patients. During model development, the optimal hyperparameters were set. The ROC curves and the corresponding area under the curve (AUC) were used to evaluate the model performance. The clinical practicality of the models was evaluated with decision curve analysis (DCA). The performance of the best model in the test set was further interpreted via the SHapley Additive exPlanations (SHAP) value, which provides insight into the importance and ranking of each variable included in the model. The SHAP value can clearly and intuitively reveal the positive or negative impact of each variable on the model prediction, and the screening threshold was set to 0.05. In addition, the performance of the optimal model was evaluated via bootstrapping. Results Baseline patient characteristics A total of 3269 IS patients were included in this study. Table 1 shows the baseline characteristics of the participants at the 30-day and 1-year follow-ups. In this cohort, compared with the 30-day survival group, the patients in the 30-day nonsurvival group were significantly older, and the incidences of CKD and IHD were significantly greater. In terms of laboratory indicators, compared with the 30-day survival group, the 30-day nonsurvival group demonstrated a significantly lower HGI, significantly greater levels of BUN, Cr, and FBG, a significantly greater WBC count, and significantly lower LDL-C and Hb levels. Similar trends were observed at the 1-year follow-up results. Specifically, patients in the 1-year nonsurvival group were older than those in the survival group. The incidences of CKD, IHD, and COPD in the nonsurvival group were significantly greater than those in the survival group, whereas the prevalence of HTN was significantly lower than that in the survival group. In terms of laboratory indicators, compared with the survival group, the nonsurvival group again demonstrated a significantly lower HGI, significantly greater levels of BUN, Cr, and FBG, a significantly lower WBC count, and significantly lower LDL-C and Hb levels. Table 1 Patient baseline information Variables 30-day follow-up 1-year follow-up Survivors ( n = 2930) Nonsurvivors ( n = 339) P -value Survivors ( n = 2609) Nonsurvivors ( n = 660) P -value Age, years 70 (59–81) 82 (71–89) < 0.0001 69 (58–79) 81 (71–88) < 0.0001 Male, n (%) 1522 (51.95) 150 (44.25) 0.0086 1382 (52.97) 290 (43.94) < 0.0001 HTN (%) 1735 (59.22) 183 (53.98) 0.0728 1571 (60.21) 347 (52.58) 0.0004 CKD (%) 264 (9.01) 72 (21.24) < 0.0001 200 (7.67) 136 (20.61) < 0.0001 HLD (%) 1360 (46.42) 155 (45.72) 0.8533 1213 (46.49) 302 (45.76) 0.7682 T2DM (%) 560 (19.11) 73 (21.53) 0.3195 492 (18.86) 141 (21.36) 0.1614 IHD (%) 644 (21.98) 109 (32.15) < 0.0001 531 (20.35) 222 (33.64) < 0.0001 COPD (%) 245 (8.36) 37 (10.91) 0.1382 191 (7.32) 91 (13.79) < 0.0001 HGI -0.0374 (-0.3243-0.2758) -0.1604 (-0.4439-0.1699) < 0.0001 -0.039 (-0.3226-0.2692) -0.102 (-0.404-0.2599) 0.0031 BUN, mg/dL 16 (12–21) 21 (15–30) < 0.0001 15 (12–20) 20 (14–29) < 0.0001 Cr, mg/dL 0.9 (0.7–1.1) 1 (0.8–1.4) < 0.0001 0.9 (0.7–1.1) 1 (0.7–1.3) < 0.0001 TG, mg/dL 106 (76.25–146) 106 (74-154.5) 0.0249 106 (77–146) 101.5 (74-143.25) 0.0924 LDL-C, mg/dL 93 (69–119) 74 (52.5–103) < 0.0001 94 (70–121) 77.5 (56–104) < 0.0001 FBG, mg/dL 106 (93–126) 123 (102–144) < 0.0001 105 (93–125) 117 (98-140.25) < 0.0001 HbA1c, % 5.7 (5.4-6) 5.7 (5.3-6) 0.2745 5.7 (5.4-6) 5.7 (5.4–6.1) 0.4969 WBC, K/µL 8.2 (6.4-10.575) 10.4 (8.1-13.75) < 0.0001 8.1 (6.4–10.4) 9.7 (7.6–12.9) < 0.0001 PLT, K/µL 215 (175–266) 204 (160-262.5) 0.0896 216 (177–266) 208 (160–268) 0.0637 Hb, g/dl 12.8 (11.5–14) 11.5 (10.1–12.8) < 0.0001 12.9 (11.6–14) 11.65 (10.2–13) < 0.0001 Primary outcomes of logistic regression analyses Analysis of factors associated with 30-day mortality Univariable and multivariable logistic regression analyses (Table 2 ) both revealed that age (univariable: OR = 1.06, 95% CI: 1.05–1.07, P < 0.0001; multivariable: OR = 1.05, 95% CI: 1.04–1.07, P < 0.0001) was positively associated with 30-day mortality, whereas the HGI (univariable: OR = 0.57, 95% CI: 0.46–0.71, P < 0.0001; multivariable: OR = 0.44, 95% CI: 0.34–0.57, P < 0.0001) was negatively associated with 30-day mortality. Table 2 Logistic analyses: risk factors of mortality Variables Univariable logistic regression analysis Multivariable logistic regression analysis LASSO-logistic regression analysis OR (95% CI) P -value* OR (95% CI) P -value* OR (95% CI) P -value 30-day follow-up Age 1.06 (1.05–1.07) < 0.0001 1.05 (1.04–1.07) < 0.0001 1.05 (1.04–1.06) < 0.0001 Sex 0.73 (0.59–0.92) 0.0074 1.11 (0.85–1.45) 0.4371 HTN 0.81 (0.64–1.01) 0.0643 0.98 (0.74–1.31) 0.8813 CKD 2.72 (2.03–3.62) < 0.0001 1.08 (0.72–1.60) 0.7018 T2DM 1.16 (0.88–1.52) 0.2858 1.45 (1.03–2.01) 0.0296 HLD 0.97 (0.78–1.22) 0.8084 0.90 (0.70–1.15) 0.3979 IHD 1.68 (1.31–2.14) < 0.0001 1.04 (0.78–1.36) 0.8021 COPD 1.34 (0.92–1.91) 0.1142 1.02 (0.68–1.49) 0.9387 HGI 0.57 (0.46–0.71) < 0.0001 0.44 (0.34–0.57) < 0.0001 0.51 (0.40–0.65) < 0.0001 Hb 0.77 (0.73–0.81) < 0.0001 0.86 (0.81–0.92) < 0.0001 0.85 (0.80–0.90) < 0.0001 PLT 1.00 (0.99-1.00) 0.0887 0.99 (0.99-1.00 0.8290 WBC 1.05 (1.03–1.07) < 0.0001 1.04 (1.02–1.07) 0.0002 1.05 (1.03–1.07) 0.0001 LDL 0.99 (0.98–0.99) < 0.0001 0.99 (0.99-1.00) 0.1900 TTW 1.00 (1.00-1.01) 0.0332 1.00 (1.00-1.01) 0.0009 Cr 1.20 (1.10–1.30) < 0.0001 0.84 (0.70–0.98) 0.0474 BUN 1.04 (1.03–1.04) < 0.0001 1.02 (1.02–1.04) < 0.0001 1.02 (1.02–1.03) < 0.0001 1-year follow-up Age 1.06 (1.05–1.07) < 0.0001 1.07 (1.06–1.08) < 0.0001 1.07 (1.06–1.07) < 0.0001 Sex 0.70 (0.59–0.83) < 0.0001 1.03 (0.84–1.28) 0.7348 HTN 0.73 (0.62–0.87) 0.0004 0.82 (0.66–1.03) 0.0864 0.80 (0.65-1.00) 0.0541 CKD 3.13 (2.46–3.96) < 0.0001 1.16 (0.83–1.60) 0.3884 1.12 (0.81–1.54) 0.5014 T2DM 1.17 (0.94–1.44) 0.1458 1.08 (0.82–1.41) 0.5913 HLD 0.97 (0.82–1.15) 0.8084 0.85 (0.70–1.04) 0.1138 IHD 1.98 (1.64–2.39) < 0.0001 1.17 (0.94–1.46) 0.1646 1.16 (0.94–1.45) 0.1636 COPD 2.02 (1.55–2.63) < 0.0001 1.47 (1.09–1.97) 0.0115 1.46 (1.09–1.96) 0.0113 HGI 0.79 (0.67–0.92) 0.0031 0.69 (0.56–0.84) 0.0003 0.70 (0.58–0.83) 0.0001 Hb 0.76 (0.72–0.79) < 0.0001 0.84 (0.79–0.88) < 0.0001 0.84 (0.80–0.89) < 0.0001 PLT 0.99 (0.99-1.00) 0.0637 0.99 (0.99-1.00) 0.2008 WBC 1.09 (1.07–1.11) < 0.0001 1.10 (1.08–1.13) < 0.0001 1.10 (1.08–1.13) < 0.0001 LDL 0.99 (0.98–0.99) < 0.0001 0.99 (0.99-1.00) 0.0899 0.99 (0.99–0.99) 0.0478 TTW 1.00 (0.99–1.01) 0.1011 1.00 (1.00–1.00) 0.0011 1.00 (1.00–1.00) 0.0010 Cr 1.24 (1.14–1.35) < 0.0001 0.95 (0.82–1.07) 0.3969 BUN 1.04 (1.03–1.05) < 0.0001 1.02 (1.01–1.03) 0.0007 1.02 (1.01–1.02) 0.0001 LASSO regression analysis confirmed that age, the HGI, Hb level, WBC count, and BUN level were significant predictors of 30-day and 1-year mortality. A multivariable logistic regression model was subsequently constructed, and the results revealed that age (OR = 1.05, 95% CI: 1.04–1.06, P < 0.0001) was positively correlated with 30-day mortality, whereas the HGI (OR = 0.51, 95% CI: 0.40–0.65, P < 0.0001) was inversely correlated with 30-day mortality. Analysis of the factors associated with 1-year mortality Both univariable and multivariable logistic regression analyses revealed that age (univariable: OR = 1.06, 95% CI: 1.05–1.07, P < 0.0001; multivariable: OR = 1.07, 95% CI: 1.06–1.08, P < 0.0001) was positively correlated with 1-year mortality, whereas the HGI (univariable: OR = 0.79, 95% CI: 0.67–0.92, P = 0.0031; multivariable: OR = 0.69, 95% CI: 0.56–0.84, P = 0.0003) was negatively correlated with 1-year mortality. LASSO logistic regression analysis further confirmed that age (OR = 1.07, 95% CI: 1.06–1.07, P < 0.0001) and the presence of COPD (OR = 1.46, 95% CI: 1.09–1.96, P = 0.0113) were positively correlated with 1-year mortality, whereas the HGI (OR = 0.70, 95% CI: 0.58–0.83, P = 0.0001) was negatively correlated with 1-year mortality. Stratified analysis of the HGI and its relationship with mortality risk To further investigate the association between HGI and mortality risk, the HGI was subject to stratified analysis by quartile (Q1-Q4) (Table 3 ). In Model 1, there was no adjustment for any covariates; in Model 2, the model was adjusted for age; and in Model 3, the model was adjusted for age, Hb level, WBC count, and BUN level. Compared with the Q1 group (-0.55 - -0.33), in the Q3 (0.09–0.27) and Q4 groups (0.27–2.13), the 30-day mortality risk was significantly reduced [Q3 group, OR = 0.53 (95% CI: 0.38–0.73, P < 0.0001) in Model 1, OR = 0.40 (95% CI: 0.29–0.56, P < 0.0001) in Model 2 with the variable adjustment, and OR = 0.50 (95% CI: 0.35–0.71, P < 0.0001) in Model 3; Q4 group, OR = 0.60 (95% CI: 0.44–0.82, P = 0.0017) in Model 1, OR = 0.47 (95% CI: 0.34–0.65, P < 0.0001) in Model 2, and OR = 0.51 (95% CI: 0.36–0.71, P < 0.0001) in Model 3]. A trend test revealed that as the HGI increased (i.e., from the Q1 group to Q4 group), the mortality risk decreased significantly (p for trend < 0.0001). A similar pattern was observed at the 1-year follow-up, i.e., as the HGI increased (i.e., from the Q1 group to Q4 group), the mortality risk tended to decrease. Table 3 The association between HGI levels and mortality by logistic regression analyses Medel 1 OR (95% CI) P -value Medel 2 OR (95% CI) P -value Medel 3 OR (95% CI) P -value 30-day follow-up HGI 0.57 (0.46–0.71) < 0.0001 0.46 (0.36–0.58) < 0.0001 0.51 (0.41–0.65) < 0.0001 HGI (quartile) Q1 -0.55 (-1.74 - -0.33) reference reference reference Q2 -0.18 (-0.33 - -0.05) 0.83 (0.62–1.11) 0.2047 0.74 (0.54-1.00) 0.0516 0.93 (0.68–1.28) 0.6705 Q3 0.09 (-0.05-0.27) 0.53 (0.38–0.73) 0.0001 0.40 (0.29–0.56) < 0.0001 0.50 (0.35–0.71) 0.0001 Q4 0.59 (0.272.13) 0.60 (0.44–0.82) 0.0017 0.47 (0.34–0.65) < 0.0001 0.51 (0.36–0.71) 0.0001 P for trend < 0.0001 < 0.0001 0.0001 1-year follow-up HGI 0.79 (0.67–0.92) 0.0031 0.63 (0.53–0.75) < 0.0001 0.70 (0.58–0.83) 0.0001 HGI (quartile) Q1 -0.55 (-1.74 - -0.33) reference reference reference Q2 -0.18 (-0.33 -0.05) 0.80 (0.63–1.01) 0.0648 0.69 (0.54–0.89) 0.0038 0.91 (0.70–1.19) 0.5023 Q3 0.09 (-0.05-0.27) 0.63 (0.49–0.81) 0.0001 0.45 (0.35–0.58) < 0.0001 0.58 (0.44–0.76) 0.0001 Q4 0.59 (0.27–2.13) 0.79 (0.62–0.99) 0.0474 0.58 (0.45–0.75) < 0.0001 0.65 (0.50–0.86) 0.0020 P for trend 0.0123 < 0.0001 0.0001 Results of Cox regression analysis To identify prognostic indicators for 30-day and 1-year mortality, univariable and multivariable Cox regression analyses were performed. As shown in Table 4 , in the analysis of 30-day mortality, age (univariable HR = 1.05, 95% CI: 1.04–1.06, P < 0.001; multivariable HR = 1.05, 95% CI: 1.04–1.06, P < 0.001; <0.001) was an independent risk factor, whereas the HGI (univariable: HR = 0.59, 95% CI: 0.48–0.72, P < 0.001; multivariable: HR = 0.47, 95% CI: 0.38–0.59, P < 0.001) was an independent protective factor for the IS patients. For the 1-year mortality risk, Cox regression analysis revealed a similar trend; age (univariable HR = 1.05, 95% CI: 1.05–1.06, P < 0.001; multivariable: HR = 1.05, 95% CI: 1.05–1.06, P < 0.001) was an independent risk factor, whereas the HGI (univariable: HR = 0.79, 95% CI: 0.68–0.91, P = 0.001; multivariable: HR = 0.63, 95% CI: 0.54–0.74, P < 0.001) and Hb level (HR = 0.79, 95% CI: 0.76–0.82, P < 0.001) were independent protective factors for the IS patients. Table 4 Cox analyses: risk factors of mortality Variables Univariable Cox regression analysis Multivariable Cox regression analysis LASSO-Cox regression analysis HR (95% CI) P -value* HR (95% CI) P -value* HR (95% CI) P -value 30-day follow-up Age 1.05 (1.04–1.06) < 0.001 1.05 (1.04–1.06) < 0.001 1.05 (1.04–1.06) < 0.001 Sex 0.76 (0.60–0.92) 0.007 1.09 (0.86–1.38) 0.485 HTN 0.82 (0.66–1.02) 0.070 0.97 (0.75–1.25) 0.803 CKD 2.49 (1.92–3.24) < 0.001 1.05 (0.75–1.46) 0.793 T2DM 1.15 (0.89–1.49) 0.297 1.41 (1.05–1.88) 0.020 HLD 0.98 (0.79–1.21) 0.819 0.88 (0.71–1.10) 0.273 IHD 1.62 (1.29–2.04) < 0.001 1.03 (0.81–1.32) 0.809 COPD 1.32 (0.94–1.86) 0.113 1.02 (0.72–1.44) 0.916 HGI 0.59 (0.48–0.72) < 0.001 0.47 (0.38–0.59) < 0.001 0.54 (0.44–0.66) < 0.001 Hb 0.79 (0.75–0.83) < 0.001 0.89 (0.84–0.94) < 0.001 0.87 (0.82–0.92) < 0.001 PLT 0.99 (0.99-1.00) 0.088 1.00 (0.99-1.00) 0.937 WBC 1.01 (1.01–1.02) < 0.001 1.02 (1.01–1.02) < 0.001 1.01 (1.01–1.02) < 0.001 LDL 0.99 (0.98–0.99) < 0.001 0.99 (0.99-1.00) 0.099 TTW 1.00 (1.00-1.01) 0.015 1.00 (1.00-1.01) < 0.001 Cr 1.15 (1.09–1.21) < 0.001 0.86 (0.75–0.99) 0.041 BUN 1.03 (1.02–1.03) < 0.001 1.02 (1.02–1.03) < 0.001 1.02 (1.01–1.03) < 0.001 1-year follow-up Age 1.05 (1.05–1.06) < 0.001 1.05 (1.05–1.06) < 0.001 1.05 (1.04–1.06) < 0.001 Sex 0.73 (0.62–0.85) < 0.001 1.04 (0.88–1.24) 0.620 HTN 0.76 (0.65–0.89) < 0.001 0.83 (0.69–0.99) 0.042 CKD 2.61 (2.16–3.16) < 0.001 1.02 (0.80–1.30) 0.897 1.21 (0.98–1.51) 0.08 T2DM 1.14 (0.95–1.38) 0.159 1.18 (0.95–1.45) 0.134 HLD 0.97 (0.84–1.13) 0.723 0.88 (0.75–1.03) 0.114 IHD 1.81 (1.54–2.13) < 0.001 1.15 (0.97–1.37) 0.110 COPD 1.80 (1.44–2.26) < 0.001 1.32 (1.05–1.65) 0.016 HGI 0.79 (0.68–0.91) 0.001 0.63 (0.54–0.74) < 0.001 0.68 (0.58–0.78) < 0.001 Hb 0.79 (0.76–0.82) < 0.001 0.88 (0.84–0.92) < 0.001 0.86 (0.83–0.89) < 0.001 PLT 0.99 (0.99-1.00) 0.054 1.00 (0.99-1.00) 0.800 WBC 1.01 (1.01–1.02) < 0.001 1.01 (1.01–1.02) < 0.001 1.01 (1.01–1.02) < 0.001 LDL 0.99 (0.98–0.99) < 0.001 0.99 (0.99-1.00) 0.022 TTW 1.00 (1.00-1.01) 0.044 1.00 (1.00-1.01) < 0.001 Cr 1.24 (1.14–1.35) < 0.001 0.94 (0.85–1.03) 0.178 BUN 1.03 (1.02–1.03) < 0.001 1.02 (1.01–1.02) < 0.001 1.02 (1.01–1.02) < 0.001 After screening the variables with LASSO regression analysis, age (30 days: HR = 1.05, 95% CI: 1.04–1.06, P < 0.001; 1 year: HR = 1.05, 95% CI: 1.04–1.06, P < 0.001) remained an independent risk factor for both 30-day and 1-year mortality for the IS patients, whereas the HGI (30 days: HR = 0.54, 95% CI: 0.44–0.66, P < 0.001; 1 year: HR = 0.68, 95% CI: 0.58–0.78, P < 0.001) and Hb level (HR = 0.87, 95% CI: 0.82–0.92, P < 0.001) remained independent protective factors against 30-day and 1-year mortality for the IS patients. Kaplan–Meier (K–M) survival analysis K–M survival analysis, (curves shown in Fig. 3a), revealed that the HGI was significantly associated with 30-day mortality in IS patients (log-rank test, p < 0.0001). Analysis of the survival curves revealed that, during the 30-day follow-up period, the mortality risk in the low-HGI group was significantly greater than that in the high-HGI group. This survival difference persisted (log-rank test, p < 0.0001) at the 1-year follow-up (Fig. 3b). Moreover, significant differences were observed in both the 30-day and 1-year survival rates between patients in the older and younger groups (log-rank test, P < 0.0001). Specifically, survival curve analysis (Fig. 3c, 3d) revealed that the mortality risk in the older group was significantly greater than that in the younger group. Restricted Mean Survival Time RMST analysis was employed to evaluate the association between the HGI and 30-day and 1-year mortality in IS patients (Fig. 4). Patients were divided into a high-HGI group (Arm 1) and a normal-HGI group (Arm 0) for comparative analysis. During the 30-day follow-up period, the average survival of patients in the high-HGI group was 26.70 days (95% CI: 25.88–27.51), whereas that of patients in the normal-HGI group was 28.17 days (95% CI: 27.95–28.40). The RMST difference between the two groups was − 1.48 days (95% CI: -2.32 - -0.63, p = 0.0006) According to RMST analysis, the 30-day survival of patients in the high-HGI group was significantly shorter than that of patients in the normal-HGI group. This survival difference was greater at the 1-year follow-up. The average survival of the high-HGI group was 272.75 days (95% CI: 257.49-288.02), whereas that of the normal-HGI group was 309.94 days (95% CI: 305.55-314.32). Nonlinear analyses To further investigate the association between HGI and the outcomes of IS patients, RCS analysis was used to assess the nonlinear association between the HGI and the 30-day and 1-year mortality of patients (Fig. 5). RCS analysis revealed a "J"-shaped relationship between the HGI and both the 30-day and 1-year mortality risk. Low HGI values were associated with greatly elevated risks of mortality, but as the HGI increased, the mortality risk gradually decreased. A significant nonlinear relationship was observed between age and the mortality risk in IS patients (Fig. 6). The RCS prediction plots revealed that with increasing age, the 30-day mortality risk in IS patients significantly increased, with a greater growth rate observed in the older group (> 80 years). In the 1-year mortality analysis, the association between age and mortality risk was also significant. Mediating effect of HGI on age and mortality in IS patients Mediation analysis was performed to assess the mediating effect of the HGI on the relationship between age and the 30-day and 1-year mortality of IS patients (Fig. 7). To increase the interpretability of the assessments of the mediating effect, the values of the age variable were divided by 10. As shown in Table 5 and Fig. 8, in the analysis of 30-day mortality, the total effect of age on mortality was 0.0017 (95% CI: 0.0010 to 0.0029, P < 0.001), indicating that with increasing age, the 30-day mortality risk significantly increased. The average direct effect (ADE) was 0.0019 (95% CI: 0.0010–0.0032, P < 0.001), whereas the average causal mediation effect (ACME) via HGI was − 0.0001 (95% CI: -0.0003-0.0001, P < 0.001). The proportion of mediation of the HGI was − 0.0805 (95% CI: -0.1276 - -0.0494, P < 0.001); the negative sign indicates that, as a mediating variable, the HGI has an inhibitory effect on the positive relationship between age and mortality. Similar results were observed for the 1-year mortality assessment, indicating that the inhibitory effect of the HGI remained significant in the assessment of long-term mortality risk. Table 5 Mediating efect of HGI on age (per 10-year increase) and the mortality of IS patients Mediating effects Estimate 95% CI, lower bound 95% CI, upper bound P value 30-day follow-up Total Effect 0.0017 0.0010 0.0029 < 0.001 ACME (average) -0.0001 -0.0003 -0.0001 < 0.001 ADE (average) 0.0019 0.0010 0.0032 < 0.001 PropMediated (average) -0.0805 -0.1276 -0.0494 < 0.001 1-year follow-up Total Effect 0.0026 0.0016 0.0038 0 ACME (average) -0.0001 -0.0002 -0.0000 0 ADE (average) 0.0027 0.0017 0.0040 0 PropMediated (average) -0.0414 -0.0653 -0.0222 0 Establishment and validation of the prediction models Multiple ML algorithms, including the SVM, XGB, RF and DT algorithms, were used to construct models to predict the 30-day mortality of IS patients. The models were evaluated via ROC curve analysis and DCA. In the ROC curve analysis, the four ML models all achieved good predictive performance. As shown in Fig. 9, the ROC curves of all the models were located near the upper left corner, indicating that they all performed well in maximizing the true-positive rate while minimizing the false-positive rate. In terms of the AUC, the RF model performed the best, followed by the XGB model, SVM model and DT model. To explain the contribution of each feature to the performance of the ML models, the SHAP method was employed. As shown in Fig. 10, age was the most important feature for predicting the 30-day mortality of IS patients, followed by the BUN level, Hb level, WBC count, and HGI. The SHAP dual-coordinate line graph and histogram clearly show the ranking of the relative importance of each feature, with age having the most significant effect. The SHAP ripple plot further revealed the direction and magnitude of the impact of the changes in the feature on the prediction results and indicated that age was positively correlated with mortality risk. A detailed analysis of the characteristic screening table and the SHAP result matrix showed that the age factor had the highest SHAP value for predicting mortality (mean approximately 0.0319), indicating that age contributed most to the model predictions. Although the HGI was not the most important predictor, it interacted with age, Hb level and other factors, thus having a supplementary value to the performance of the prediction model. Discussion Through the analysis of the data of 3269 IS patients in the MIMIC-IV database, this study revealed the important role of the HGI and age in the prediction of mortality risk in IS patients. The results revealed that the HGI was an independent protective factor for the 30-day and 1-year mortality in IS patients, whereas age was the main risk factor. In addition, this is the first study to reveal that the HGI has a mediating effect on the relationship between age and mortality, which provides a new perspective for understanding the association between abnormal glucose metabolism and the outcomes of patients with IS. Zhang et al. [ 4 ] showed that an increased HGI was associated with an increased probability of cardiovascular diseases and total mortality risk in T2DM patients. Ahn et al. [ 5 ] analyzed the data of 1248 South Korean adults and revealed that a high HGI was independently associated with a greater risk of cardiovascular diseases, cerebrovascular diseases, impaired glucose metabolism, and peripheral arterial disease. However, contrary to these studies, in this study, both K–M curve and Cox multivariable regression analyses revealed that the HGI was negatively correlated with mortality rate in IS patients, instead serving as a protective factor for survival. Previous studies have shown that a low HGI was associated with increased mortality in cardiovascular patients [ 13 , 14 ] . Our results are partially consistent with those of Huang et al. [ 9 ] , who similarly employed the MIMIC-IV database and found that a low HGI was associated with poor outcomes in patients with severe IS. However, unlike Huang et al., who reported that a high HGI may be related to a greater long-term mortality risk, the long-term follow-up results in this study revealed that the protective effects of a high HGI lasted for 1 year. This difference may be due to the constitutions of the study populations, the differences in follow-up times, and the differences in the adjustment variables. Notably, RCS analysis revealed a "J"-shaped relationship between the HGI and mortality risk, with low-HGI patients having a significantly increased risk of mortality. Low HGI values arise from various factors, such as shortening or renewal of the erythrocyte lifespan and the glucose gradient across the human erythrocyte membrane [ 15 ] . Stress hyperglycemia, a common condition in IS, may lead to higher FPG values and lower HGI values [ 16 ] . This acute glucose metabolism disorder under the action of stressors [ 17 ] is mainly due to high catabolism caused by the activation of the hypothalamus-pituitary-adrenal axis and the release of various counterregulatory hormones, such as glucocorticoids and catecholamines [ 18 ] , and is closely associated with insulin resistance [ 19 ] . Multiple studies have shown that stress hyperglycemia is associated with poor outcomes in IS patients [ 20 – 22 ] , which indirectly supports the correlation between the HGI and the outcomes of IS patients observed in this study. Age has been extensively confirmed in previous studies to be a strong predictor of mortality in IS patients [ 23 – 25 ] . In this study, the nonlinear relationship between age and mortality risk was elucidated through RCS curve analysis, revealing that at age = 24, the mortality risk was the lowest, whereas older age (> 80 years) was associated with a rapid increase in the mortality risk. This age effect may reflect the multiple challenges faced by elderly patients, including decreased immune function, increased incidence of complications, reduced vascular function and others [ 25 – 27 ] . This study explored the mediating role of the HGI in the relationship between age and mortality in IS patients. Mediation analysis revealed that the average mediation effect was 0.0358 (95% CI: 0.0269–0.0457, P < 0.001), indicating that the HGI has a significant mediating effect on the relationship between age and mortality [ 28 ] . Age indirectly reduced the mortality risk by increasing the HGI (when the HGI was low), thus showing that the HGI exerted an inhibitory effect. Specifically, when other factors remain unchanged, age directly increased the mortality risk, but simultaneously, by affecting the HGI, age slightly reduced the mortality risk. This negative mediating effect means that, without the mediating effect of HGI, the effect of age on mortality may be stronger. In other words, the HGI partially alleviates the adverse effects of aging on the mortality risk, which may be associated with adaptive changes in metabolic regulation in elderly patients [ 29 ] , such as individual changes in blood glucose control or glycation. The value of ML prediction models In this study, multiple ML algorithms were used to successfully construct prediction models for 30-day mortality in IS patients. The RF model showed the best performance, which is consistent with the ability of the algorithm to capture complex nonlinear relationships among variables. SHAP analysis further confirmed that age was the most important predictor of mortality, followed by BUN level, Hb level, WBC count, and the HGI. The identification of these features provides not only a reference for clinical risk assessment but also potential targets for early intervention. The DCA results confirmed that these prediction models were superior to the "treat-all" or "treat-none" strategies under multiple decision thresholds, indicating that the models can offer tangible clinical benefits in managing IS patients. The integration of these models into clinical decision support systems could improve the risk stratification and individualized management of IS patients. Study limitations This study has several limitations. First, as a retrospective study, the results may be limited by inherent selection and information biases. Second, although known confounding factors were adjusted for through multivariable analysis, unmeasured confounding factors may have affected the results. Third, this study is based on the data from a single-center database, which may limit the external validity of the results. Fourth, the calculation of the HGI is based on the value first measured at the time of admission and therefore fails to reflect the dynamic changes in the HGI over time. Finally, detailed information about the treatments received by the patients, such as thrombolytic therapy and antiplatelet therapy, which could have affected the outcomes of the patients, could not be obtained. Clinical significance and future directions The results of this study may have great clinical importance. First, as a simple and easy-to-obtain indicator, the HGI could be included in the routine assessments of IS patients, especially elderly patients. Second, the identification of the optimal HGI threshold could provide a reference for the clinical identification of high-risk patients. Third, an understanding of the interaction between the HGI and age can be helpful for developing more accurate individualized treatment strategies. In the future, studies with larger sample sizes will be conducted to understand the relationship between the dynamic changes in the HGI and patient outcomes and to determine the mechanism of action of this relationship to support the findings of this study. Conclusions In this study, the HGI was revealed to be an independent protective factor against 30-day and 1-year mortality in IS patients as well as a cofactor alongside age to affect the outcomes of IS patients. A "J"-shaped nonlinear relationship was observed between the HGI and mortality risk, with excessively low HGI values associated with a significantly increased risk of mortality. The mediating role of the HGI in the relationship between age and mortality provides a new perspective for understanding the metabolic characteristics of elderly IS patients. The ML-based prediction models further confirmed the clinical predictive value of the HGI. These findings provide new ideas and tools for risk assessment and individualized management in IS patients. Declarations Author contribution statement Xinyu Tong and Jianxiong Gu were responsible for data collection, analysis, and first-draft writing. Chuxin Lyu was involved in the study design and data interpretation. Yichun Zhao assisted in statistical analysis and chart production. As the corresponding author, Minjie Guo and Ying Rui were responsible for the research conception, design, and supervision of the entire research process, and approved the final version of the paper. All authors read and agree to publish the final manuscript and agree to be responsible for all aspects of the research work, ensuring that the accuracy and completeness of the questions are properly investigated and addressed. Ethical Approval and consent to participate This study complies with the tenets of the Declaration of Helsinki , and the need for informed consent was waived due to the use of anonymized data. The institutional review board of Beth Israel Deaconess Medical Center (BIDMC) waived the need for informed consent due to the use of anonymized data. Funding This work was supported by Innovation and Development Fund of Wuxi City Traditional Chinese Medicine Hospital (ZYYZD24003) and Scientific Research Project of Jiangsu Province Traditional Chinese Medicine Society (ZXFZ2024035). 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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-6680914","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":471707552,"identity":"97d11a28-da55-4e95-99c9-0d181a1d3bf7","order_by":0,"name":"Xinyu Tong","email":"","orcid":"","institution":"Department of Neurology, Wuxi Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xinyu","middleName":"","lastName":"Tong","suffix":""},{"id":471707554,"identity":"540240af-0572-4cc8-8af0-bb90f22a3786","order_by":1,"name":"Jianxiong Gu","email":"","orcid":"","institution":"Department of Neurology, Wuxi Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jianxiong","middleName":"","lastName":"Gu","suffix":""},{"id":471707557,"identity":"40b1cf8c-fb7d-47de-831d-7b706cdef60b","order_by":2,"name":"Chuxin Lyu","email":"","orcid":"","institution":"First Clinical Medical School, Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chuxin","middleName":"","lastName":"Lyu","suffix":""},{"id":471707559,"identity":"c69b3b65-c62f-46f2-9d2e-add54437bd36","order_by":3,"name":"Yichun Zhao","email":"","orcid":"","institution":"Department of Neurology, Wuxi Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yichun","middleName":"","lastName":"Zhao","suffix":""},{"id":471707560,"identity":"ca8179e5-8beb-41f6-9131-e466831d9316","order_by":4,"name":"Ying Rui","email":"","orcid":"","institution":"Department of Neurology, Wuxi Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Rui","suffix":""},{"id":471707562,"identity":"a3fd229f-0de6-4d9c-b16e-46889d693d9a","order_by":5,"name":"Minjie Guo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIie3QPQrCMByH4ZRIJsVVCeoVIoXqIHqVFsEu7eTiGBAcne0t4qRufyi0Sw7QUZfOfqxCrVXXtG6CeYcmhd+zBCGd7ldz7lkXISjuBq9ELhyb3xEj4Nj5LMsJS9yUNvbE3Rmyf03QqCMAp0clkTCjDVn3D1ya1EMzUwAZMBWxYh7ROmn5AqSFPRQ6AvJfJQmNVU6Yy14kq0DiJWkHK9t+EygnExnh4VlCX0A0px6bmkFILCVpb9xTYi+gx5Jwe/MW4846XqZKkld7DVqQf55PhUv2z8m5OJq8fKrT6XT/2QN8XU570Uo/oAAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Neurology, Wuxi Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Minjie","middleName":"","lastName":"Guo","suffix":""}],"badges":[],"createdAt":"2025-05-16 12:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6680914/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6680914/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-14028-6","type":"published","date":"2025-08-02T16:05:35+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84867802,"identity":"587faa84-98f3-45c0-93c8-c6544db07cc9","added_by":"auto","created_at":"2025-06-18 08:36:44","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":117847,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of patient inclusion and exclusion\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6680914/v1/308c1d513e47c7bf4c82f9ea.jpg"},{"id":84867803,"identity":"357d57a2-b0ab-4a98-a943-012b27d344c9","added_by":"auto","created_at":"2025-06-18 08:36:44","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":95075,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between HGI and HbA1c\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6680914/v1/eece27d8037f43a11dcb3e98.jpg"},{"id":84869744,"identity":"08d81b15-a585-4c90-9641-608c37a6f95a","added_by":"auto","created_at":"2025-06-18 08:52:44","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":525036,"visible":true,"origin":"","legend":"\u003cp\u003eKM survival curves of age and HGI levels for 30-day and 1-year mortality in IS patients\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6680914/v1/c5d65910e6eac4d7e9b44af8.jpg"},{"id":84868803,"identity":"3b539a64-953a-4574-8f27-2e597db3d568","added_by":"auto","created_at":"2025-06-18 08:44:44","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":172753,"visible":true,"origin":"","legend":"\u003cp\u003eRMST analysis of age and HGI levels for 30-day and 1-year mortality in IS patients\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6680914/v1/f259b9bba07bc703bcc43545.jpg"},{"id":84868808,"identity":"bbdd8839-989a-4186-8f96-b09357941d3c","added_by":"auto","created_at":"2025-06-18 08:44:45","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":405986,"visible":true,"origin":"","legend":"\u003cp\u003eRCS analysis of HGI levels for 180-day and 1-year mortality in IS patients\u003c/p\u003e","description":"","filename":"Fig.5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6680914/v1/d7dae37da6bf871b9dfae554.jpg"},{"id":84867811,"identity":"2ef9b56a-faef-46ef-b24e-d9d08cf8a0de","added_by":"auto","created_at":"2025-06-18 08:36:44","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":333603,"visible":true,"origin":"","legend":"\u003cp\u003eRCS analysis of age for 180-day and 1-year mortality in IS patients\u003c/p\u003e","description":"","filename":"Fig.6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6680914/v1/38247990c783dbd9d69420bf.jpg"},{"id":84870869,"identity":"12ba0aeb-66a8-46ea-a32e-816da8d9e523","added_by":"auto","created_at":"2025-06-18 09:00:44","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":36597,"visible":true,"origin":"","legend":"\u003cp\u003eMediational models\u003c/p\u003e","description":"","filename":"Fig.7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6680914/v1/ba47b3e402825e8731a631b0.jpg"},{"id":84867819,"identity":"b0eb1cf0-cec4-40ad-b588-3e96bb1e3e6c","added_by":"auto","created_at":"2025-06-18 08:36:45","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":214828,"visible":true,"origin":"","legend":"\u003cp\u003eMediating efect of HGI on age (per 10-year increase) and the mortality of IS patients\u003c/p\u003e","description":"","filename":"fig.8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6680914/v1/a3535141343f375dbbbfa9bf.jpg"},{"id":84867807,"identity":"533b2014-afef-4a30-83c1-b7b029d7b895","added_by":"auto","created_at":"2025-06-18 08:36:44","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":82734,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curves for the four models\u003c/p\u003e","description":"","filename":"Fig.9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6680914/v1/cd4513baff996fae7104dd16.jpg"},{"id":84871962,"identity":"03647352-d4e4-42ef-9a75-7bcb62e41c27","added_by":"auto","created_at":"2025-06-18 09:08:45","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":630690,"visible":true,"origin":"","legend":"\u003cp\u003eThe results of SHAP analysis\u003c/p\u003e","description":"","filename":"fig.10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6680914/v1/f8618985bf0088efa373b6e9.jpg"},{"id":88268846,"identity":"acd92b47-16fa-4236-98da-fdea4e32c69f","added_by":"auto","created_at":"2025-08-04 16:52:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4110638,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6680914/v1/14bed0c9-0953-470b-850e-046e0f5225c3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations between age and the hemoglobin glycation index and 30-day and 1-year mortality in ischemic stroke patients: Mediation analyses and machine learning in a cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIschemic stroke (IS) is the most common type of stroke worldwide, accounting for 68% of all cases. According to the 2020 Global Burden of Disease study, IS affects 7.59\u0026nbsp;million individuals and is a leading cause of death, accounting for 49% of stroke deaths worldwide\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Although preventive measures and treatments for the disease continue to be updated, the high morbidity and mortality of IS position it as a major threat to human health. Therefore, greater attention should be paid to the prognostic management of IS patients in clinical research.\u003c/p\u003e \u003cp\u003eThe main risk factors for IS include hypertension (HTN), atrial fibrillation, smoking, drinking, hyperlipidemia (HLD), and most importantly, diabetes. Controlling blood glucose levels and improving the status of other related risk factors (such as blood pressure and blood lipids) are key to preventing IS and improving patient outcomes\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe concept of the hemoglobin glycation index (HGI) was first proposed in 2002 by Hempe et al.\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e, who defined it as the difference between actual glycated hemoglobin (HbA1c) and the HbA1c predicted from linear regression analysis of the fasting plasma glucose (FPG) (HGI\u0026thinsp;=\u0026thinsp;actual HbA1c - predicted HbA1c). This index can more accurately evaluate an individual\u0026rsquo;s blood glucose control than other methods.\u003c/p\u003e \u003cp\u003eA high HGI has been shown to be a risk factor for adverse cardiovascular diseases\u003csup\u003e[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. In the ACCORD trial, patients with high HGI were predicted to be at increased cardiovascular disease risk due to excessive hypoglycemia, whereas patients with low HGI were expected to benefit more from the use of treatment regimens for cardiovascular diseases\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. This difference suggests that the HGI not only is a sign of blood glucose control but also may be closely related to the development of other complications. A study of patients with type 2 diabetes mellitus (T2DM) and acute IS revealed a U-shaped association between HGI and patient outcomes; that is, both low and high HGI levels were associated with increased risks of developing adverse outcomes\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. However, another study based on data from the Medical Information Mart for Intensive Care (MIMIC)-IV database revealed that a low HGI was always associated with poor outcomes in patients with severe IS, whereas a high HGI may be a protective factor in the short term but may increase the risk of death in the long term\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe HGI could serve as a new predictor of all-cause mortality in IS patients by integrating the quality of blood glucose control, the extent of insulin resistance and individual biological differences. However, most studies on the HGI have focused on critically ill patients, and studies on the relationship between the HGI and the outcomes of IS patients are still rare. Therefore, this study used the data from the MIMIC-IV database to construct a linear regression equation to calculate the HGI and analyzed the associations between this index and mortality in hospitalized IS patients with the aim of providing new insights for prognostic assessment and improved clinical management of IS patients.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eData source\u003c/p\u003e \u003cp\u003eIn this study, data from the MIMIC-IV (version 3.1) were used\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. MIMIC-IV is an extensive and deidentified collection of health records containing information on more than 65,000 intensive care unit (ICU) patients and more than 200,000 emergency department visits in Boston, Massachusetts, USA, between 2008 and 2022\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. After successful application, the author (Chuxin Lyu) obtained access to the MIMIC-IV database and completed the training initiative project at the partner institution (Certification Number: 61738903).\u003c/p\u003e \u003cp\u003eStudy population\u003c/p\u003e \u003cp\u003eInpatients diagnosed with IS were identified from the MIMIC-IV database according to International Classification of Diseases (ICD) codes\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e (see Supplementary Table S1 for the ICD-9 and ICD-10 codes of the diseases of interest). Notably, the included IS patients were not necessarily newly diagnosed at the time of hospitalization, and IS was not always the main reason for their hospitalization. Some patients, for example, may have been admitted to the hospital for other reasons and been diagnosed with IS during hospitalization. For patients with multiple admissions, only the data from the first hospitalization were included for analysis.\u003c/p\u003e \u003cp\u003eThe inclusion criteria were as follows (Fig.\u0026nbsp;1):\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;18 years on first admission to the hospital;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eICU stay\u0026thinsp;\u0026ge;\u0026thinsp;48 hours\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe exclusion criteria were as follows:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e\u0026lt;80% data completeness;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMissing FPG or HbA1c data;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eExtreme data values.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eData extraction and definitions\u003c/p\u003e \u003cp\u003eThe baseline characteristics of the patients were extracted from the database with PostgreSQL software. Potential confounding variables included the following: 1. Demographic characteristics: age and sex; 2. Complications: HTN, chronic kidney disease (CKD), HLD, T2DM, ischemic heart disease (IHD), and chronic obstructive pulmonary disease (COPD); 3. Laboratory test data: levels of urea nitrogen (BUN), creatinine (Cr), glycerol triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), FBG, and HbA1c; white blood cell (WBC) count and platelet (PLT) count; and hemoglobin (Hb) level. For laboratory data, the first recorded values after admission were considered for analysis. The HGI was calculated via the method proposed by Hempe et al.\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, in which the baseline FBG and HbA1c data of all individuals were used to assess the linear relationship between FBG and HbA1c in the study group. The predicted HbA1c was calculated from the included samples (predicted HbA1c\u0026thinsp;=\u0026thinsp;0.0082*FPG\u0026thinsp;+\u0026thinsp;4.8386), and the difference between the observed HbA1c and predicted HbA1c levels was subsequently calculated as the HGI. The relationship between the HGI and HbA1c level is shown in Fig.\u0026nbsp;2. The mortality rate was calculated from the first day of hospitalization. The primary endpoint of this study was the 30-day and 1-year mortality rates of IS patients.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eCategorical variables are presented as percentages. Continuous variables were first subjected to a normality test; data conforming to a normal distribution are expressed as the mean and standard deviation (SD), whereas for data that did not conform to a normal distribution, the median and interquartile range (IQR) are used to describe the central tendency and variability. Appropriate statistical tests, such as Student's t test, the Mann-Whitney U test, the chi-square test, and Fisher's exact test, were used for between-group comparisons. Logistic regression analysis and Cox proportional hazards regression analysis were used to investigate the associations between age and the HGI and mortality. First, univariable analyses were performed for each potential predictive factor. Bonferroni correction was performed for the univariable analyses. Clinically important variables with significant differences after Bonferroni correction were subsequently included in the multivariable analysis. The least absolute shrinkage and selection operator (LASSO) regression method was used to identify factors significantly associated with an outcome of death in IS patients. The factors identified with LASSO regression were used as independent variables in multivariable logistic regression and Cox proportional hazards regression models. The performance of these two multivariable models was evaluated via receiver operating characteristic (ROC) curve analysis. For survival analysis, Kaplan\u0026ndash;Meier (K\u0026ndash;M) curves and the restricted mean survival time (RMST) were plotted. Restriction cubic spline (RCS) curves were used to study potential nonlinear relationships among age, the HGI and mortality. Mediation analysis was performed to determine whether HGI had a mediating role in the relationship between the exposure variable (age) and mortality. To increase the robustness of the analysis, bootstrapping with 1000 resamplings was performed. The results included the size of the indirect path effect, the proportion of the mediating effect, and the related p values. Owing to missing values for some variables and the nonnormal distribution of many variables, the median imputation method was used to process missing data. Variables with excessive missing data were excluded from the analysis. All the statistical analyses were performed with R software (version 4.4.1). A P value of less than 0.05 (two-sided) was considered to indicate statistical significance. The results of the multivariable logistic regression and Cox regression analyses are expressed as odds ratios (ORs) or hazard ratios (HRs) and 95% confidence intervals (CIs).\u003c/p\u003e \u003cp\u003eMachine learning (ML)\u003c/p\u003e \u003cp\u003eVariables selected by LASSO analysis were incorporated into a machine learning (ML) algorithm. The dataset was divided into a training set and a test set at a ratio of 7:3. The training set was used for model construction, and the test set was used for model evaluation. The support vector machine (SVM), extreme gradient boosting (XGB), random forest (RF) and decision tree (DT) algorithms were used to construct models on the basis of the selected variables and predict the 30-day mortality risk of IS patients. During model development, the optimal hyperparameters were set. The ROC curves and the corresponding area under the curve (AUC) were used to evaluate the model performance. The clinical practicality of the models was evaluated with decision curve analysis (DCA). The performance of the best model in the test set was further interpreted via the SHapley Additive exPlanations (SHAP) value, which provides insight into the importance and ranking of each variable included in the model. The SHAP value can clearly and intuitively reveal the positive or negative impact of each variable on the model prediction, and the screening threshold was set to 0.05. In addition, the performance of the optimal model was evaluated via bootstrapping.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eBaseline patient characteristics\u003c/p\u003e \u003cp\u003eA total of 3269 IS patients were included in this study. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the baseline characteristics of the participants at the 30-day and 1-year follow-ups. In this cohort, compared with the 30-day survival group, the patients in the 30-day nonsurvival group were significantly older, and the incidences of CKD and IHD were significantly greater. In terms of laboratory indicators, compared with the 30-day survival group, the 30-day nonsurvival group demonstrated a significantly lower HGI, significantly greater levels of BUN, Cr, and FBG, a significantly greater WBC count, and significantly lower LDL-C and Hb levels. Similar trends were observed at the 1-year follow-up results. Specifically, patients in the 1-year nonsurvival group were older than those in the survival group. The incidences of CKD, IHD, and COPD in the nonsurvival group were significantly greater than those in the survival group, whereas the prevalence of HTN was significantly lower than that in the survival group. In terms of laboratory indicators, compared with the survival group, the nonsurvival group again demonstrated a significantly lower HGI, significantly greater levels of BUN, Cr, and FBG, a significantly lower WBC count, and significantly lower LDL-C and Hb levels.\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\u003ePatient baseline information\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=\"char\" char=\".\" 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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e30-day follow-up\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e1-year follow-up\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSurvivors (\u003c/b\u003e\u003cb\u003en\u003c/b\u003e\u0026thinsp;=\u0026thinsp;\u003cb\u003e2930)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNonsurvivors (\u003c/b\u003e\u003cb\u003en\u003c/b\u003e\u0026thinsp;=\u0026thinsp;\u003cb\u003e339)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e \u003cb\u003e-value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eSurvivors (\u003c/b\u003e\u003cb\u003en\u003c/b\u003e\u0026thinsp;=\u0026thinsp;\u003cb\u003e2609)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eNonsurvivors (\u003c/b\u003e\u003cb\u003en\u003c/b\u003e\u0026thinsp;=\u0026thinsp;\u003cb\u003e660)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e \u003cb\u003e-value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\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\u003e70 (59\u0026ndash;81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82 (71\u0026ndash;89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69 (58\u0026ndash;79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81 (71\u0026ndash;88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1522 (51.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150 (44.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1382 (52.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e290 (43.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHTN (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1735 (59.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e183 (53.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1571 (60.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e347 (52.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e264 (9.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (21.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e200 (7.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e136 (20.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLD (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1360 (46.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155 (45.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1213 (46.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e302 (45.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.7682\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2DM (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e560 (19.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (21.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e492 (18.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e141 (21.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1614\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIHD (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e644 (21.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109 (32.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e531 (20.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e222 (33.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e245 (8.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (10.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e191 (7.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91 (13.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0374 (-0.3243-0.2758)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1604 (-0.4439-0.1699)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.039 (-0.3226-0.2692)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.102 (-0.404-0.2599)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (12\u0026ndash;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (15\u0026ndash;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (12\u0026ndash;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 (14\u0026ndash;29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9 (0.7\u0026ndash;1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.8\u0026ndash;1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9 (0.7\u0026ndash;1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (0.7\u0026ndash;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106 (76.25\u0026ndash;146)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106 (74-154.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106 (77\u0026ndash;146)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e101.5 (74-143.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0924\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93 (69\u0026ndash;119)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (52.5\u0026ndash;103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94 (70\u0026ndash;121)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77.5 (56\u0026ndash;104)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBG, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106 (93\u0026ndash;126)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123 (102\u0026ndash;144)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105 (93\u0026ndash;125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e117 (98-140.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.7 (5.4-6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.7 (5.3-6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.7 (5.4-6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.7 (5.4\u0026ndash;6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.4969\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC, K/\u0026micro;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.2 (6.4-10.575)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.4 (8.1-13.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.1 (6.4\u0026ndash;10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.7 (7.6\u0026ndash;12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT, K/\u0026micro;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215 (175\u0026ndash;266)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e204 (160-262.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e216 (177\u0026ndash;266)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e208 (160\u0026ndash;268)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0637\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb, g/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.8 (11.5\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.5 (10.1\u0026ndash;12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.9 (11.6\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.65 (10.2\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePrimary outcomes of logistic regression analyses\u003c/p\u003e \u003cp\u003eAnalysis of factors associated with 30-day mortality\u003c/p\u003e \u003cp\u003eUnivariable and multivariable logistic regression analyses (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) both revealed that age (univariable: OR\u0026thinsp;=\u0026thinsp;1.06, 95% CI: 1.05\u0026ndash;1.07, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; multivariable: OR\u0026thinsp;=\u0026thinsp;1.05, 95% CI: 1.04\u0026ndash;1.07, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) was positively associated with 30-day mortality, whereas the HGI (univariable: OR\u0026thinsp;=\u0026thinsp;0.57, 95% CI: 0.46\u0026ndash;0.71, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; multivariable: OR\u0026thinsp;=\u0026thinsp;0.44, 95% CI: 0.34\u0026ndash;0.57, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) was negatively associated with 30-day mortality.\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\u003eLogistic analyses: risk factors of mortality\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=\"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 \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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariable logistic regression analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariable logistic regression analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eLASSO-logistic regression analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOR (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e \u003cb\u003e-value*\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eOR (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e \u003cb\u003e-value*\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eOR (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e \u003cb\u003e-value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30-day follow-up\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.06 (1.05\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.05 (1.04\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.05 (1.04\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.73 (0.59\u0026ndash;0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.11 (0.85\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4371\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.81 (0.64\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.74\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8813\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.72 (2.03\u0026ndash;3.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.08 (0.72\u0026ndash;1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7018\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.16 (0.88\u0026ndash;1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.45 (1.03\u0026ndash;2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0296\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.78\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.90 (0.70\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3979\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.68 (1.31\u0026ndash;2.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.04 (0.78\u0026ndash;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8021\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.34 (0.92\u0026ndash;1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02 (0.68\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9387\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.57 (0.46\u0026ndash;0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.44 (0.34\u0026ndash;0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.51 (0.40\u0026ndash;0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.77 (0.73\u0026ndash;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.86 (0.81\u0026ndash;0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.85 (0.80\u0026ndash;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.99-1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8290\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.05 (1.03\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.04 (1.02\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.05 (1.03\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.98\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1900\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTTW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00-1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00 (1.00-1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0009\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.20 (1.10\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84 (0.70\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0474\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.04 (1.03\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02 (1.02\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.02 (1.02\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1-year follow-up\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.06 (1.05\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.07 (1.06\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.07 (1.06\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.70 (0.59\u0026ndash;0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03 (0.84\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7348\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.73 (0.62\u0026ndash;0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.82 (0.66\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.80 (0.65-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0541\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.13 (2.46\u0026ndash;3.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.16 (0.83\u0026ndash;1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.12 (0.81\u0026ndash;1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.5014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.17 (0.94\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.08 (0.82\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5913\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.82\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.85 (0.70\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1138\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.98 (1.64\u0026ndash;2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.17 (0.94\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.16 (0.94\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1636\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.02 (1.55\u0026ndash;2.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.47 (1.09\u0026ndash;1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.46 (1.09\u0026ndash;1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.79 (0.67\u0026ndash;0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.69 (0.56\u0026ndash;0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.70 (0.58\u0026ndash;0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.76 (0.72\u0026ndash;0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84 (0.79\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.84 (0.80\u0026ndash;0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2008\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09 (1.07\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.10 (1.08\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.10 (1.08\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.98\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.99 (0.99\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0478\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTTW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.24 (1.14\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.95 (0.82\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3969\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.04 (1.03\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02 (1.01\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.02 (1.01\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLASSO regression analysis confirmed that age, the HGI, Hb level, WBC count, and BUN level were significant predictors of 30-day and 1-year mortality. A multivariable logistic regression model was subsequently constructed, and the results revealed that age (OR\u0026thinsp;=\u0026thinsp;1.05, 95% CI: 1.04\u0026ndash;1.06, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) was positively correlated with 30-day mortality, whereas the HGI (OR\u0026thinsp;=\u0026thinsp;0.51, 95% CI: 0.40\u0026ndash;0.65, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) was inversely correlated with 30-day mortality.\u003c/p\u003e \u003cp\u003eAnalysis of the factors associated with 1-year mortality\u003c/p\u003e \u003cp\u003eBoth univariable and multivariable logistic regression analyses revealed that age (univariable: OR\u0026thinsp;=\u0026thinsp;1.06, 95% CI: 1.05\u0026ndash;1.07, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; multivariable: OR\u0026thinsp;=\u0026thinsp;1.07, 95% CI: 1.06\u0026ndash;1.08, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) was positively correlated with 1-year mortality, whereas the HGI (univariable: OR\u0026thinsp;=\u0026thinsp;0.79, 95% CI: 0.67\u0026ndash;0.92, P\u0026thinsp;=\u0026thinsp;0.0031; multivariable: OR\u0026thinsp;=\u0026thinsp;0.69, 95% CI: 0.56\u0026ndash;0.84, P\u0026thinsp;=\u0026thinsp;0.0003) was negatively correlated with 1-year mortality.\u003c/p\u003e \u003cp\u003eLASSO logistic regression analysis further confirmed that age (OR\u0026thinsp;=\u0026thinsp;1.07, 95% CI: 1.06\u0026ndash;1.07, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and the presence of COPD (OR\u0026thinsp;=\u0026thinsp;1.46, 95% CI: 1.09\u0026ndash;1.96, P\u0026thinsp;=\u0026thinsp;0.0113) were positively correlated with 1-year mortality, whereas the HGI (OR\u0026thinsp;=\u0026thinsp;0.70, 95% CI: 0.58\u0026ndash;0.83, P\u0026thinsp;=\u0026thinsp;0.0001) was negatively correlated with 1-year mortality.\u003c/p\u003e \u003cp\u003eStratified analysis of the HGI and its relationship with mortality risk\u003c/p\u003e \u003cp\u003eTo further investigate the association between HGI and mortality risk, the HGI was subject to stratified analysis by quartile (Q1-Q4) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In Model 1, there was no adjustment for any covariates; in Model 2, the model was adjusted for age; and in Model 3, the model was adjusted for age, Hb level, WBC count, and BUN level. Compared with the Q1 group (-0.55 - -0.33), in the Q3 (0.09\u0026ndash;0.27) and Q4 groups (0.27\u0026ndash;2.13), the 30-day mortality risk was significantly reduced [Q3 group, OR\u0026thinsp;=\u0026thinsp;0.53 (95% CI: 0.38\u0026ndash;0.73, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) in Model 1, OR\u0026thinsp;=\u0026thinsp;0.40 (95% CI: 0.29\u0026ndash;0.56, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) in Model 2 with the variable adjustment, and OR\u0026thinsp;=\u0026thinsp;0.50 (95% CI: 0.35\u0026ndash;0.71, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) in Model 3; Q4 group, OR\u0026thinsp;=\u0026thinsp;0.60 (95% CI: 0.44\u0026ndash;0.82, P\u0026thinsp;=\u0026thinsp;0.0017) in Model 1, OR\u0026thinsp;=\u0026thinsp;0.47 (95% CI: 0.34\u0026ndash;0.65, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) in Model 2, and OR\u0026thinsp;=\u0026thinsp;0.51 (95% CI: 0.36\u0026ndash;0.71, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) in Model 3]. A trend test revealed that as the HGI increased (i.e., from the Q1 group to Q4 group), the mortality risk decreased significantly (p for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). A similar pattern was observed at the 1-year follow-up, i.e., as the HGI increased (i.e., from the Q1 group to Q4 group), the mortality risk tended to decrease.\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\u003eThe association between HGI levels and mortality by logistic regression analyses\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedel 1\u003c/p\u003e \u003cp\u003eOR (95% CI)\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 \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedel 2\u003c/p\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e -value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedel 3\u003c/p\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\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\u003e30-day follow-up\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57 (0.46\u0026ndash;0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.46 (0.36\u0026ndash;0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.51 (0.41\u0026ndash;0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGI (quartile)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.55 (-1.74 - -0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.18 (-0.33 - -0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83 (0.62\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.74 (0.54-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.93 (0.68\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.6705\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.09 (-0.05-0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.53 (0.38\u0026ndash;0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40 (0.29\u0026ndash;0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.50 (0.35\u0026ndash;0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.59 (0.272.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.60 (0.44\u0026ndash;0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.47 (0.34\u0026ndash;0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.51 (0.36\u0026ndash;0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1-year follow-up\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.79 (0.67\u0026ndash;0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.63 (0.53\u0026ndash;0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.70 (0.58\u0026ndash;0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGI (quartile)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.55 (-1.74 - -0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.18 (-0.33 -0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80 (0.63\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.69 (0.54\u0026ndash;0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.91 (0.70\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.5023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.09 (-0.05-0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.63 (0.49\u0026ndash;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.45 (0.35\u0026ndash;0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.58 (0.44\u0026ndash;0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.59 (0.27\u0026ndash;2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.79 (0.62\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.58 (0.45\u0026ndash;0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.65 (0.50\u0026ndash;0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eResults of Cox regression analysis\u003c/p\u003e \u003cp\u003eTo identify prognostic indicators for 30-day and 1-year mortality, univariable and multivariable Cox regression analyses were performed. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, in the analysis of 30-day mortality, age (univariable HR\u0026thinsp;=\u0026thinsp;1.05, 95% CI: 1.04\u0026ndash;1.06, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; multivariable HR\u0026thinsp;=\u0026thinsp;1.05, 95% CI: 1.04\u0026ndash;1.06, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u0026lt;0.001) was an independent risk factor, whereas the HGI (univariable: HR\u0026thinsp;=\u0026thinsp;0.59, 95% CI: 0.48\u0026ndash;0.72, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; multivariable: HR\u0026thinsp;=\u0026thinsp;0.47, 95% CI: 0.38\u0026ndash;0.59, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was an independent protective factor for the IS patients. For the 1-year mortality risk, Cox regression analysis revealed a similar trend; age (univariable HR\u0026thinsp;=\u0026thinsp;1.05, 95% CI: 1.05\u0026ndash;1.06, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; multivariable: HR\u0026thinsp;=\u0026thinsp;1.05, 95% CI: 1.05\u0026ndash;1.06, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was an independent risk factor, whereas the HGI (univariable: HR\u0026thinsp;=\u0026thinsp;0.79, 95% CI: 0.68\u0026ndash;0.91, P\u0026thinsp;=\u0026thinsp;0.001; multivariable: HR\u0026thinsp;=\u0026thinsp;0.63, 95% CI: 0.54\u0026ndash;0.74, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Hb level (HR\u0026thinsp;=\u0026thinsp;0.79, 95% CI: 0.76\u0026ndash;0.82, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were independent protective factors for the IS patients.\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\u003eCox analyses: risk factors of mortality\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=\"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 \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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariable Cox regression analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariable Cox regression analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eLASSO-Cox regression analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHR (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e \u003cb\u003e-value*\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eHR (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e \u003cb\u003e-value*\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eHR (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e \u003cb\u003e-value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30-day follow-up\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.05 (1.04\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.05 (1.04\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.05 (1.04\u0026ndash;1.06)\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\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.76 (0.60\u0026ndash;0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.09 (0.86\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.485\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.82 (0.66\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.97 (0.75\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.803\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.49 (1.92\u0026ndash;3.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.05 (0.75\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.793\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.15 (0.89\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.41 (1.05\u0026ndash;1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.020\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.79\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.88 (0.71\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.273\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.62 (1.29\u0026ndash;2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03 (0.81\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.809\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.32 (0.94\u0026ndash;1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02 (0.72\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.916\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.59 (0.48\u0026ndash;0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.47 (0.38\u0026ndash;0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.54 (0.44\u0026ndash;0.66)\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\u003eHb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.79 (0.75\u0026ndash;0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.89 (0.84\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.87 (0.82\u0026ndash;0.92)\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\u003ePLT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.937\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.01\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02 (1.01\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.01 (1.01\u0026ndash;1.02)\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\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.98\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.099\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTTW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00-1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00 (1.00-1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.15 (1.09\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.86 (0.75\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.041\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03 (1.02\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02 (1.02\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.02 (1.01\u0026ndash;1.03)\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\u003e1-year follow-up\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.05 (1.05\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.05 (1.05\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.05 (1.04\u0026ndash;1.06)\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\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.73 (0.62\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.04 (0.88\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.620\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.76 (0.65\u0026ndash;0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.83 (0.69\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.042\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.61 (2.16\u0026ndash;3.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02 (0.80\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.21 (0.98\u0026ndash;1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.14 (0.95\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.18 (0.95\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.134\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.84\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.88 (0.75\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.114\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.81 (1.54\u0026ndash;2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.15 (0.97\u0026ndash;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.110\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.80 (1.44\u0026ndash;2.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.32 (1.05\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.016\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.79 (0.68\u0026ndash;0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.63 (0.54\u0026ndash;0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.68 (0.58\u0026ndash;0.78)\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\u003eHb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.79 (0.76\u0026ndash;0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.88 (0.84\u0026ndash;0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.86 (0.83\u0026ndash;0.89)\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\u003ePLT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.800\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.01\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.01 (1.01\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.01 (1.01\u0026ndash;1.02)\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\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.98\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTTW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00-1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00 (1.00-1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.24 (1.14\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94 (0.85\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.178\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03 (1.02\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02 (1.01\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.02 (1.01\u0026ndash;1.02)\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 \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAfter screening the variables with LASSO regression analysis, age (30 days: HR\u0026thinsp;=\u0026thinsp;1.05, 95% CI: 1.04\u0026ndash;1.06, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; 1 year: HR\u0026thinsp;=\u0026thinsp;1.05, 95% CI: 1.04\u0026ndash;1.06, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) remained an independent risk factor for both 30-day and 1-year mortality for the IS patients, whereas the HGI (30 days: HR\u0026thinsp;=\u0026thinsp;0.54, 95% CI: 0.44\u0026ndash;0.66, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; 1 year: HR\u0026thinsp;=\u0026thinsp;0.68, 95% CI: 0.58\u0026ndash;0.78, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Hb level (HR\u0026thinsp;=\u0026thinsp;0.87, 95% CI: 0.82\u0026ndash;0.92, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) remained independent protective factors against 30-day and 1-year mortality for the IS patients.\u003c/p\u003e \u003cp\u003eKaplan\u0026ndash;Meier (K\u0026ndash;M) survival analysis\u003c/p\u003e \u003cp\u003eK\u0026ndash;M survival analysis, (curves shown in Fig.\u0026nbsp;3a), revealed that the HGI was significantly associated with 30-day mortality in IS patients (log-rank test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Analysis of the survival curves revealed that, during the 30-day follow-up period, the mortality risk in the low-HGI group was significantly greater than that in the high-HGI group. This survival difference persisted (log-rank test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) at the 1-year follow-up (Fig.\u0026nbsp;3b).\u003c/p\u003e \u003cp\u003eMoreover, significant differences were observed in both the 30-day and 1-year survival rates between patients in the older and younger groups (log-rank test, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Specifically, survival curve analysis (Fig.\u0026nbsp;3c, 3d) revealed that the mortality risk in the older group was significantly greater than that in the younger group.\u003c/p\u003e \u003cp\u003eRestricted Mean Survival Time\u003c/p\u003e \u003cp\u003eRMST analysis was employed to evaluate the association between the HGI and 30-day and 1-year mortality in IS patients (Fig.\u0026nbsp;4). Patients were divided into a high-HGI group (Arm 1) and a normal-HGI group (Arm 0) for comparative analysis.\u003c/p\u003e \u003cp\u003eDuring the 30-day follow-up period, the average survival of patients in the high-HGI group was 26.70 days (95% CI: 25.88\u0026ndash;27.51), whereas that of patients in the normal-HGI group was 28.17 days (95% CI: 27.95\u0026ndash;28.40). The RMST difference between the two groups was \u0026minus;\u0026thinsp;1.48 days (95% CI: -2.32 - -0.63, p\u0026thinsp;=\u0026thinsp;0.0006) According to RMST analysis, the 30-day survival of patients in the high-HGI group was significantly shorter than that of patients in the normal-HGI group.\u003c/p\u003e \u003cp\u003eThis survival difference was greater at the 1-year follow-up. The average survival of the high-HGI group was 272.75 days (95% CI: 257.49-288.02), whereas that of the normal-HGI group was 309.94 days (95% CI: 305.55-314.32).\u003c/p\u003e \u003cp\u003eNonlinear analyses\u003c/p\u003e \u003cp\u003eTo further investigate the association between HGI and the outcomes of IS patients, RCS analysis was used to assess the nonlinear association between the HGI and the 30-day and 1-year mortality of patients (Fig.\u0026nbsp;5).\u003c/p\u003e \u003cp\u003eRCS analysis revealed a \"J\"-shaped relationship between the HGI and both the 30-day and 1-year mortality risk. Low HGI values were associated with greatly elevated risks of mortality, but as the HGI increased, the mortality risk gradually decreased.\u003c/p\u003e \u003cp\u003eA significant nonlinear relationship was observed between age and the mortality risk in IS patients (Fig.\u0026nbsp;6). The RCS prediction plots revealed that with increasing age, the 30-day mortality risk in IS patients significantly increased, with a greater growth rate observed in the older group (\u0026gt;\u0026thinsp;80 years). In the 1-year mortality analysis, the association between age and mortality risk was also significant.\u003c/p\u003e \u003cp\u003eMediating effect of HGI on age and mortality in IS patients\u003c/p\u003e \u003cp\u003eMediation analysis was performed to assess the mediating effect of the HGI on the relationship between age and the 30-day and 1-year mortality of IS patients (Fig.\u0026nbsp;7). To increase the interpretability of the assessments of the mediating effect, the values of the age variable were divided by 10. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;8, in the analysis of 30-day mortality, the total effect of age on mortality was 0.0017 (95% CI: 0.0010 to 0.0029, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that with increasing age, the 30-day mortality risk significantly increased. The average direct effect (ADE) was 0.0019 (95% CI: 0.0010\u0026ndash;0.0032, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas the average causal mediation effect (ACME) via HGI was \u0026minus;\u0026thinsp;0.0001 (95% CI: -0.0003-0.0001, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The proportion of mediation of the HGI was \u0026minus;\u0026thinsp;0.0805 (95% CI: -0.1276 - -0.0494, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001); the negative sign indicates that, as a mediating variable, the HGI has an inhibitory effect on the positive relationship between age and mortality. Similar results were observed for the 1-year mortality assessment, indicating that the inhibitory effect of the HGI remained significant in the assessment of long-term mortality risk.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMediating efect of HGI on age (per 10-year increase) and the mortality of IS patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMediating effects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI, lower bound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI, upper bound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30-day follow-up\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eACME (average)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eADE (average)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003ePropMediated (average)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.1276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003e1-year follow-up\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACME (average)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADE (average)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePropMediated (average)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eEstablishment and validation of the prediction models\u003c/p\u003e \u003cp\u003eMultiple ML algorithms, including the SVM, XGB, RF and DT algorithms, were used to construct models to predict the 30-day mortality of IS patients. The models were evaluated via ROC curve analysis and DCA.\u003c/p\u003e \u003cp\u003eIn the ROC curve analysis, the four ML models all achieved good predictive performance. As shown in Fig.\u0026nbsp;9, the ROC curves of all the models were located near the upper left corner, indicating that they all performed well in maximizing the true-positive rate while minimizing the false-positive rate. In terms of the AUC, the RF model performed the best, followed by the XGB model, SVM model and DT model.\u003c/p\u003e \u003cp\u003eTo explain the contribution of each feature to the performance of the ML models, the SHAP method was employed. As shown in Fig.\u0026nbsp;10, age was the most important feature for predicting the 30-day mortality of IS patients, followed by the BUN level, Hb level, WBC count, and HGI.\u003c/p\u003e \u003cp\u003eThe SHAP dual-coordinate line graph and histogram clearly show the ranking of the relative importance of each feature, with age having the most significant effect. The SHAP ripple plot further revealed the direction and magnitude of the impact of the changes in the feature on the prediction results and indicated that age was positively correlated with mortality risk.\u003c/p\u003e \u003cp\u003eA detailed analysis of the characteristic screening table and the SHAP result matrix showed that the age factor had the highest SHAP value for predicting mortality (mean approximately 0.0319), indicating that age contributed most to the model predictions. Although the HGI was not the most important predictor, it interacted with age, Hb level and other factors, thus having a supplementary value to the performance of the prediction model.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThrough the analysis of the data of 3269 IS patients in the MIMIC-IV database, this study revealed the important role of the HGI and age in the prediction of mortality risk in IS patients. The results revealed that the HGI was an independent protective factor for the 30-day and 1-year mortality in IS patients, whereas age was the main risk factor. In addition, this is the first study to reveal that the HGI has a mediating effect on the relationship between age and mortality, which provides a new perspective for understanding the association between abnormal glucose metabolism and the outcomes of patients with IS.\u003c/p\u003e \u003cp\u003eZhang et al.\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e showed that an increased HGI was associated with an increased probability of cardiovascular diseases and total mortality risk in T2DM patients. Ahn et al.\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e analyzed the data of 1248 South Korean adults and revealed that a high HGI was independently associated with a greater risk of cardiovascular diseases, cerebrovascular diseases, impaired glucose metabolism, and peripheral arterial disease. However, contrary to these studies, in this study, both K\u0026ndash;M curve and Cox multivariable regression analyses revealed that the HGI was negatively correlated with mortality rate in IS patients, instead serving as a protective factor for survival. Previous studies have shown that a low HGI was associated with increased mortality in cardiovascular patients\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Our results are partially consistent with those of Huang et al.\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e, who similarly employed the MIMIC-IV database and found that a low HGI was associated with poor outcomes in patients with severe IS. However, unlike Huang et al., who reported that a high HGI may be related to a greater long-term mortality risk, the long-term follow-up results in this study revealed that the protective effects of a high HGI lasted for 1 year. This difference may be due to the constitutions of the study populations, the differences in follow-up times, and the differences in the adjustment variables.\u003c/p\u003e \u003cp\u003eNotably, RCS analysis revealed a \"J\"-shaped relationship between the HGI and mortality risk, with low-HGI patients having a significantly increased risk of mortality. Low HGI values arise from various factors, such as shortening or renewal of the erythrocyte lifespan and the glucose gradient across the human erythrocyte membrane\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Stress hyperglycemia, a common condition in IS, may lead to higher FPG values and lower HGI values\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. This acute glucose metabolism disorder under the action of stressors\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e is mainly due to high catabolism caused by the activation of the hypothalamus-pituitary-adrenal axis and the release of various counterregulatory hormones, such as glucocorticoids and catecholamines\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, and is closely associated with insulin resistance\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Multiple studies have shown that stress hyperglycemia is associated with poor outcomes in IS patients\u003csup\u003e[\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, which indirectly supports the correlation between the HGI and the outcomes of IS patients observed in this study.\u003c/p\u003e \u003cp\u003eAge has been extensively confirmed in previous studies to be a strong predictor of mortality in IS patients\u003csup\u003e[\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. In this study, the nonlinear relationship between age and mortality risk was elucidated through RCS curve analysis, revealing that at age\u0026thinsp;=\u0026thinsp;24, the mortality risk was the lowest, whereas older age (\u0026gt;\u0026thinsp;80 years) was associated with a rapid increase in the mortality risk. This age effect may reflect the multiple challenges faced by elderly patients, including decreased immune function, increased incidence of complications, reduced vascular function and others\u003csup\u003e[\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study explored the mediating role of the HGI in the relationship between age and mortality in IS patients. Mediation analysis revealed that the average mediation effect was 0.0358 (95% CI: 0.0269\u0026ndash;0.0457, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that the HGI has a significant mediating effect on the relationship between age and mortality\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Age indirectly reduced the mortality risk by increasing the HGI (when the HGI was low), thus showing that the HGI exerted an inhibitory effect. Specifically, when other factors remain unchanged, age directly increased the mortality risk, but simultaneously, by affecting the HGI, age slightly reduced the mortality risk. This negative mediating effect means that, without the mediating effect of HGI, the effect of age on mortality may be stronger. In other words, the HGI partially alleviates the adverse effects of aging on the mortality risk, which may be associated with adaptive changes in metabolic regulation in elderly patients\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e, such as individual changes in blood glucose control or glycation.\u003c/p\u003e \u003cp\u003eThe value of ML prediction models\u003c/p\u003e \u003cp\u003eIn this study, multiple ML algorithms were used to successfully construct prediction models for 30-day mortality in IS patients. The RF model showed the best performance, which is consistent with the ability of the algorithm to capture complex nonlinear relationships among variables. SHAP analysis further confirmed that age was the most important predictor of mortality, followed by BUN level, Hb level, WBC count, and the HGI. The identification of these features provides not only a reference for clinical risk assessment but also potential targets for early intervention.\u003c/p\u003e \u003cp\u003eThe DCA results confirmed that these prediction models were superior to the \"treat-all\" or \"treat-none\" strategies under multiple decision thresholds, indicating that the models can offer tangible clinical benefits in managing IS patients. The integration of these models into clinical decision support systems could improve the risk stratification and individualized management of IS patients.\u003c/p\u003e \u003cp\u003eStudy limitations\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, as a retrospective study, the results may be limited by inherent selection and information biases. Second, although known confounding factors were adjusted for through multivariable analysis, unmeasured confounding factors may have affected the results. Third, this study is based on the data from a single-center database, which may limit the external validity of the results. Fourth, the calculation of the HGI is based on the value first measured at the time of admission and therefore fails to reflect the dynamic changes in the HGI over time. Finally, detailed information about the treatments received by the patients, such as thrombolytic therapy and antiplatelet therapy, which could have affected the outcomes of the patients, could not be obtained.\u003c/p\u003e \u003cp\u003eClinical significance and future directions\u003c/p\u003e \u003cp\u003eThe results of this study may have great clinical importance. First, as a simple and easy-to-obtain indicator, the HGI could be included in the routine assessments of IS patients, especially elderly patients. Second, the identification of the optimal HGI threshold could provide a reference for the clinical identification of high-risk patients. Third, an understanding of the interaction between the HGI and age can be helpful for developing more accurate individualized treatment strategies. In the future, studies with larger sample sizes will be conducted to understand the relationship between the dynamic changes in the HGI and patient outcomes and to determine the mechanism of action of this relationship to support the findings of this study.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, the HGI was revealed to be an independent protective factor against 30-day and 1-year mortality in IS patients as well as a cofactor alongside age to affect the outcomes of IS patients. A \"J\"-shaped nonlinear relationship was observed between the HGI and mortality risk, with excessively low HGI values associated with a significantly increased risk of mortality. The mediating role of the HGI in the relationship between age and mortality provides a new perspective for understanding the metabolic characteristics of elderly IS patients. The ML-based prediction models further confirmed the clinical predictive value of the HGI. These findings provide new ideas and tools for risk assessment and individualized management in IS patients.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003eAuthor contribution statement\u003c/p\u003e\n\u003cp\u003eXinyu Tong and Jianxiong Gu were responsible for data collection, analysis, and first-draft writing. Chuxin Lyu was involved in the study design and data interpretation. Yichun Zhao assisted in statistical analysis and chart production. As the corresponding author, Minjie Guo and Ying Rui were responsible for the research conception, design, and supervision of the entire research process, and approved the final version of the paper. All authors read and agree to publish the final manuscript and agree to be responsible for all aspects of the research work, ensuring that the accuracy and completeness of the questions are properly investigated and addressed.\u003c/p\u003e\n\u003cp\u003eEthical Approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study complies with the tenets of the \u003cem\u003eDeclaration of Helsinki\u003c/em\u003e, and the need for informed consent was waived due to the use of anonymized data. The institutional review board of Beth Israel Deaconess Medical Center (BIDMC) waived the need for informed consent due to the use of anonymized data.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by\u0026nbsp;Innovation and Development Fund of Wuxi City Traditional Chinese Medicine Hospital (ZYYZD24003) and Scientific Research Project of Jiangsu Province Traditional Chinese Medicine Society (ZXFZ2024035).\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are openly available in the Medical Information Mart for Intensive Care (MIMIC)-IV database at https://doi.org/10.13026/kpb9-mt58.\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTSAO C W et al. 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Diabetes Complicat.\u003c/em\u003e \u003cb\u003e36\u003c/b\u003e (7), 108223. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jdiacomp.2022.108223\u003c/span\u003e\u003cspan address=\"10.1016/j.jdiacomp.2022.108223\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6680914/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6680914/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo investigate the associations between both age and the hemoglobin glycation index (HGI) and the 30-day and 1-year mortality in ischemic stroke (IS) patients and to analyze the mediating effect of the HGI on the relationship between age and mortality.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 3269 hospitalized patients with IS included in the Medical Information Mart for Intensive Care (MIMIC)-IV database were included in this study. The effects of age and HGI on short- (30 days) and long-term (1 year) mortality were analyzed with logistic, Cox, and least absolute shrinkage and selection operator (LASSO) regression analysis. The nonlinear relationship among the variables was further investigated via restriction cubic spline (RCS) analysis, and the mediating effects of HGI on the age-mortality relationship were confirmed via mediation analysis. Kaplan\u0026ndash;Meier (K\u0026ndash;M) survival curves and restricted mean survival time (RMST) analyses were used to evaluate the differences in survival among patients with different HGI levels. Finally, multiple machine learning (ML) models were constructed and subsequently evaluated in terms of predictive performance.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eLogistic and Cox regression analyses revealed that a lower HGI and a greater age were significantly associated with higher risks of 30-day and 1-year mortality (both P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). RCS analysis revealed a J-shaped relationship between HGI and mortality risk. Mediation analysis revealed that HGI had a negative mediating effect on the relationship between age and mortality. K\u0026ndash;M curve and RMST analyses further revealed that patients with higher HGIs had greater probabilities of survival. ML models also confirmed the importance of HGI in predicting the risk of mortality.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAge and HGI are correlated with both the 30-day and 1-year risks of mortality in IS patients. The HGI may play a partial mediating role between age and the risk of mortality.\u003c/p\u003e","manuscriptTitle":"Associations between age and the hemoglobin glycation index and 30-day and 1-year mortality in ischemic stroke patients: Mediation analyses and machine learning in a cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-18 08:36:39","doi":"10.21203/rs.3.rs-6680914/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-07T05:59:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-06T11:30:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-25T12:53:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"31693198274287448573984832528718389868","date":"2025-06-25T11:08:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216970063080576887392799534537024398826","date":"2025-06-25T10:14:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"249066300669415643193251352782019132766","date":"2025-06-13T15:44:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"23424468214502990047315364947840077429","date":"2025-06-13T13:31:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-13T10:07:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-13T09:19:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-30T11:18:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-30T03:41:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-05-16T12:41:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ce847bfd-ddff-44d8-baef-484469823f82","owner":[],"postedDate":"June 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":50088119,"name":"Biological sciences/Neuroscience"},{"id":50088120,"name":"Health sciences/Neurology"}],"tags":[],"updatedAt":"2025-08-04T16:48:48+00:00","versionOfRecord":{"articleIdentity":"rs-6680914","link":"https://doi.org/10.1038/s41598-025-14028-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-08-02 16:05:35","publishedOnDateReadable":"August 2nd, 2025"},"versionCreatedAt":"2025-06-18 08:36:39","video":"","vorDoi":"10.1038/s41598-025-14028-6","vorDoiUrl":"https://doi.org/10.1038/s41598-025-14028-6","workflowStages":[]},"version":"v1","identity":"rs-6680914","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6680914","identity":"rs-6680914","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}