Association between different hemoglobin glycation index and poor prognosis in patients with a first diagnosis of acute myocardial infarction-a study based on the MIMIC-IV database

Association between different hemoglobin glycation index and poor prognosis in patients with a first diagnosis of acute myocardial infarction-a study based on the MIMIC-IV database | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between different hemoglobin glycation index and poor prognosis in patients with a first diagnosis of acute myocardial infarction-a study based on the MIMIC-IV database Ben Hu, Linlin Hou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4143857/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The relationship between HGI and short-term mortality risk in patients with a first diagnosis of acute myocardial infarction (AMI) remains unclear. This study sought to understand better the relationship between HGI and mortality risk in patients with a first diagnosis of AMI. Methods We conducted a cohort study using data from 1961 patients with a first diagnosis of AMI from the MIMIC-IV (version 2.2) database. Patients were divided into four groups based on HGI quartiles. The Cox proportional hazards model and a two-segmented Cox proportional hazards model were used to elucidate the nonlinear relationship between HGI in patients with a first diagnosis of AMI and mortality were used to elucidate the nonlinear relationship between HGI in patients with a first diagnosis of AMI and mortality. Results Of the surveyed population, 175 patients (8.92%) died within 90 days, and 210 patients (10.71%) died within 180 days. A low HGI was significantly associated with 90-day mortality [HR, 1.96; 95% CI, (1.26, 3.05); P < 0.001] and 180-day mortality [HR, 1.62; 95% CI, (1.10, 2.38); P < 0.001] in patients with a first diagnosis of AMI in the completely adjusted Cox proportional risk model, showing a non-linear correlation with an inflection point at 0.16 and 0.44. In the subgroup analysis, patients with pre-diabetes mellitus (pre-DM) and lower HGI levels had increased 90-day (HR 8.30; 95% CI 2.91, 23.68) and 180-day mortality risks (HR 6.84; 95% CI 2.86, 16.34). Conclusion There is a significant correlation between HGI and all-cause mortality in patients diagnosed with AMI, especially those with lower HGI. HGI can serve as a potential indicator for evaluating the 90 and 180-day death risk of such patients. Endocrinology & Metabolism acute myocardial infarction hemoglobin glycation index MIMIC-Ⅳ All-cause mortality Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction As the global population ages and expands, acute myocardial infarction (AMI) has become the leading cause of death globally ( 1 ), - killing more than 2 million people in the United States each year ( 2 ). With the standardisation of pharmacological treatments and the development of coronary interventions, the mortality rate of AMI has fallen dramatically ( 3 ). However, an increasing number of patients have adverse outcomes that may be exacerbated by metabolic disorders ( 4 ) and the prognostic management of patients with AMI is a current medical priority that requires attention. Stable blood glucose levels have long-term benefits for AMI patients ( 5 ). Glycated haemoglobin (HbA1c) is used in the diagnosis and treatment of diabetes, reflects an individual's average blood glucose level over a three-month period, and is currently the most commonly used surrogate for the effectiveness of glucose-lowering interventions ( 6 ). However, some studies have suggested that erythrocyte renewal and glucose gradients across the erythrocyte membrane may influence HbA1c levels, and in addition, mean erythrocyte lifespan, differences in cell membrane glucose transmembrane gradients, enzyme abnormalities, and genetic factors have independent effects on HbA1c ( 7 ). Therefore, HbA1c measurements do not fully reflect the state of glycaemic metabolism. Hempe et al. developed the haemoglobin glycation index (HGI) with the aim of quantifying the relationship between HbA1c and plasma glucose concentration ( 8 ). HGI defined as the difference between observed and predicted HbA1c in a linear regression equation fitted to FPG ( 9 ). Several studies have shown that HGI can predict the risk of diabetic complications, including nephropathy and microvascular complications ( 10 ). In addition, studies have shown that high HGI is strongly associated with an increased risk of atherosclerosis ( 11 ). However, some studies have shown that both low and high HGI are associated with an increased risk ( 12 , 13 ). In different populations, different HGI values reflect individual differences in glucose metabolism and Hb glycosylation. In addition, in patients with myocardial infarction, glycaemic control is particularly important for their prognosis. Previous studies have shown that the HGI can be a relatively intuitive reflection of a patient's glycaemic variability ( 14 ). In addition, HGI is related to HbA1c and FPG, which reflect long-term and short-term glycaemic control. Enhanced management of patients' HGI may also improve their glycaemic control. There are relatively few studies on HGI in patients with AMI. Exploring the relationship between HGI and the prognosis of AMI patients will help to understand the relationship between glycaemic metabolic status and the survival of AMI patients and to identify high-risk patients. Therefore, in this study, we used the MIMIC-IV (version 2.2) database to construct linear regression equations to calculate HGI in patients with AMI and to analyse the correlation between HGI and adverse outcomes. 2. Method 2.1 Data source and study population This retrospective study utilized health-related data from the MIMIC-IV (version 2.2) database ( 15 ). To access the database, the author (XHC) obtained the necessary certification and then extracted the required variables (Certification No.: 58951192). In the final analysis, 1961 individuals were included and categorized into four groups based on the quartiles of their HGI levels (Fig. 1) . 2.2 Data collection Data extraction was performed using the PostgreSQL tool. Extracted variables included: ( 1 ) Baseline characteristics; ( 2 ) Comorbidities; ( 3 ) Laboratory parameters: ; ( 4 ) Medication use; ( 5 ) Hospital and ICU admission data. Specific details and query codes for each metric are available in Table S1 . 2.3 Data definitions The Body Mass Index (BMI) was calculated as weight (kg) divided by the square of height (m). Diabetes was defined as a history of diabetes or HbA1c > 6.5%. Pre-DM was defined as patients without a history of diabetes but with HbA1c levels between 5.7–6.4%. Normoglycemia (NGR) was identified in patients without a history of diabetes or with HbA1c ≤ 5.7% ( 16 ). 2.4 Exposure Variables A linear regression model between FPG and HbA1c was developed based on all patients included in this study. Based on this, the predicted HbA1c was calculated (predicted HbA1c = 0.015*FPG + 4.350) and subsequently the difference between the observed and predicted values of HbA1c was calculated as the HGI ( 17 ). The correlation between HGI and HbA1c is shown in Fig. 2 . 2.5 Outcomes Variables The primary outcomes were mortality rates at 90 and 180 days post-admission, calculated based on the first AMI diagnosis time and the follow-up death dates from MIMIC-IV 2.2. 2.6 Statistical analysis Based on the baseline HGI quartiles, data are expressed as mean (SD) or median (interquartile range) for continuous variables and frequency (percentage) for categorical variables. Differences in HGI levels were assessed using the chi-squared test (for categorical variables), one-way ANOVA (for normally distributed data), and the Kruskal-Wallis H test (for skewed data). The third quartile of HGI served as the reference group. The multiple Cox proportional hazards regression models were utilized to calculate Hazard ratios (HRs) and 95% confidence intervals (CIs). Model 1 did not adjust for any covariates. Any covariate altering the resulting estimate by more than 10% was included in Model 2 as a potential confounder. If the covariate changed the resulting estimate by more than 10% or had a regression coefficient P-value < 0.1, it was considered a potential confounder in Model 3 ( 18 ). Additionally, we employed restricted cubic splines to explore dose-response relationships according to the Akaike information criterion (AIC) minimum criteria. We performed sensitivity analyses to assess how reliable our results were. Firstly, distributions of variables with missing data comparing observed complete case data. Secondly, the association between HGI and the risk of 90-day or 180-day mortality was investigated using data before multiple imputations (n = 1358). Thirdly, further adjustments were made to the SIRS and SOFA scores based on clinical data (n = 1360). Finally, recognizing the varying diabetes status among patients, we performed subgroup analyses. R 4.3.0 ( http://www.R-project.org ) was used for all analyses. A two-sided P-value of less than 0.05 was deemed statistically significant. 3. Result 3.1 Baseline characteristics of patients with AMI Data were derived from 1961 AMI patients (mean age 66.65 years; 69.10% male). Of these, 210 patients experienced a fatal event during the 180-day follow-up period. Baseline data based on HGI quartiles are shown in Table 1 . Those with elevated HGI tended to be diabetes patients with increased BMI and HbA1c. However, the proportion of patients with high WBC, cardiogenic shock and atrial fibrillation was greater in the low HGI group. In addition, we found that glucose, heart failure and vasoactive drugs use were higher in both the high and low HGI groups than in the median HGI group.Notable differences in BMI, WBC, RBC, PLT, creatinine, glucose, HbA1c, heart failure, cardiogenic shock, diabetes, Antihypertensive drugs, antilipidemic drugs, antiplatelets, vasoactive drugs, 90-day and 180-day mortality were statistically significant across the three patient groups (all P < 0.05) ( Table 1 ) . Table 1 Baseline characteristics of patients according to quartiles of hemoglobin glycation index. Hemoglobin glycation index (HGI) quartile Characteristics Q1 (< -0.673) Q2 (≥-0.673, <-0.281) Q3 (≥-0.281, < 0.234) Q4 (≥ 0.234) P value Patients, n 489 490 490 492 Age (years) 66.54 (14.07) 66.14 (12.83) 67.33 (13.48) 66.59 (12.44) 0.559 Sex % 0.738 Male 341 (69.73%) 342 (69.80%) 342 (69.80%) 330 (67.07%) Female 148 (30.27%) 148 (30.20%) 148 (30.20%) 162 (32.93%) BMI kg/m2 28.69 (7.15) 28.90 (7.50) 29.11 (8.08) 30.08 (7.43) 0.021 WBC (1000 cells/uL) 11.82 (5.63) 10.88 (4.61) 10.56 (4.84) 10.14 (4.46) < 0.001 RBC (1000 cells/uL) 4.15 (0.80) 4.36 (0.74) 4.40 (0.64) 4.30 (0.75) < 0.001 PLT (1000 cells/uL) 225.84 (72.27) 234.49 (75.61) 240.77 (83.72) 236.39 (80.54) 0.024 Creatinine (mg/dL) 1.00 (0.80–1.40) 1.00 (0.80–1.20) 1.00 (0.80–1.20) 1.00 (0.80–1.30) < 0.001 Glucose (mg/dL) 155.48 (70.57) 116.02 (33.96) 118.03 (41.03) 153.68 (63.49) < 0.001 HbA1c (%) 5.53 (0.76) 5.68 (0.53) 6.11 (0.66) 8.46 (1.96) < 0.001 Heart failure % 178 (36.40%) 116 (23.67%) 137 (27.96%) 165 (33.54%) < 0.001 Cardiogenic shock % 78 (15.95%) 41 (8.37%) 35 (7.14%) 36 (7.32%) < 0.001 Cardiac arrest % 15 (3.07%) 8 (1.63%) 8 (1.63%) 6 (1.22%) 0.157 Atrial fibrillation % 145 (29.65%) 114 (23.27%) 114 (23.27%) 122 (24.80%) 0.068 Hypertension % 205 (41.92%) 233 (47.55%) 243 (49.59%) 238 (48.37%) 0.078 Diabetes % 123 (25.15%) 80 (16.33%) 150 (30.61%) 417 (84.76%) < 0.001 Antihypertensive drugs % 470 (96.11%) 470 (95.92%) 478 (97.55%) 486 (98.78%) 0.025 Antilipidemic drugs % 437 (89.37%) 468 (95.51%) 477 (97.35%) 472 (95.93%) < 0.001 Antiplatelets % 457 (93.46%) 480 (97.96%) 477 (97.35%) 477 (96.95%) < 0.001 Vasoactive drugs % 201 (41.10%) 154 (31.43%) 144 (29.39%) 202 (41.06%) < 0.001 90-day mortality % 74 (15.13%) 34 (6.94%) 29 (5.92%) 38 (7.72%) < 0.001 180-day mortality % 82 (16.77%) 47 (9.59%) 41 (8.37%) 40 (8.13%) < 0.001 Mean (SD) or median (interquartile range) for continuous variables and as frequencies (percentages) for categorical variables. Abbreviations: BMI: body mass index; WBC: white blood cell; RBC: red blood cell; PLT: platelet. 3.2 Associations between HGI and Outcomes Over the 90-day and 180-day follow-up periods post-admission, there were 175 and 210 recorded deaths, respectively. After multivariable adjustment for age, sex, BMI, cardiogenic shock, cardiac arrest, and hypertension, compared with the reference quartile, the first quartile showed a significant association with HGI concerning 90-day and 180-day mortality in Model 2. With further adjustments for potential confounder, the results were consistent in Model 3, multivariable-adjusted HRs (95% CIs) quartiles of across HGI were 1.96 (1.26, 3.05), 1.09 (0.66, 1.82), 1.00 (reference), and 1.22 (0.73, 2.04) (P trend = 0.007); and 1.62 (1.10, 2.38), 1.11 (0.72, 1.71), 1.00 (reference), and 0.90 (0.57, 1.44) (P trend = 0.002), respectively ( Table 2 ) . Table 2 Multivariable Cox regression analyses for 180-day and 90-day mortality in patients with acute myocardial infarction. Model 1: no covariates were adjusted. Model 2: we only adjusted for age, sex (male, female), BMI, cardiogenic shock (yes, no), cardiac arrest (yes, no), hypertension (yes, no). Model 3: we additionally adjusted for WBC, RBC, PLT, creatinine, diabetes (yes, no), heart failure (yes, no), atrial fibrillation (yes, no), insulin (yes, no), antihypertensive drugs (yes, no), antilipidemic drugs (yes, no), antiplatelets (yes, no), vasoactive (yes, no). Outcomes Exposure Model 1 HR,95%CI Model 2 HR,95%CI Model 3 HR,95%CI 90-day mortality HGI index (quartiles) Q1 2.72 (1.77, 4.18)*** 2.54 (1.65, 3.91)*** 1.96 (1.26, 3.05)*** Q2 1.18 (0.72, 1.94) 1.29 (0.78, 2.12) 1.09 (0.66, 1.82) Q3 Ref Ref Ref Q4 1.31 (0.81, 2.13) 1.46 (0.90, 2.38) 1.22 (0.73, 2.04) P for trend < 0.001 < 0.001 0.007 180-day mortality HGI index (quartiles) Q1 2.14 (1.47, 3.12) *** 2.03 (1.39, 2.95) *** 1.62 (1.10, 2.38) *** Q2 1.16 (0.76, 1.76) 1.27 (0.83, 1.93) 1.11 (0.72, 1.71) Q3 Ref Ref Ref Q4 0.98 (0.63, 1.51) 1.09 (0.71, 1.70) 0.90 (0.57, 1.44) P for trend < 0.001 < 0.001 0.002 The dose-response relationship between HGI and the adjusted hazard ratio for 90-day and 180-day mortality in AMI patients was depicted using restricted cubic splines. A L-shaped association between HGI and the 90-day and 180-day mortality rates was observed (Fig. 3) . Additionally, a combination of Cox proportional hazard models with a two-segmented Cox proportional hazards model was employed to study the non-linear relationship between HGI levels in AMI patients and the mortality mentioned above rates (P for log-likelihood ratio < 0.05) ( Table 3 ) . An inflection points were detected at HGI of 0.16 and 0.44,.respectively. When the HGI lower than 0.16 and 0.44, for each unit increase in the HGI level, the adjusted HRs for 90-day and 180-day mortality decrease by 28% (HR 0.72; 95% CI, 0.61 to 0.85) and 25% (HR 0.75; 95% CI, 0.64 to 0.88), respectively. We compared the incidence of the outcome between groups using Kaplan-Meier survival analysis curves based on HGI quartiles. The Q1 group had significantly higher mortality rates at 90 and 180 days than the other groups (log-rank P < 0.001) (Fig. 4) . This suggests that low HGI is detrimental to the survival of patients with AMI. Table 3 Threshold effect analysis of HGI index on 90-day and 180-day mortality in acute myocardial infarction patients. The adjustment strategy is the same as the Model 3. Adjusted HR (95% CI), P-value 90-day mortality Standard linear regression 0.92 (0.81, 1.04) 0.173 Fitting model by two-piecewise linear regression Inflection point 0.16 HGI index < 0.9 0.72 (0.61, 0.85) 0.9 1.15 (0.99, 1.33) 0.068 P for the Log-likelihood ratio < 0.001 180-day mortality Standard linear regression 0.91 (0.81, 1.02) 0.103 Fitting model by two-piecewise linear regression Inflection point 0.44 HGI index < 0.9 0.75 (0.64, 0.88) 0.9 1.12 (0.96, 1.31) 0.136 P for the Log-likelihood ratio 0.003 3.3 Stratified analysis After stratification of studies by age, sex, BMI, hypertension, heart failure, cardiogenic shock, and diabetes to explore the associations with 90-day and 180-day mortality. Consistent results were observed in people over age 65 who were female, BMI (25–30 kg/m2), with cardiogenic shock, without heart failure or diabetes. The model's interaction tests for covariates with HGI were non-significant (P for interaction > 0.05) (Table S2 and S3) . 3.4 Sensitivity analysis The characteristics of raw data and data after multiple imputations (Table S4) . Similar results were observed when we used data before multiple imputations to investigate the association between HGI and the risk of 90-day and 180-day mortality using multivariate Cox regression models (Table S5) . Similar results were also observed when further adjusting for SIRS and SOFA (Table S6) . When we conducted a subgroup analysis for individuals with different diabetes statuses, the results were consistent among those with pre-diabetes ( Tables 4 and 5 ) . Table 4 Multivariable Cox regression analyses for 90-day mortality in patients with acute myocardial infarction and different diabetes mellitus. The adjustment strategy is the same as the Table 2 . P-value *P < 0.05 **P < 0.01 ***P < 0.001. Diabetes status Model 1 HR,95%CI Model 2 HR,95%CI Model 3 HR,95%CI NGR HGI index (quartiles) Q1 2.24 (0.94, 5.32) 1.86 (0.78, 4.47) 1.68 (0.66, 4.26) Q2 0.90 (0.35, 2.32) 0.98 (0.38, 2.54) 1.16 (0.43, 3.17) Q3 Ref Ref Ref Q4 0.00 (0.00, Inf) 0.00 (0.00, Inf) 0.00 (0.00, Inf) P for trend 0.005 0.043 0.162 Pre-DM HGI index (quartiles) Q1 9.43 (4.03, 22.06) *** 8.61 (3.54, 20.92) *** 8.30 (2.91, 23.68) *** Q2 1.96 (0.73, 5.25) 1.77 (0.64, 4.88) 1.75 (0.54, 5.66) Q3 Ref Ref Ref Q4 1.14 (0.25, 5.28) 1.08 (0.23, 5.02) 1.06 (0.20, 5.54) P for trend < 0.001 < 0.001 < 0.001 DM HGI index (quartiles) Q1 2.47 (1.28, 4.74) ** 2.07 (1.06, 4.03) * 1.73 (0.87, 3.45) Q2 1.77 (0.82, 3.83) 1.63 (0.75, 3.54) 0.95 (0.41, 2.20) Q3 Ref Ref Ref Q4 0.91 (0.49, 1.69) 1.01 (0.54, 1.88) 0.94 (0.50, 1.77) P for trend < 0.001 0.005 0.051 Table 5 Multivariable Cox regression analyses for 180-day mortality in patients with acute myocardial infarction and different diabetes mellitus. The adjustment strategy is the same as the Table 2 . P-value *P < 0.05 **P < 0.01 ***P < 0.001. Diabetes status Model 1 HR,95%CI Model 2 HR,95%CI Model 3 HR,95%CI NGR HGI index (quartiles) Q1 1.71 (0.83, 3.51) 1.44 (0.69, 2.99) 1.33 (0.60, 2.93) Q2 0.80 (0.36, 1.75) 0.87 (0.40, 1.92) 1.04 (0.45, 2.42) Q3 Ref Ref Ref Q4 0.00 (0.00, Inf) 0.00 (0.00, Inf) 0.00 (0.00, Inf) P for trend 0.019 0.117 0.335 Pre-DM HGI index (quartiles) Q1 7.28 (3.42, 15.49) *** 6.95 (3.15, 15.36) *** 6.84 (2.86, 16.34) *** Q2 2.16 (0.97, 4.82) 2.16 (0.95, 4.92) 1.87 (0.74, 4.69) Q3 Ref Ref Ref Q4 0.79 (0.18, 3.51) 0.74 (0.17, 3.29) 0.74 (0.16, 3.47) P for trend < 0.001 < 0.001 < 0.001 DM HGI index (quartiles) Q1 1.98 (1.10, 3.56) * 1.74 (0.95, 3.17) 1.47 (0.79, 2.73) Q2 1.75 (0.90, 3.41) 1.65 (0.85, 3.23) 1.05 (0.51, 2.15) Q3 Ref Ref Ref Q4 0.71 (0.41, 1.23) 0.79 (0.45, 1.39) 0.75 (0.43, 1.33) P for trend < 0.001 < 0.001 0.014 4. Discussion In this study, we used data from the MIMIC-IV database (version 2.2) to reveal an independent association between HGI and 90- and 180-day mortality in patients with a first diagnosis of AMI. This association was particularly evident in patients with prediabetes. In addition, we observed an L-shaped curve relationship between HGI levels and 90- and 180-day mortality in patients diagnosed with AMI. Therefore, HGI may be an independent risk factor for AMI patients. Understanding and HGI levels in patients with AMI may help to improve subsequent health outcomes in these individuals. In clinical practice, linear regression models are needed to calculate HGI in large samples of patients. HbA1c is glycated haemoglobin formed by a non-enzymatic intracellular reaction, whereas FPG reflects plasma glucose status ( 8 ). There is significant inter-individual variability in the association between HbA1c and plasma glucose concentration, which is quantified by the HGI, which is regarded as a marker of the inherent risk of developing diabetic complications and is advocated as a clinical tool for identifying high-risk diabetic patients ( 14 ). In the Diabetes Control and Complications Trial, patients in the high HGI group had a 3-fold increased risk of retinopathy and a 6-fold increased risk of nephropathy when 1,441 patients with type 1 diabetes were followed for 7 years ( 19 ). This suggests that the HGI or other biological variants of HbA1c may prove to be clinically important in identifying high-risk patients and monitoring treatment outcomes. However, the impact of this variability on cardiovascular disease and mortality remains controversial ( 20 ). Several previous studies have shown that HGI is strongly associated with adverse cardiovascular events ( 21 ), kidney injury ( 22 ) and NAFLD ( 23 ). Some studies suggest that both high HGI and low HGI seem to be associated with poor prognosis to varying degrees. In a cohort study from China, both low and high HGI were associated with an increased risk of poor outcome in patients with acute coronary syndromes after a median follow-up time of 3 years ( 13 ). However, in another cohort study of 1910 patients with T2DM, low HGI was found to be a possible risk factor for myocardial infarction in patients with coronary artery disease, but the benefit was limited compared with HbA1c ( 20 ). The HGI quantifies the magnitude and direction of the difference between the set of observed and predicted HbA1c outcomes for each patient, and additionally, it is noteworthy that the FPG levels in Q1 were significantly higher than the other groups in both our study. Stress hyperglycaemia may lead to high FPG followed by low HGI, and stress activates the hypothalamic-pituitary-adrenal axis and the sympathetic-adrenal system, increasing the release of pro-inflammatory cytokines that exacerbate the severity of coronary artery disease in patients with coronary artery disease ( 24 ) such as leading to endothelial dysfunction and exacerbating microvascular obstruction, which in turn damages the endothelium of the blood vessels ( 25 ). Thus, in the present study, relatively high stress glucose in the low HGI group may have mediated an increased risk of short-term all-cause mortality in AMI patients. In addition, our findings emphasise the strong association between HGI and mortality, especially in patients with pre-DM but not in those with diabetes. The underlying mechanism for the significantly increased risk of in-hospital death in non-diabetic patients compared to diabetic patients is elusive and may be attributed to several factors. Firstly, our study focused on mortality at 90 and 180 days after admission, representing short-term mortality. Secondly, based on the available evidence, glycation is a complex biological process, and factors affecting intracellular glucose concentration or non-enzymatic haemoglobin glycosylation may also influence the degree of haemoglobin glycation ( 26 ). Diabetic patients may exhibit insensitivity to HGI due to long-term adaptation to chronic inflammation and oxidative stress ( 27 ). Finally, FPG can be altered to varying degrees in diabetic patients treated with glucose-lowering drugs, with FPG levels being much lower than normal in patients with previous regular insulin therapy ( 17 ). They may therefore have a higher HGI. we should consider the potential beneficial outcomes in diabetic patients treated with intensive glucose-lowering therapy or other anti-inflammatory drugs despite adjusting insulin use ( 28 ). Based on stratified analyses, we observed that low HGI was associated with increased mortality in patients with AMI, especially in patients older than 65 years, with BMI (25–30 kg/m 2 ), without heart failure and without diabetes. Therefore, it can be used as a potential indicator for risk stratification of mortality in such patients and should be given extra attention in clinical practice. Our study has several limitations. First, as a single-centre study with a limited sample size, even though multivariate adjustment and subgroup analysis were performed, we may not have extracted the full clinical diagnostic information and sociodemographic indicators of the patients, and potential bias due to residual confounders may persist. Second, the association between HGI and adverse outcomes other than all-cause mortality was not considered in this study. Finally, in our study, HGI was calculated based on the study population and could not be generalised to other populations, and we believe that regression models should be built based on data retrieved from various large databases in order to calculate HGI in various populations. 5. Conclusion Using patient data retrieved from the MIMIC-IV database, we found a nonlinear relationship between HGI and all-cause mortality in patients with AMI. Our findings emphasise that low HGI is associated with increased mortality. We suggest that HGI is a good indicator of poor prognosis in patients with AMI, especially in pre-diabetic patients, and that this could be used as a potential indicator for risk stratification of mortality in such patients.Patients with AMI who have a low HGI should receive extra attention during hospitalisation. Declarations Ethics approval and consent to participate This study was conducted according to the guidelines of the Declaration of Helsinki. The review boards of the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center approved the use of the MIMIC-IV database. Because the data were publicly available, the study was exempt from the requirements of an ethics approval statement and informed consent. Consent for publication All authors agree to publish this work Availability of data and materials The data utilized in the current study can be obtained from the corresponding author upon reasonable request Competing interests The authors have no conflict of interests Funding There were no external funding sources for this study. Authors’ contributions BH, XW and XHC designed the study. XHC extracted clinical data from the MIMIC-IV database. XW, BH, XXS, SHY performed the statistical analysis of the data. BH authored the first draft. LLH and JF examined and revised the paper. All authors read and approved the final manuscript. 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(2002) 16:313-20. doi: 10.1016/s1056-8727(01)00227-6 Soros AA, Chalew SA, Mccarter RJ, Shepard R, Hempe JM. Hemoglobin glycation index: a robust measure of hemoglobin a1c bias in pediatric type 1 diabetes patients. Pediatr Diabetes . (2010) 11:455-61. doi: 10.1111/j.1399-5448.2009.00630.x Xin S, Zhao X, Ding J, Zhang X. Association between hemoglobin glycation index and diabetic kidney disease in type 2 diabetes mellitus in china: a cross- sectional inpatient study. Front Endocrinol (Lausanne) . (2023) 14:1108061. doi: 10.3389/fendo.2023.1108061 Marini MA, Fiorentino TV, Succurro E, Pedace E, Andreozzi F, Sciacqua A, et al. Association between hemoglobin glycation index with insulin resistance and carotid atherosclerosis in non-diabetic individuals. PLoS One . (2017) 12:e175547. doi: 10.1371/journal.pone.0175547 Wang Y, Liu H, Hu X, Wang A, Wang A, Kang S, et al. Association between hemoglobin glycation index and 5-year major adverse cardiovascular events: the reaction cohort study. Chin Med J (Engl) . (2023) 136:2468-75. doi: 10.1097/CM9.0000000000002717 Li J, Xin Y, Li J, Zhou L, Qiu H, Shen A, et al. Association of haemoglobin glycation index with outcomes in patients with acute coronary syndrome: results from an observational cohort study in china. Diabetol Metab Syndr . (2022) 14:162. doi: 10.1186/s13098-022-00926-6 Hempe JM, Hsia DS. Variation in the hemoglobin glycation index. J Diabetes Complications . (2022) 36:108223. doi: 10.1016/j.jdiacomp.2022.108223 Johnson A, Bulgarelli L, Shen L, Gayles A, Shammout A, Horng S, et al. Mimic-iv, a freely accessible electronic health record dataset. Sci Data . (2023) 10:1. doi: 10.1038/s41597-022-01899-x Kilpatrick ES, Bloomgarden ZT, Zimmet PZ. International expert committee report on the role of the a1c assay in the diagnosis of diabetes: response to the international expert committee. Diabetes Care . (2009) 32:e159, author reply e160. doi: 10.2337/dc09-1231 Hempe JM, Liu S, Myers L, Mccarter RJ, Buse JB, Fonseca V. The hemoglobin glycation index identifies subpopulations with harms or benefits from intensive treatment in the accord trial. Diabetes Care . (2015) 38:1067-74. doi: 10.2337/dc14-1844 Jaddoe VW, de Jonge LL, Hofman A, Franco OH, Steegers EA, Gaillard R. First trimester fetal growth restriction and cardiovascular risk factors in school age children: population based cohort study. BMJ . (2014) 348:g14. doi: 10.1136/bmj.g14 Mccarter RJ, Hempe JM, Gomez R, Chalew SA. Biological variation in hba1c predicts risk of retinopathy and nephropathy in type 1 diabetes. Diabetes Care . (2004) 27:1259-64. doi: 10.2337/diacare.27.6.1259 Ostergaard HB, Mandrup-Poulsen T, Berkelmans G, van der Graaf Y, Visseren F, Westerink J. Limited benefit of haemoglobin glycation index as risk factor for cardiovascular disease in type 2 diabetes patients. Diabetes Metab . (2019) 45:254-60. doi: 10.1016/j.diabet.2018.04.006 Klein KR, Franek E, Marso S, Pieber TR, Pratley RE, Gowda A, et al. Hemoglobin glycation index, calculated from a single fasting glucose value, as a prediction tool for severe hypoglycemia and major adverse cardiovascular events in devote. BMJ Open Diabetes Res Care . (2021) 9. doi: 10.1136/bmjdrc-2021-002339 Lin CH, Lai YC, Chang TJ, Jiang YD, Chang YC, Chuang LM. Hemoglobin glycation index predicts renal function deterioration in patients with type 2 diabetes and a low risk of chronic kidney disease. Diabetes Res Clin Pract . (2022) 186:109834. doi: 10.1016/j.diabres.2022.109834 Wang M, Li S, Zhang X, Li X, Cui J. Association between hemoglobin glycation index and non-alcoholic fatty liver disease in the patients with type 2 diabetes mellitus. J Diabetes Investig . (2023) 14:1303-11. doi: 10.1111/jdi.14066 Marik PE, Bellomo R. Stress hyperglycemia: an essential survival response!. Crit Care . (2013) 17:305. doi: 10.1186/cc12514 Yang J, Zheng Y, Li C, Gao J, Meng X, Zhang K, et al. The impact of the stress hyperglycemia ratio on short-term and long-term poor prognosis in patients with acute coronary syndrome: insight from a large cohort study in asia. Diabetes Care . (2022) 45:947-56. doi: 10.2337/dc21-1526 Chalew SA, Mccarter RJ, Thomas J, Thomson JL, Hempe JM. A comparison of the glycosylation gap and hemoglobin glycation index in patients with diabetes. J Diabetes Complications . (2005) 19:218-22. doi: 10.1016/j.jdiacomp.2005.01.004 Bahadoran Z, Mirmiran P, Ghasemi A. Role of nitric oxide in insulin secretion and glucose metabolism. Trends Endocrinol Metab . (2020) 31:118-30. doi: 10.1016/j.tem.2019.10.001 Dandona P, Chaudhuri A, Ghanim H, Mohanty P. Insulin as an anti-inflammatory and antiatherogenic modulator. J Am Coll Cardiol . (2009) 53:S14-20. doi: 10.1016/j.jacc.2008.10.038 Additional Declarations The authors declare no competing interests. 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Hou","email":"data:image/png;base64,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","orcid":"","institution":"Hefei Hospital Affiliated to Anhui Medical University","correspondingAuthor":true,"prefix":"","firstName":"Linlin","middleName":"","lastName":"Hou","suffix":""}],"badges":[],"createdAt":"2024-03-21 13:26:52","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-4143857/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4143857/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53255731,"identity":"c048065a-ec2f-49e5-90b6-63ab09c65f37","added_by":"auto","created_at":"2024-03-22 13:29:45","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":107023,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of study.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4143857/v1/4f8527e52baf9c642a1c75d0.jpg"},{"id":53255732,"identity":"1fbb9895-45e3-4d28-bca7-5201c2ee4fed","added_by":"auto","created_at":"2024-03-22 13:29:45","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":376264,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation between HGI and HbA1c.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4143857/v1/7bcebe1ea7f415d9fa2502f1.jpg"},{"id":53255734,"identity":"48956934-afa0-451e-9bc6-3c90e68e404f","added_by":"auto","created_at":"2024-03-22 13:29:45","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":438426,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline analyses\u0026nbsp;and comparison\u0026nbsp;of the association of HGI\u0026nbsp;with all-cause mortality (A: all-cause death in 90 days, B: all-cause death in 180 days).\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4143857/v1/250896a39a15797cf14528a6.jpg"},{"id":53255735,"identity":"503fd28e-64ec-43e0-9fa7-b2e82168c33b","added_by":"auto","created_at":"2024-03-22 13:29:45","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":431324,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival analysis curves for all-cause mortality. 90 days (A) and 180 days (B)\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4143857/v1/7143f709e64dc869ddbe1d2f.jpg"},{"id":53257075,"identity":"9d5640ef-a096-4659-ba7f-599daae7add8","added_by":"auto","created_at":"2024-03-22 13:45:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":663076,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4143857/v1/f6b37143-546a-4ec3-9aa5-f6d8830574c8.pdf"},{"id":53256557,"identity":"9b8f68f5-f708-49f8-bc61-20e275c9f61f","added_by":"auto","created_at":"2024-03-22 13:37:45","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":36259,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4143857/v1/45c25622866019471581922a.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eAssociation between different hemoglobin glycation index and poor prognosis in patients with a first diagnosis of acute myocardial infarction-a study based on the MIMIC-IV database\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAs the global population ages and expands, acute myocardial infarction (AMI) has become the leading cause of death globally (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), - killing more than 2\u0026nbsp;million people in the United States each year (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). With the standardisation of pharmacological treatments and the development of coronary interventions, the mortality rate of AMI has fallen dramatically (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). However, an increasing number of patients have adverse outcomes that may be exacerbated by metabolic disorders (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) and the prognostic management of patients with AMI is a current medical priority that requires attention.\u003c/p\u003e \u003cp\u003eStable blood glucose levels have long-term benefits for AMI patients (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Glycated haemoglobin (HbA1c) is used in the diagnosis and treatment of diabetes, reflects an individual's average blood glucose level over a three-month period, and is currently the most commonly used surrogate for the effectiveness of glucose-lowering interventions (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). However, some studies have suggested that erythrocyte renewal and glucose gradients across the erythrocyte membrane may influence HbA1c levels, and in addition, mean erythrocyte lifespan, differences in cell membrane glucose transmembrane gradients, enzyme abnormalities, and genetic factors have independent effects on HbA1c (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Therefore, HbA1c measurements do not fully reflect the state of glycaemic metabolism. Hempe et al. developed the haemoglobin glycation index (HGI) with the aim of quantifying the relationship between HbA1c and plasma glucose concentration (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). HGI defined as the difference between observed and predicted HbA1c in a linear regression equation fitted to FPG (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral studies have shown that HGI can predict the risk of diabetic complications, including nephropathy and microvascular complications (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In addition, studies have shown that high HGI is strongly associated with an increased risk of atherosclerosis (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). However, some studies have shown that both low and high HGI are associated with an increased risk (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). In different populations, different HGI values reflect individual differences in glucose metabolism and Hb glycosylation. In addition, in patients with myocardial infarction, glycaemic control is particularly important for their prognosis. Previous studies have shown that the HGI can be a relatively intuitive reflection of a patient's glycaemic variability (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). In addition, HGI is related to HbA1c and FPG, which reflect long-term and short-term glycaemic control. Enhanced management of patients' HGI may also improve their glycaemic control. There are relatively few studies on HGI in patients with AMI. Exploring the relationship between HGI and the prognosis of AMI patients will help to understand the relationship between glycaemic metabolic status and the survival of AMI patients and to identify high-risk patients. Therefore, in this study, we used the MIMIC-IV (version 2.2) database to construct linear regression equations to calculate HGI in patients with AMI and to analyse the correlation between HGI and adverse outcomes.\u003c/p\u003e"},{"header":"2. Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data source and study population\u003c/h2\u003e \u003cp\u003eThis retrospective study utilized health-related data from the MIMIC-IV (version 2.2) database (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). To access the database, the author (XHC) obtained the necessary certification and then extracted the required variables (Certification No.: 58951192). In the final analysis, 1961 individuals were included and categorized into four groups based on the quartiles of their HGI levels \u003cb\u003e(Fig.\u0026nbsp;1)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data collection\u003c/h2\u003e \u003cp\u003eData extraction was performed using the PostgreSQL tool. Extracted variables included: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Baseline characteristics; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Comorbidities; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Laboratory parameters: ; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Medication use; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) Hospital and ICU admission data. Specific details and query codes for each metric are available in \u003cb\u003eTable S1\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data definitions\u003c/h2\u003e \u003cp\u003eThe Body Mass Index (BMI) was calculated as weight (kg) divided by the square of height (m). Diabetes was defined as a history of diabetes or HbA1c\u0026thinsp;\u0026gt;\u0026thinsp;6.5%. Pre-DM was defined as patients without a history of diabetes but with HbA1c levels between 5.7\u0026ndash;6.4%. Normoglycemia (NGR) was identified in patients without a history of diabetes or with HbA1c\u0026thinsp;\u0026le;\u0026thinsp;5.7% (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Exposure Variables\u003c/h2\u003e \u003cp\u003eA linear regression model between FPG and HbA1c was developed based on all patients included in this study. Based on this, the predicted HbA1c was calculated (predicted HbA1c\u0026thinsp;=\u0026thinsp;0.015*FPG\u0026thinsp;+\u0026thinsp;4.350) and subsequently the difference between the observed and predicted values of HbA1c was calculated as the HGI (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The correlation between HGI and HbA1c is shown in \u003cb\u003eFig.\u0026nbsp;2\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Outcomes Variables\u003c/h2\u003e \u003cp\u003eThe primary outcomes were mortality rates at 90 and 180 days post-admission, calculated based on the first AMI diagnosis time and the follow-up death dates from MIMIC-IV 2.2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eBased on the baseline HGI quartiles, data are expressed as mean (SD) or median (interquartile range) for continuous variables and frequency (percentage) for categorical variables. Differences in HGI levels were assessed using the chi-squared test (for categorical variables), one-way ANOVA (for normally distributed data), and the Kruskal-Wallis H test (for skewed data). The third quartile of HGI served as the reference group. The multiple Cox proportional hazards regression models were utilized to calculate Hazard ratios (HRs) and 95% confidence intervals (CIs). Model 1 did not adjust for any covariates. Any covariate altering the resulting estimate by more than 10% was included in Model 2 as a potential confounder. If the covariate changed the resulting estimate by more than 10% or had a regression coefficient P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.1, it was considered a potential confounder in Model 3 (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e Additionally, we employed restricted cubic splines to explore dose-response relationships according to the Akaike information criterion (AIC) minimum criteria. We performed sensitivity analyses to assess how reliable our results were. Firstly, distributions of variables with missing data comparing observed complete case data. Secondly, the association between HGI and the risk of 90-day or 180-day mortality was investigated using data before multiple imputations (n\u0026thinsp;=\u0026thinsp;1358). Thirdly, further adjustments were made to the SIRS and SOFA scores based on clinical data (n\u0026thinsp;=\u0026thinsp;1360). Finally, recognizing the varying diabetes status among patients, we performed subgroup analyses. R 4.3.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org\u003c/span\u003e\u003cspan address=\"http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used for all analyses. A two-sided P-value of less than 0.05 was deemed statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 Baseline characteristics of patients with AMI\u003c/h2\u003e\n\u003cp\u003eData were derived from 1961 AMI patients (mean age 66.65 years; 69.10% male). Of these, 210 patients experienced a fatal event during the 180-day follow-up period. Baseline data based on HGI quartiles are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Those with elevated HGI tended to be diabetes patients with increased BMI and HbA1c. However, the proportion of patients with high WBC, cardiogenic shock and atrial fibrillation was greater in the low HGI group. In addition, we found that glucose, heart failure and vasoactive drugs use were higher in both the high and low HGI groups than in the median HGI group.Notable differences in BMI, WBC, RBC, PLT, creatinine, glucose, HbA1c, heart failure, cardiogenic shock, diabetes, Antihypertensive drugs, antilipidemic drugs, antiplatelets, vasoactive drugs, 90-day and 180-day mortality were statistically significant across the three patient groups (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003cstrong\u003e(\u003c/strong\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eBaseline characteristics of patients according to quartiles of hemoglobin glycation index.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\u003ccolgroup\u003e\u003c/colgroup\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eHemoglobin glycation index (HGI) quartile\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCharacteristics\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ1 (\u0026lt; -0.673)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ2 (\u0026ge;-0.673, \u0026lt;-0.281)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ3 (\u0026ge;-0.281, \u0026lt;\u0026thinsp;0.234)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ4 (\u0026ge;\u0026thinsp;0.234)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePatients, n\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e489\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e490\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e490\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e492\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge (years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66.54 (14.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66.14 (12.83)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67.33 (13.48)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66.59 (12.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.559\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSex %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.738\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e341 (69.73%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e342 (69.80%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e342 (69.80%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e330 (67.07%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e148 (30.27%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e148 (30.20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e148 (30.20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e162 (32.93%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBMI kg/m2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.69 (7.15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.90 (7.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.11 (8.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.08 (7.43)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.021\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWBC (1000 cells/uL)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.82 (5.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.88 (4.61)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.56 (4.84)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.14 (4.46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRBC (1000 cells/uL)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.15 (0.80)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.36 (0.74)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.40 (0.64)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.30 (0.75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePLT (1000 cells/uL)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e225.84 (72.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e234.49 (75.61)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e240.77 (83.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e236.39 (80.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.024\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00 (0.80\u0026ndash;1.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00 (0.80\u0026ndash;1.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00 (0.80\u0026ndash;1.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00 (0.80\u0026ndash;1.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlucose (mg/dL)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e155.48 (70.57)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e116.02 (33.96)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e118.03 (41.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e153.68 (63.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHbA1c (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.53 (0.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.68 (0.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.11 (0.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.46 (1.96)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHeart failure %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e178 (36.40%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e116 (23.67%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e137 (27.96%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e165 (33.54%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCardiogenic shock %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e78 (15.95%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41 (8.37%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35 (7.14%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36 (7.32%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCardiac arrest %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (3.07%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (1.63%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (1.63%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (1.22%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.157\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAtrial fibrillation %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e145 (29.65%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e114 (23.27%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e114 (23.27%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e122 (24.80%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.068\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e205 (41.92%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e233 (47.55%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e243 (49.59%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e238 (48.37%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.078\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiabetes %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e123 (25.15%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80 (16.33%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e150 (30.61%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e417 (84.76%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAntihypertensive drugs %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e470 (96.11%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e470 (95.92%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e478 (97.55%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e486 (98.78%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAntilipidemic drugs %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e437 (89.37%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e468 (95.51%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e477 (97.35%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e472 (95.93%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAntiplatelets %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e457 (93.46%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e480 (97.96%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e477 (97.35%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e477 (96.95%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVasoactive drugs %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e201 (41.10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e154 (31.43%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e144 (29.39%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e202 (41.06%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90-day mortality %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e74 (15.13%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34 (6.94%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29 (5.92%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38 (7.72%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e180-day mortality %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82 (16.77%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47 (9.59%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41 (8.37%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40 (8.13%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eMean (SD) or median (interquartile range) for continuous variables and as frequencies (percentages) for categorical variables.\u003c/p\u003e\n\u003cp\u003eAbbreviations: BMI: body mass index; WBC: white blood cell; RBC: red blood cell; PLT: platelet.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2 Associations between HGI and Outcomes\u003c/h2\u003e\n\u003cp\u003eOver the 90-day and 180-day follow-up periods post-admission, there were 175 and 210 recorded deaths, respectively. After multivariable adjustment for age, sex, BMI, cardiogenic shock, cardiac arrest, and hypertension, compared with the reference quartile, the first quartile showed a significant association with HGI concerning 90-day and 180-day mortality in Model 2. With further adjustments for potential confounder, the results were consistent in Model 3, multivariable-adjusted HRs (95% CIs) quartiles of across HGI were 1.96 (1.26, 3.05), 1.09 (0.66, 1.82), 1.00 (reference), and 1.22 (0.73, 2.04) (P trend\u0026thinsp;=\u0026thinsp;0.007); and 1.62 (1.10, 2.38), 1.11 (0.72, 1.71), 1.00 (reference), and 0.90 (0.57, 1.44) (P trend\u0026thinsp;=\u0026thinsp;0.002), respectively \u003cstrong\u003e(\u003c/strong\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMultivariable Cox regression analyses for 180-day and 90-day mortality in patients with acute myocardial infarction. Model 1: no covariates were adjusted. Model 2: we only adjusted for age, sex (male, female), BMI, cardiogenic shock (yes, no), cardiac arrest (yes, no), hypertension (yes, no). Model 3: we additionally adjusted for WBC, RBC, PLT, creatinine, diabetes (yes, no), heart failure (yes, no), atrial fibrillation (yes, no), insulin (yes, no), antihypertensive drugs (yes, no), antilipidemic drugs (yes, no), antiplatelets (yes, no), vasoactive (yes, no).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOutcomes Exposure\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 1\u003c/p\u003e\n\u003cp\u003eHR,95%CI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 2\u003c/p\u003e\n\u003cp\u003eHR,95%CI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 3\u003c/p\u003e\n\u003cp\u003eHR,95%CI\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e90-day mortality\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHGI index (quartiles)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.72 (1.77, 4.18)***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.54 (1.65, 3.91)***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.96 (1.26, 3.05)***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.18 (0.72, 1.94)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.29 (0.78, 2.12)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.09 (0.66, 1.82)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.31 (0.81, 2.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.46 (0.90, 2.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.22 (0.73, 2.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP for trend\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e180-day mortality\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHGI index (quartiles)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.14 (1.47, 3.12) ***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.03 (1.39, 2.95) ***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.62 (1.10, 2.38) ***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.16 (0.76, 1.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.27 (0.83, 1.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.11 (0.72, 1.71)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.98 (0.63, 1.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.09 (0.71, 1.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.90 (0.57, 1.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP for trend\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe dose-response relationship between HGI and the adjusted hazard ratio for 90-day and 180-day mortality in AMI patients was depicted using restricted cubic splines. A L-shaped association between HGI and the 90-day and 180-day mortality rates was observed \u003cstrong\u003e(Fig.\u0026nbsp;3)\u003c/strong\u003e. Additionally, a combination of Cox proportional hazard models with a two-segmented Cox proportional hazards model was employed to study the non-linear relationship between HGI levels in AMI patients and the mortality mentioned above rates (P for log-likelihood ratio\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003cstrong\u003e(\u003c/strong\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e. An inflection points were detected at HGI of 0.16 and 0.44,.respectively. When the HGI lower than 0.16 and 0.44, for each unit increase in the HGI level, the adjusted HRs for 90-day and 180-day mortality decrease by 28% (HR 0.72; 95% CI, 0.61 to 0.85) and 25% (HR 0.75; 95% CI, 0.64 to 0.88), respectively. We compared the incidence of the outcome between groups using Kaplan-Meier survival analysis curves based on HGI quartiles. The Q1 group had significantly higher mortality rates at 90 and 180 days than the other groups (log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003cstrong\u003e(Fig.\u0026nbsp;4)\u003c/strong\u003e. This suggests that low HGI is detrimental to the survival of patients with AMI.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eThreshold effect analysis of HGI index on 90-day and 180-day mortality in acute myocardial infarction patients. The adjustment strategy is the same as the Model 3.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAdjusted HR (95% CI), P-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e90-day mortality\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStandard linear regression\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.92 (0.81, 1.04) 0.173\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFitting model by two-piecewise linear regression\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInflection point\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHGI index\u0026thinsp;\u0026lt;\u0026thinsp;0.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.72 (0.61, 0.85)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHGI index\u0026thinsp;\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.15 (0.99, 1.33) 0.068\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP for the Log-likelihood ratio\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e180-day mortality\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStandard linear regression\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.91 (0.81, 1.02) 0.103\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFitting model by two-piecewise linear regression\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInflection point\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.44\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHGI index\u0026thinsp;\u0026lt;\u0026thinsp;0.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.75 (0.64, 0.88)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHGI index\u0026thinsp;\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.12 (0.96, 1.31) 0.136\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP for the Log-likelihood ratio\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3 Stratified analysis\u003c/h2\u003e\n\u003cp\u003eAfter stratification of studies by age, sex, BMI, hypertension, heart failure, cardiogenic shock, and diabetes to explore the associations with 90-day and 180-day mortality. Consistent results were observed in people over age 65 who were female, BMI (25\u0026ndash;30 kg/m2), with cardiogenic shock, without heart failure or diabetes. The model's interaction tests for covariates with HGI were non-significant (P for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05) \u003cstrong\u003e(Table S2 and S3)\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003e3.4 Sensitivity analysis\u003c/h2\u003e\n\u003cp\u003eThe characteristics of raw data and data after multiple imputations \u003cstrong\u003e(Table S4)\u003c/strong\u003e. Similar results were observed when we used data before multiple imputations to investigate the association between HGI and the risk of 90-day and 180-day mortality using multivariate Cox regression models \u003cstrong\u003e(Table S5)\u003c/strong\u003e. Similar results were also observed when further adjusting for SIRS and SOFA \u003cstrong\u003e(Table S6)\u003c/strong\u003e. When we conducted a subgroup analysis for individuals with different diabetes statuses, the results were consistent among those with pre-diabetes \u003cstrong\u003e(\u003c/strong\u003eTables\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMultivariable Cox regression analyses for 90-day mortality in patients with acute myocardial infarction and different diabetes mellitus. The adjustment strategy is the same as the Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. P-value *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDiabetes status\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 1\u003c/p\u003e\n\u003cp\u003eHR,95%CI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 2\u003c/p\u003e\n\u003cp\u003eHR,95%CI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 3\u003c/p\u003e\n\u003cp\u003eHR,95%CI\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNGR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHGI index (quartiles)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.24 (0.94, 5.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.86 (0.78, 4.47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.68 (0.66, 4.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.90 (0.35, 2.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.98 (0.38, 2.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.16 (0.43, 3.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00 (0.00, Inf)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00 (0.00, Inf)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00 (0.00, Inf)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP for trend\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.043\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.162\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePre-DM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHGI index (quartiles)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.43 (4.03, 22.06) ***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.61 (3.54, 20.92) ***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.30 (2.91, 23.68) ***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.96 (0.73, 5.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.77 (0.64, 4.88)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.75 (0.54, 5.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.14 (0.25, 5.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.08 (0.23, 5.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.06 (0.20, 5.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP for trend\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHGI index (quartiles)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.47 (1.28, 4.74) **\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.07 (1.06, 4.03) *\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.73 (0.87, 3.45)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.77 (0.82, 3.83)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.63 (0.75, 3.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.95 (0.41, 2.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.91 (0.49, 1.69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.01 (0.54, 1.88)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.94 (0.50, 1.77)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP for trend\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.051\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMultivariable Cox regression analyses for 180-day mortality in patients with acute myocardial infarction and different diabetes mellitus. The adjustment strategy is the same as the Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. P-value *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDiabetes status\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 1\u003c/p\u003e\n\u003cp\u003eHR,95%CI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 2\u003c/p\u003e\n\u003cp\u003eHR,95%CI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 3\u003c/p\u003e\n\u003cp\u003eHR,95%CI\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNGR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHGI index (quartiles)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.71 (0.83, 3.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.44 (0.69, 2.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.33 (0.60, 2.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.80 (0.36, 1.75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.87 (0.40, 1.92)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.04 (0.45, 2.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00 (0.00, Inf)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00 (0.00, Inf)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00 (0.00, Inf)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP for trend\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.117\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.335\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePre-DM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHGI index (quartiles)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.28 (3.42, 15.49) ***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.95 (3.15, 15.36) ***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.84 (2.86, 16.34) ***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.16 (0.97, 4.82)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.16 (0.95, 4.92)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.87 (0.74, 4.69)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.79 (0.18, 3.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.74 (0.17, 3.29)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.74 (0.16, 3.47)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP for trend\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHGI index (quartiles)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.98 (1.10, 3.56) *\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.74 (0.95, 3.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.47 (0.79, 2.73)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.75 (0.90, 3.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.65 (0.85, 3.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.05 (0.51, 2.15)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.71 (0.41, 1.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.79 (0.45, 1.39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.75 (0.43, 1.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP for trend\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.014\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we used data from the MIMIC-IV database (version 2.2) to reveal an independent association between HGI and 90- and 180-day mortality in patients with a first diagnosis of AMI. This association was particularly evident in patients with prediabetes. In addition, we observed an L-shaped curve relationship between HGI levels and 90- and 180-day mortality in patients diagnosed with AMI. Therefore, HGI may be an independent risk factor for AMI patients. Understanding and HGI levels in patients with AMI may help to improve subsequent health outcomes in these individuals. In clinical practice, linear regression models are needed to calculate HGI in large samples of patients.\u003c/p\u003e \u003cp\u003eHbA1c is glycated haemoglobin formed by a non-enzymatic intracellular reaction, whereas FPG reflects plasma glucose status (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). There is significant inter-individual variability in the association between HbA1c and plasma glucose concentration, which is quantified by the HGI, which is regarded as a marker of the inherent risk of developing diabetic complications and is advocated as a clinical tool for identifying high-risk diabetic patients (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). In the Diabetes Control and Complications Trial, patients in the high HGI group had a 3-fold increased risk of retinopathy and a 6-fold increased risk of nephropathy when 1,441 patients with type 1 diabetes were followed for 7 years (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). This suggests that the HGI or other biological variants of HbA1c may prove to be clinically important in identifying high-risk patients and monitoring treatment outcomes. However, the impact of this variability on cardiovascular disease and mortality remains controversial (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Several previous studies have shown that HGI is strongly associated with adverse cardiovascular events (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), kidney injury (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) and NAFLD (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Some studies suggest that both high HGI and low HGI seem to be associated with poor prognosis to varying degrees. In a cohort study from China, both low and high HGI were associated with an increased risk of poor outcome in patients with acute coronary syndromes after a median follow-up time of 3 years (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). However, in another cohort study of 1910 patients with T2DM, low HGI was found to be a possible risk factor for myocardial infarction in patients with coronary artery disease, but the benefit was limited compared with HbA1c (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The HGI quantifies the magnitude and direction of the difference between the set of observed and predicted HbA1c outcomes for each patient, and additionally, it is noteworthy that the FPG levels in Q1 were significantly higher than the other groups in both our study. Stress hyperglycaemia may lead to high FPG followed by low HGI, and stress activates the hypothalamic-pituitary-adrenal axis and the sympathetic-adrenal system, increasing the release of pro-inflammatory cytokines that exacerbate the severity of coronary artery disease in patients with coronary artery disease (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) such as leading to endothelial dysfunction and exacerbating microvascular obstruction, which in turn damages the endothelium of the blood vessels (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Thus, in the present study, relatively high stress glucose in the low HGI group may have mediated an increased risk of short-term all-cause mortality in AMI patients.\u003c/p\u003e \u003cp\u003eIn addition, our findings emphasise the strong association between HGI and mortality, especially in patients with pre-DM but not in those with diabetes. The underlying mechanism for the significantly increased risk of in-hospital death in non-diabetic patients compared to diabetic patients is elusive and may be attributed to several factors. Firstly, our study focused on mortality at 90 and 180 days after admission, representing short-term mortality. Secondly, based on the available evidence, glycation is a complex biological process, and factors affecting intracellular glucose concentration or non-enzymatic haemoglobin glycosylation may also influence the degree of haemoglobin glycation (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Diabetic patients may exhibit insensitivity to HGI due to long-term adaptation to chronic inflammation and oxidative stress (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Finally, FPG can be altered to varying degrees in diabetic patients treated with glucose-lowering drugs, with FPG levels being much lower than normal in patients with previous regular insulin therapy (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). They may therefore have a higher HGI. we should consider the potential beneficial outcomes in diabetic patients treated with intensive glucose-lowering therapy or other anti-inflammatory drugs despite adjusting insulin use (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Based on stratified analyses, we observed that low HGI was associated with increased mortality in patients with AMI, especially in patients older than 65 years, with BMI (25\u0026ndash;30 kg/m\u003csup\u003e2\u003c/sup\u003e), without heart failure and without diabetes. Therefore, it can be used as a potential indicator for risk stratification of mortality in such patients and should be given extra attention in clinical practice.\u003c/p\u003e \u003cp\u003eOur study has several limitations. First, as a single-centre study with a limited sample size, even though multivariate adjustment and subgroup analysis were performed, we may not have extracted the full clinical diagnostic information and sociodemographic indicators of the patients, and potential bias due to residual confounders may persist. Second, the association between HGI and adverse outcomes other than all-cause mortality was not considered in this study. Finally, in our study, HGI was calculated based on the study population and could not be generalised to other populations, and we believe that regression models should be built based on data retrieved from various large databases in order to calculate HGI in various populations.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eUsing patient data retrieved from the MIMIC-IV database, we found a nonlinear relationship between HGI and all-cause mortality in patients with AMI. Our findings emphasise that low HGI is associated with increased mortality. We suggest that HGI is a good indicator of poor prognosis in patients with AMI, especially in pre-diabetic patients, and that this could be used as a potential indicator for risk stratification of mortality in such patients.Patients with AMI who have a low HGI should receive extra attention during hospitalisation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted according to the guidelines of the Declaration of Helsinki. The review boards of the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center approved the use of the MIMIC-IV database. Because the data were publicly available, the study was exempt from the requirements of an ethics approval statement and informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors agree to publish this work\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data utilized in the current study can be obtained from the corresponding author upon reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflict of interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were no external funding sources for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBH, XW and XHC designed the study. XHC extracted clinical data from the MIMIC-IV database. XW, BH, XXS, SHY performed the statistical analysis of the data. BH authored the first draft. LLH and JF examined and revised the paper. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe wish to show our gratitude to all those who were involved in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBergmark BA, Mathenge N, Merlini PA, Lawrence-Wright MB, Giugliano RP. Acute coronary syndromes. \u003cem\u003eLancet\u003c/em\u003e. (2022) 399:1347-58. doi: 10.1016/S0140-6736(21)02391-6\u003c/li\u003e\n\u003cli\u003eReed GW, Rossi JE, Cannon CP. Acute myocardial infarction. \u003cem\u003eLancet\u003c/em\u003e. (2017) 389:197-210. doi: 10.1016/S0140-6736(16)30677-8\u003c/li\u003e\n\u003cli\u003eSmilowitz NR, Mahajan AM, Roe MT, Hellkamp AS, Chiswell K, Gulati M, et al. 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(2009) 53:S14-20. doi: 10.1016/j.jacc.2008.10.038\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"acute myocardial infarction, hemoglobin glycation index, MIMIC-Ⅳ, All-cause mortality","lastPublishedDoi":"10.21203/rs.3.rs-4143857/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4143857/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe relationship between HGI and short-term mortality risk in patients with a first diagnosis of acute myocardial infarction (AMI) remains unclear. This study sought to understand better the relationship between HGI and mortality risk in patients with a first diagnosis of AMI.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a cohort study using data from 1961 patients with a first diagnosis of AMI from the MIMIC-IV (version 2.2) database. Patients were divided into four groups based on HGI quartiles. The Cox proportional hazards model and a two-segmented Cox proportional hazards model were used to elucidate the nonlinear relationship between HGI in patients with a first diagnosis of AMI and mortality were used to elucidate the nonlinear relationship between HGI in patients with a first diagnosis of AMI and mortality.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOf the surveyed population, 175 patients (8.92%) died within 90 days, and 210 patients (10.71%) died within 180 days. A low HGI was significantly associated with 90-day mortality [HR, 1.96; 95% CI, (1.26, 3.05); P\u0026thinsp;\u0026lt;\u0026thinsp;0.001] and 180-day mortality [HR, 1.62; 95% CI, (1.10, 2.38); P\u0026thinsp;\u0026lt;\u0026thinsp;0.001] in patients with a first diagnosis of AMI in the completely adjusted Cox proportional risk model, showing a non-linear correlation with an inflection point at 0.16 and 0.44. In the subgroup analysis, patients with pre-diabetes mellitus (pre-DM) and lower HGI levels had increased 90-day (HR 8.30; 95% CI 2.91, 23.68) and 180-day mortality risks (HR 6.84; 95% CI 2.86, 16.34).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThere is a significant correlation between HGI and all-cause mortality in patients diagnosed with AMI, especially those with lower HGI. HGI can serve as a potential indicator for evaluating the 90 and 180-day death risk of such patients.\u003c/p\u003e","manuscriptTitle":"Association between different hemoglobin glycation index and poor prognosis in patients with a first diagnosis of acute myocardial infarction-a study based on the MIMIC-IV database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-22 13:29:40","doi":"10.21203/rs.3.rs-4143857/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"697a144f-e727-46bd-8854-271994d14e6b","owner":[],"postedDate":"March 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":29723848,"name":"Endocrinology \u0026 Metabolism"}],"tags":[],"updatedAt":"2024-05-02T16:55:44+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-22 13:29:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4143857","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4143857","identity":"rs-4143857","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}