Association between the triglyceride glucose body mass index and long-term mortality in ICU patients: a cohort study of over 3000 patients
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Background: The Triglyceride Glucose (TyG) index has recently been considered an accurate surrogate biomarker for assessing insulin resistance (IR). The TyG-BMI index, integrating the Body Mass Index (BMI), has been recognized by numerous studies as a superior representation of IR status. This research aimed to investigate the relationship between the TyG-BMI index and long-term mortality risk in critically ill patients. Methods Patient data for this study were sourced from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, from which the TyG-BMI indexes were extracted. The primary endpoint was all-cause mortality within one year. Kaplan-Meier survival analysis was utilized to compare the primary endpoint across quartiles. Restricted cubic splines and Cox proportional hazards analyses were employed to explore the association between the TyG-BMI index and the endpoint. Results A total of 3,216 patients admitted to the ICU were included in the study. Kaplan-Meier analysis revealed that patients with higher TyG-BMI index values had a significantly reduced risk of death (log-rank P < 0.001). Additionally, restricted cubic spline analysis indicated a U-shaped relationship between the TyG-BMI index and long-term mortality. Furthermore, multivariable Cox proportional hazard analysis showed that the highest quartile of the TyG-BMI index, compared to the lowest quartile, had a hazard ratio (HR) of 0.66(95% CI: 0.46, 0.88; P < 0.001) for one-year mortality, suggesting a protective effect. Conclusions Among critically ill patients, the highest quartile of the TyG-BMI index was associated with a lower rate of long-term mortality. The TyG-BMI index also demonstrated a U-shaped relationship with long-term mortality, suggesting the existence of an optimal TyG-BMI range that may confer protective effects within a certain interval for critically ill patients.
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Association between the triglyceride glucose body mass index and long-term mortality in ICU patients: a cohort study of over 3000 patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association between the triglyceride glucose body mass index and long-term mortality in ICU patients: a cohort study of over 3000 patients Yuqing Fu, Cong Xu, Yanan Tang, Yuewei Li, Guifu Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3839347/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 Triglyceride Glucose (TyG) index has recently been considered an accurate surrogate biomarker for assessing insulin resistance (IR). The TyG-BMI index, integrating the Body Mass Index (BMI), has been recognized by numerous studies as a superior representation of IR status. This research aimed to investigate the relationship between the TyG-BMI index and long-term mortality risk in critically ill patients. Methods Patient data for this study were sourced from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, from which the TyG-BMI indexes were extracted. The primary endpoint was all-cause mortality within one year. Kaplan-Meier survival analysis was utilized to compare the primary endpoint across quartiles. Restricted cubic splines and Cox proportional hazards analyses were employed to explore the association between the TyG-BMI index and the endpoint. Results A total of 3,216 patients admitted to the ICU were included in the study. Kaplan-Meier analysis revealed that patients with higher TyG-BMI index values had a significantly reduced risk of death (log-rank P < 0.001). Additionally, restricted cubic spline analysis indicated a U-shaped relationship between the TyG-BMI index and long-term mortality. Furthermore, multivariable Cox proportional hazard analysis showed that the highest quartile of the TyG-BMI index, compared to the lowest quartile, had a hazard ratio (HR) of 0.66(95% CI: 0.46, 0.88; P < 0.001) for one-year mortality, suggesting a protective effect. Conclusions Among critically ill patients, the highest quartile of the TyG-BMI index was associated with a lower rate of long-term mortality. The TyG-BMI index also demonstrated a U-shaped relationship with long-term mortality, suggesting the existence of an optimal TyG-BMI range that may confer protective effects within a certain interval for critically ill patients. Health sciences/Diseases/Endocrine system and metabolic diseases Health sciences/Diseases/Metabolic disorders Health sciences/Diseases/Nutrition disorders Insulin resistance Prognosis Triglyceride Glucose-Body Mass Index (TyG-BMI) Critical care Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Despite significant advancements in the treatment of patients in intensive care units (ICU) over the last decade, a substantial number of these patients continue to succumb to their illnesses. The high mortality rate and associated hospital costs in ICUs have become pressing public health concerns worldwide[ 1 ][ 2 ]. In recent years, numerous indicators have been developed to assess the severity and in-hospital mortality of ICU patients[ 3 ], yet studies evaluating the long-term mortality post-discharge remain scarce. Insulin resistance (IR) is typified by reduced insulin sensitivity in peripheral tissues and is linked to various pathological conditions[ 4 ]. Critically ill patients, often in a state of heightened immune response, are predisposed to develop IR. Meanwhile, the Triglyceride Glucose (TyG) index, calculated as ln[TG (mg/dL) × FBG (mg/dL)/2], has risen in prominence for evaluating IR, with many studies investigating its prognostic relationship with diseases when combined with body weight[ 5 ][ 6 ][ 7 ]. This combined measure, known as the TyG-BMI index, aims to serve as a direct and potential marker for IR. By factoring in BMI, the TyG-BMI index may more accurately reflect the impact of obesity on IR[ 8 ], potentially identifying IR more effectively than other surrogate markers, given the established role of obesity as a risk factor for IR. In summary, the TyG-BMI index could be a superior marker of insulin resistance. ICU patients often exhibit increased IR due to an elevated inflammatory state[ 9 ], thus underscoring the need to investigate the prognostic value of TyG-BMI in these individuals. However, research into the long-term mortality associated with the TyG-BMI index in ICU survivors is yet to be conducted. Methods Study Population The present study utilized the Medical Information Mart for Intensive Care (MIMIC-IV) electronic database (version 2.2), developed through a collaboration between the Massachusetts Institute of Technology (MIT) and Beth Israel Deaconess Medical Center (BIDMC)[ 10 ]. This database contains detailed information on patients who received inpatient treatment at BIDMC from 2008 to 2019. The author Yuqing Fu fulfilled all the necessary requirements to access the database More information on the database is available at https://lcp.mit.edu/mimic . Inclusion criteria were all admitted ICU patients, while exclusion criteria included those with ICU stays shorter than 24 hours or lacking admission day blood glucose, triglycerides, or BMI data. Only the first admission was considered for patients with multiple admissions. The flowchart for patient screening is presented in Fig. 1 Patient Characteristics Relevant medical data were extracted from MIMIC-IV using Structured Query Language (SQL). The extracted information included demographics, comorbidities (based on ICD-9 or ICD-10 codes), laboratory indicators, and disease severity scores. The TyG-BMI index was calculated using the formula: ln[TG (mg/dL) × FBG (mg/dL)/2] × BMI. Laboratory values were taken from the first 24 hours after admission, using average values in case of multiple readings. Variables missing more than 20% of data were excluded, while those with less than 20% missing data were imputed using the missForest package in R software. Outcomes The primary endpoint was 365-day all-cause mortality. The secondary endpoint is the 30-day mortality rate. Statistical Analysis Data were analyzed using t-tests or ANOVA for continuous variables and chi-square tests or Fisher’s exact test for categorical variables. Kaplan-Meier analysis, along with log-rank tests, was used to compare mortality rates across TyG-BMI quartiles. Cox regression models were used to analyze the association between TyG-BMI and mortality, with various adjustments for confounding factors. Restricted cubic spline analysis was used to explore the dose-response relationship between TyG-BMI and mortality risk. Subgroup analyses were conducted to assess the index’s prognostic value across different patient categories. All analyses were performed using R (version 4.2.2) and SPSS (Version 26.0), with a P-value of < 0.05 considered significant. Institutional Review Board Statement The study was approved by the IRBs of both MIT and BIDMC, with informed consent obtained for the original data collection. All procedures adhered to the ethical standards of the institutional and national research committee and the 1964 Helsinki declaration and its later amendments. Results Baseline Characteristics Table 1 lists the baseline characteristics for ICU patients. Older age groups exhibited lower TyG-BMI levels, while higher levels were associated with increased white blood cell count, neutrophils, uric acid, lipids, and creatine kinase. Mortality rates at 30 days were not significantly different, but there was a gradual decrease in 365-day mortality across TyG-BMI quartiles (p = 0.001). Table 1 Baseline characteristics of included participants stratified by quartiles of TyG-BMI index. Categories group1(n = 804) group 2(n = 804) group3(n = 804) group4(n = 804) Overall (N = 3216) p Demographic Female, n(%) 457 (56.84%) 490 (60.95%) 532 (66.17%) 469 (58.33%) 1948 (60.57%) < 0.001 Weight,kg 62.02 (1,107) 76.7 (46.4,110.8) 90.58 (55.4,135.65) 112.35 (63,345) 82.96 (1,345) < 0.001 Height,cm 168 (122,203) 170 (137,196) 173 (132,196) 170 (127,198) 170 (122,203) < 0.001 BMI,kg/m2 21.98 (0.42,33.77) 26.61 (19.31,35.38) 30.73 (22.58,41.09) 38.54 (27.34,121.04) 28.5 (0.42,121.04) < 0.001 Age, years 66 (18,94) 66 (19,93) 64 (20,95) 60 (20,91) 64 (18,95) < 0.001 Ethnicity,n(%) Asian,n(%) 15 (1.87%) 12 (1.49%) 3 (0.37%) 1 (0.12%) 31 (0.96%) Black,n(%) 47 (5.85%) 46 (5.72%) 60 (7.46%) 62 (7.71%) 215 (6.69%) Other,n(%) 280 (34.83%) 278 (34.58%) 296 (36.82%) 282 (35.07%) 1136 (35.32%) White,n(%) 462 (57.46%) 468 (58.21%) 445 (55.35%) 459 (57.09%) 1834 (57.03%) ICU admission SOFA score 5 (3,8) 5 (3,9) 6 (3,10) 7 (4,11) 6 (3,9) < .001 OASIS score 35 (29,41) 35 (29,40) 36 (30,42) 37 (31,43) 36 (30,42) < .001 Vital signs HR, bmp 88.5 (75,104) 90 (75,107) 90 (78,107) 93.5 (80,110) 90 (77,107) < 0.001 NBPd, mmHg 69 (58,83) 69 (58,83) 70 (58,83) 68 (57,82) 69 (58,83) 0.198 NBPs, mmHg 123 (105,142) 123 (106.75,141) 124 (108,141) 122 (104,140) 123 (106,141) 0.311 SpO2, % 99 (96,100) 98 (95,100) 98 (95,100) 97 (94,99) 98 (95,100) < 0.001 Temperature,, 36.78 (36.44,37.17) 36.89 (36.5,37.22) 36.83 (36.5,37.28) 36.94 (36.61,37.39) 36.83 (36.5,37.28) < 0.001 Laboratory tests WBC,K/µL, 10.9 (0.3,302.5) 11.5 (0.1,235) 12.4 (0.1,96.5) 12.9 (0.1,243.6) 11.9 (0.1,302.5) < 0.001 RBC,m/µL, 3.61 (1.15,6.15) 3.7 (1.16,6.13) 3.79 (1.4,5.93) 3.9 (1.53,6.82) 3.74 (1.15,6.82) < 0.001 Neutrophil count,K/µL, 8.55 (0,59.98) 9.43 (0,52.23) 10.31 (0,67.32) 10.56 (0.54,47.97) 9.81 (0,67.32) 0.001 Lymphocytes,K/µL, 1.01 (0.01,6.32) 1.03 (0,222.85) 1.16 (0,85.09) 1.08 (0,222.08) 1.06 (0,222.85) 0.014 Platelet,K/µL 193 (6,1418) 184.5 (7,701) 191 (10,1050) 198 (6,693) 192 (6,1418) 0.002 Hemoglobin,g/dL 10.9 (4.1,18.1) 11.1 (4.1,19.4) 11.25 (4.5,18.3) 11.4 (5.4,18.4) 11.2 (4.1,19.4) 0.003 Rdw,fL 14.3 (11.4,28) 14.3 (11.5,27.1) 14.3 (11.1,28.5) 14.7 (11.6,25.3) 14.4 (11.1,28.5) < 0.001 Hematocrit 33 (12.7,54.3) 33.4 (14.1,58.8) 33.9 (14.4,54.3) 34.95 (16.9,56.5) 33.8 (12.7,58.8) < 0.001 Albumin,g/L 3 (1.1,4.8) 3 (0.8,5.5) 2.9 (1.2,5.3) 2.9 (1,4.9) 2.9 (0.8,5.5) 0.4 Sodium,mEq/L 139 (103,165) 139 (108,155) 139 (106,165) 138 (115,169) 139 (103,169) 0.016 Potassium,mEq/L 4 (1.9,9.2) 4.1 (1.8,8.7) 4.1 (2.4,8.6) 4.2 (2.3,9) 4.1 (1.8,9.2) < 0.001 Calciumtotal,mg/dl 8.2 (5.1,14.8) 8.3 (2.5,14.2) 8.2 (3.7,13.6) 8.2 (3.4,15.3) 8.3 (2.5,15.3) 0.091 Chloride,mg/dl 104 (64,135) 104 (77,131) 105 (70,125) 103 (74,135) 104 (64,135) < 0.001 Glucose,mg/dL, 117 (24,737) 129 (32,754) 140 (30,1456) 157.5 (51,1016) 134 (24,1456) < 0.001 HbA1c,%, 5.6 (4.2,15.7) 5.7 (4.4,13.3) 5.9 (4.4,16.3) 6.2 (4,16.6) 5.8 (4,16.6) < 0.001 TG,mg/dL 93 (70,133.25) 122 (86,172) 153.5 (103.75,228) 205 (130.75,337.25) 133 (90,212) < 0.001 Aniongap,mmol/L 14 (6,47) 15 (2,49) 15 (3,43) 16 (5,37) 15 (2,49) < 0.001 TT,s 23.45 (13.6,150) 18.2 (14.1,150) 19.4 (11.6,150) 21.5 (11.5,150) 18.85 (11.5,150) 0.506 PT,s 13.8 (9.6,130.9) 14 (9.2,130.4) 14.2 (9.8,150) 14.2 (9.6,117.7) 14 (9.2,150) 0.029 Fibrinogen,mg/dL 294 (41,1123) 319 (36,1020) 320 (35,1167) 364 (48,1466) 323 (35,1466) 0.003 Ddimer,mg 3527 (644,21240) 3513 (591,12069) 2882 (666,7462) 4385 (290,21509) 3472.5 (290,21509) 0.802 ALT,IU/L 25 (1,9582) 31 (3,6616) 33 (5,15018) 35 (3,7392) 31 (1,15018) < 0.001 AST,IU/L 39 (0,17600) 45 (5,16074) 48 (8,15108) 49 (6,28275) 45 (0,28275) < 0.001 Ureanitrogen,mg/dl 18 (3,147) 19 (3,160) 21 (3,186) 23 (1,200) 20 (1,200) < 0.001 Creatinine,mg/dL 0.9 (0.1,19.7) 1 (0.2,15.5) 1.1 (0.3,18.2) 1.2 (0.2,33.1) 1 (0.1,33.1) < 0.001 Uricacid,mg/dl 4.3 (0.6,23.5) 5.45 (0,16.9) 5.3 (1.4,15.9) 6.4 (1.8,15.3) 5.7 (0,23.5) 0.081 LD,U/L 279 (97,20270) 327 (67,15850) 344.5 (56,20500) 348 (107,19780) 323 (56,20500) < 0.001 CK,U/L 161 (5,148600) 149 (4,209100) 228 (11,191820) 225 (9,472680) 189 (4,472680) < 0.001 CK-MB,ng/ml 5 (1,423) 6 (1,493) 5 (1,458) 5 (1,594) 5 (1,594) 0.26 cTnT, ng/m,ean(SD) 0.12 (0.01,23.9) 0.12 (0.01,24.31) 0.15 (0.01,19.13) 0.1 (0.01,24.62) 0.12 (0.01,24.62) 0.097 NT-proBNP,pg/mL 1999 (45,57512) 3289.5 (53,53475) 2744 (26,47825) 2068.5 (30,64845) 2687 (26,64845) 0.194 TyG index 8.66 (5.75,11.08) 9 (7.34,11.63) 9.31 (6.95,13.87) 9.74 (7.76,13.67) 9.13 (5.75,13.87) < 0.001 TyG-BMI index 193.76 (3.58,218.6) 240.09 (218.69,262.02) 287.72 (262.04,319.22) 377.31 (319.4,1021.87) 262.03 (3.58,1021.87) < 0.001 Comorbidities Hypertension 312 (38.81%) 325 (40.42%) 343 (42.66%) 369 (45.9%) 1349 (41.95%) 0.025 Diabetes 113 (14.05%) 162 (20.15%) 220 (27.36%) 330 (41.04%) 825 (25.65%) < 0.001 HF 217 (26.99%) 211 (26.24%) 228 (28.36%) 240 (29.85%) 896 (27.86%) 0.387 AMI 76 (9.45%) 84 (10.45%) 105 (13.06%) 76 (9.45%) 341 (10.6%) 0.061 Cancer 107 (13.31%) 109 (13.56%) 75 (9.33%) 51 (6.34%) 342 (10.63%) < 0.001 CKD 105 (13.06%) 112 (13.93%) 130 (16.17%) 135 (16.79%) 482 (14.99%) 0.112 AKI 291 (36.19%) 349 (43.41%) 411 (51.12%) 501 (62.31%) 1552 (48.26%) < 0.001 Sepsis 194 (24.13) 198 (24.63) 222 (27.61) 38.06) 920 (28.61) < 0.001 Decompensated Cirrhosis 79 (9.83%) 89 (11.07%) 80 (9.95%) 93 (11.57%) 341 (10.6%) 0.605 Hepatitis 53 (6.59%) 43 (5.35%) 59 (7.34%) 63 (7.84%) 218 (6.78%) 0.215 Tuberculosis 30 (3.73%) 25 (3.11%) 37 (4.6%) 34 (4.23%) 126 (3.92%) 0.444 Diastroke 96 (11.94%) 98 (12.19%) 75 (9.33%) 75 (9.33%) 344 (10.7%) 0.097 Hyperlipidemia 198 (24.63%) 242 (30.1%) 285 (35.45%) 243 (30.22%) 968 (30.1%) < 0.001 AF 240 (29.85%) 236 (29.35%) 249 (30.97%) 244 (30.35%) 969 (30.13%) 0.908 Medicine Antibiotic 703 (87.44%) 709 (88.18%) 726 (90.3%) 746 (92.79%) 2884 (89.68%) 0.002 Glucocorticoids 143 (17.79%) 174 (21.64%) 162 (20.15%) 169 (21.02%) 648 (20.15%) 0.233 Nephrotoxic 682 (84.83%) 712 (88.56%) 715 (88.93%) 686 (85.32%) 2795 (86.91%) 0.022 Immunosuppressant 34 (4.23%) 41 (5.1%) 39 (4.85%) 33 (4.1%) 147 (4.57%) 0.735 Hypertensive 606 (75.37%) 651 (80.97%) 686 (85.32%) 692 (86.07%) 2635 (81.93%) < 0.001 Events 30 days death,n,(%) 191 (23.76%) 169 (21.02%) 162 (20.15%) 167 (20.77%) 689 (21.42%) 0.301 365 days death,n,(%) 342(42.5%) 299(37.1%) 276 (34.33%) 270 (33.58%) 1187 (36.91%) 0.001 Primary Outcomes Kaplan-Meier curves showed significantly higher 365-day mortality in the highest TyG-BMI quartile compared to the others (p = 0.001), with no significant difference in 30-day mortality ( p = 0.085) (in Fig. 2 ) The restricted cubic spline (RCS) analysis delineated an L-shaped association between the TyG-BMI index and all-cause mortality across a span of 365 days, with the index serving as a continuous variable (in Fig. 3 ). Insights from the restricted cubic spline suggest that the relationship between the TyG-BMI index and 30-day mortality does not adhere to a nonlinear trend( p = 0.297). In contrast, a U-shaped correlation with long-term mortality over 365 days emerges, identifying a pivotal inflection at a TyG-BMI value of 355.975. Notably, to the left of this inflection, the TyG-BMI index exhibits a negative association with long-term survival; however, beyond this threshold, the index seems to exert a protective influence against long-term mortality, with statistical significance ( p < 0.01). In Table 2 , univariable Cox regression analysis was performed to examine the association between the TyG-BMI index and the mortality rate at 365 days. Variables with clinical significance disease were incorporated into the multivariable Cox proportional hazards model. The model 1 solely included the TyG-BMI index without any further adjustments(Q2 HR:0.85,95%CI:0.73–0.99, p = 0.039, Q3 HR: 0.78,95%CI:0.66–0.91, p = 0.002, Q3 HR: 0.76, 95%CI: 0.65–0.89, p = 0.001). In Model 2, adjustments were made for age, gender, and race.(Q2 HR:0.85,95%CI:0.73–0.99, p = 0.037, Q3 HR: 0.79,95%CI:0.67–0.93, p = 0.004, Q3 HR: 0.84, 95%CI: 0.72–0.99, p = 0.038)Model 3 incorporated further adjustments for laboratory tests and comorbidities such as Hypertension, Diabetes, HF(Heart Feature),DC, Hepatitis, Pneumonia, Diastroke, Spesis,AKI,WBC,creatinine.( Q2 HR:0.81,95%CI:0.68–0.96, p = 0.016, Q3 HR: 0.72,95%CI:0.57–0.87, p = 0.001, Q3 HR: 0.66, 95%CI: 0.46–0.88, p = 0.006). The reference category for all models was the lowest quartile of the TyG-BMI index. Table 2 Association between serum Klothoand all-cause mortality in chronic kidney disease. Categories Model 1 Model 2 Model 3 TyG-BMI Events(%) HR (95% CI) P-value HR (95% CI) P-value HR (95% CI) P-value Q1(N = 804) 342(42.5%) Ref Ref Ref Q2(N = 804) 299(37.1%) 0.85[0.73,0.99] 0.039 0.85[0.73,0.99] 0.037 0.81[0.68,0.96] 0.016 Q3(N = 804) 276(34.33%) 0.78[0.66,0.91] 0.002 0.79[0.67,0.93] 0.004 0.72[0.57,0.87] 0.001 Q4(N = 804) 270(33.58%) 0.76[0.65,0.89] 0.001 0.84[0.72,0.99] 0.038 0.66[0.46,0.88] 0.006 Data were presentedas hazard ratios (95% confidence intervals), P-value. Model 1did not adjust for any covariate. Model 2 was adjusted for age (continuous), sex (male or female), and race/ethnicity. Model 3was adjusted for model2 and additional covariates, including hypertension (yes or no), age(༞65or ≤ 65), HF(yes or no), CKD(yes or no), diabetes(yes or no), AKI(yes or no), DC(yes or no), pneumonia(yes or no), diastroke(yes or no), sepsis(yes or no), WBC(continuous), Creatinine(continuous). In Table 3 and Fig. 4 , subgroup analysis revealed that the TyG-BMI index demonstrated a more pronounced protective effect in older individuals (over 65 years of age),and patients with hypertension, and there were observed interactions with gender, age, and the presence of diabetes. Table 3 Subgroup analysis subgroups Case Total Q1 Q2 Q3 Q4 P for Interaction HR(95%CI) P value HR(95%CI) P value HR(95%CI) P value Age,years > 65 476 1736 ref 0.741(0.562,0.976) 0.033 0.910(0.700,1.181) 0.477 0.761(0.588,0.986) 0.039 0.01 ≤ 65 710 1480 ref 0.857,(0.708,1.037) 0.112 0.628(0.509,0.775) ＜0.001 0.603(0.477,0.763) ＜0.001 Gender female 682 1948 ref 0.724(0.586,0.893) 0.003 0.650(0.526,0.803) ＜0.001 0.694(0.554,0.871) 0.02 0.08 male 504 1268 ref 0.937(0.739,1.187) 0.589 0.835(0.648,1.076) 0.164 0.620(0.477,0.807) 0.001 Diabetes YES 332 825 ref 0.854(0.605,1.205) 0.369 0.695(0.500,0.967 0.031 0.467(0.335,0.650) 0.0001 0.016 NO 854 2391 ref 0.794(0.665,0.949) 0.011 0.719(0.595,0.868) 0.001 0.808(0.662,0.986) 0.036 hypertension YES 485 1349 ref 0.741(0.58,0.947) 0.016 0.563(0.435,0.730) 0.000 0.587(0.4490.768) 0.000 0.069 NO 701 1867 ref 0.875(0.713,1.075) 0.205 0.851(0.691,1.049) 0.131 0.741(0.593,0.926) 0.008 HF YES 388 896 ref 0.761(0.578,1.004) 0.053 0.676(0.512,0.894) 0.006 0.540(0.402,0.727) 0.000 0.297 NO 798 2320 ref 0.852(0.704,1.032) 101 0.765(0.627.935) 0.009 0.756(0.613,0.934) 0.009 AKI YES 459 1552 ref 0.930(0.7481.156) 0.513 0.849(0.684,1.053) 0.137 0.709(0.570,0.884) 0.02 0.132 NO 727 1664 ref 0.689(0.546,0.869) 0.002 0.571(0.4410.741) 0.001 0.667(0.499,0.890) 0.006 DC YES 179 2341 ref 0.984(0.633,1.528) .942 1.052(0.664,1.666) 0.830 0.963(0.612,1.515) 0.871 0.106 NO 1007 2875 ref 0.789(0.666,0.934) .006 0.681(0.572,0.811) 0.0001 0.626(0.520,0.755) 0.000 Pneumonia YES 611 1345 ref 0.755(0.602,0.946) 0.014 0.688(0.545,0.869) 0.002 0.6680.5250.851 0.001 0.829 NO 575 1871 ref 0.856(0.685,1.069) 0.170 0.728(0.578,0.917) 0.007 0.661(0.518,0.844) 0.001 diastroke YES 138 344 ref 0.963(0.621,1.495) 0.868 0.588(0.356,0.974) 0.049 0.579(0.343,0.978) 0.051 0.266 NO 1048 2872 ref 0.792(0.669,0.938) 0.007 0.745(0.627,0.885) 0.003 0.673(0.561,0.807) 0.0001 spesis YSE 469 920 ref 0.797(0.606,1.049) .106 0.826(0.632,1.081) 0.164 0.674(0.515,0.880) 0.004 0.468 NO 717 2296 ref 0.824(0.680,0.998) 0.048 0.665(0.541,0.817) 0.0001 0.663(0.529, 0.831) 0.001 Conclusion Our investigation represents the inaugural exploration into the correlation between the TyG-BMI index and the long-term survival rates of patients in Intensive Care Units (ICUs), encompassing a cohort of over three thousand individuals to bolster the robustness of our findings. Within the context of this study, we discerned a substantial association between an elevated TyG-BMI index and a decrement in in-hospital mortality among critically ill patients, positioning the TyG-BMI index as a protective factor in this demographic. In models adjusted for a multitude of confounding variables, this protective correlation was further amplified. Moreover, when analyzed as a continuous variable, the TyG-BMI index exhibited a U-shaped relationship with long-term mortality rates. In reality, the Triglyceride-Glucose (TyG) index has emerged in recent years as a putative marker of insulin resistance(IR). Given its relatively recent inception, research into the TyG index, while burgeoning, still lacks substantial empirical evidence to corroborate its association with disease pathogenesis.[ 11 ][ 12 ][ 13 ] Contemporary research posits that the IR acts as a risk factor for a variety of diseases, including cardiovascular diseases[ 14 ], cerebrovascular events[ 15 ][ 16 ], the onset of hypertension[ 17 ], and poor prognosis in oncological outcomes. However, in critically ill populations, the applicability of IR markers in predicting adverse events remains a contentious issue. For instance, researchers have identified the Triglyceride-Glucose (TyG) index as a protective factor in the prognosis of diabetic foot outcomes.[ 18 ] Concurrently, scholarly articles have reported that the Triglyceride-Glucose-Body Mass Index (TyG-BMI) demonstrates a protective role in patients with chronic kidney disease[ 19 ] or heart failure[ 20 ]. Therefore, based on our research considerations, the TyG-BMI index, which reflects nutritional status as well as the level of insulin resistance, may have a complex relationship with patient survival. The findings presented in this article echo this perspective, demonstrating a protective effect of the TyG-BMI index on ICU patients when incorporated as a quartile into Cox regression models. When analyzed as a continuous variable using restricted cubic spline analysis, the emergence of a U-shaped relationship suggests the existence of an optimal TyG-BMI range. Exceeding this range, the long-term survival rate of ICU patients begins to decline; however, this downward trend is considerably less pronounced than in intervals with lower TyG-BMI index values. Simultaneously, our subgroup analysis revealed that the protective effect of the TyG-BMI index is more pronounced in populations with comorbidities such as diabetes, hypertension, or heart failure. This protective association was not observed in groups with comorbidities of other systems. This finding, to some extent, underscores the unique relationship of the TyG-BMI index with the cardiovascular system and highlights its protective role in critically ill cohorts. We speculate that this may be related to the elevated levels of inflammation present in patients with critical illness. Insulin resistance, to a certain extent, reflects the body’s level of inflammation, and an appropriate inflammatory response is crucial for critically ill patients, especially those with concurrent cardiovascular conditions. Additionally, the nutritional status can significantly impact the survival outcomes of critically ill patients. Within the ICU setting, sustained insulin resistance may exacerbate systemic inflammatory responses[ 24 ][ 25 ][ 26 ][ 27 ]. From a physiological standpoint, a certain degree of insulin resistance and inflammation might serve as an adaptive mechanism to acute trauma or infectious challenges. Optimal insulin resistance levels allow glucose to be redirected into intracellular stores, acting as a crucial mechanism to preserve energy reserves during periods of intense physiological stress[ 28 ][ 29 ]. Moreover, a subset of ICU patients may demonstrate a distinct survival advantage, potentially attributable to a more robust metabolic response. Elevated TyG-BMI indices in these patients may indicate enhanced nutritional reserves and metabolic vigor, potentially endowing them with an increased capacity to endure the hardships of prolonged disease and the demands of ongoing medical interventions[ 31 ][ 32 ]. From this perspective, an increased TyG-BMI may not signify a dire prognosis but rather suggest the potential for greater physiological resilience and recovery. Certainly, given the retrospective cohort of our study, there are numerous limitations inherent to its design. It is possible that the utility of the TyG-BMI index differs in a general, healthy population, a hypothesis that necessitates further investigation for validation. Abbreviations TyG-BMI Triglyceride-Glucose-Body Mass Index BMI Body mass index DC Decompensated Cirrhosis HF Heart Failure AKI Acute Kidney Injury Declarations Acknowledgements We greatly acknowledge the diligent efforts of the personnel responsible for the design and maintenance of the MIMIV database. Authors’ contributions FYQ, XC, WGF were responsible for the study concept and study design. Data extraction was undertaken by FYQ, WGF and TYN were responsible for data analysis and drafting of the manuscript. Critical revision of the manuscript for important intellectual content: LYW. All authors read and approved the final manuscript. Funding This work did not receive any funding. Data Availability The first author can be contacted to receive the datasets generated and utilized in this work upon reasonable request and with MIMIC’s permission. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Lee J, Cho YJ, Kim SJ, Yoon HI, Park JS, Lee CT, Lee JH, Lee YJ. Who Dies after ICU Discharge? Retrospective Analysis of Prognostic Factors for In-Hospital Mortality of ICU Survivors. J Korean Med Sci. 2017 Mar;32(3):528-533. doi: 10.3346/jkms.2017.32.3.528. PMID: 28145659; PMCID: PMC5290115 Bai J, Fügener A, Gönsch J, Brunner JO, Blobner M. Managing admission and discharge processes in intensive care units. Health Care Manag Sci. 2021 Dec;24(4):666-685. doi: 10.1007/s10729-021-09560-6. Epub 2021 Jun 10. PMID: 34110549; PMCID: PMC8189840. Johnson AE, Kramer AA, Clifford GD. A new severity of illness scale using a subset of acute physiology and chronic health evaluation data elements shows comparable predictive accuracy. Crit Care Med. 2013;41(7):1711–8. Muniyappa R, Madan R, Varghese RT. Assessing insulin sensitivity and resistance in humans. In: Feingold KR, Anawalt B, Boyce A, Chrousos G, de Herder WW, Dhatariya K, Dungan K, editors. Endotext. South Dartmouth (MA: MDText.com; Inc. Copyright © 2000-2021; MDText.com; Inc.; 2000. eng Chen N, Xu Y, Xu C, Duan J, Zhou Y, Jin M, Xia H, Yuan W, Chen R. Effects of triglyceride glucose (TyG) and TyG-body mass index on sex-based differences in the early-onset heart failure of ST-elevation myocardial infarction. Nutr Metab Cardiovasc Dis. 2023 Oct 4:S0939-4753(23)00388-5. doi: 10.1016/j.numecd.2023.09.027. Epub ahead of print. PMID: 37996372 Dou J, Guo C, Wang Y, Peng Z, Wu R, Li Q, Zhao H, Song S, Sun X, Wei J. Association between triglyceride glucose-body mass and one-year all-cause mortality of patients with heart failure: a retrospective study utilizing the MIMIC-IV database. Cardiovasc Diabetol. 2023 Nov 8;22(1):309. doi: 10.1186/s12933-023-02047-4. PMID: 37940979; PMCID: PMC10634170. Zhan C, Peng Y, Ye H, Diao X, Yi C, Guo Q, Chen W, Yang X. Triglyceride glucose-body mass index and cardiovascular mortality in patients undergoing peritoneal dialysis: a retrospective cohort study. Lipids Health Dis. 2023 Sep 5;22(1):143. doi: 10.1186/s12944-023-01892-2. PMID: 37670344; PMCID: PMC10478298. Er LK, Wu S, Chou HH, et al. Triglyceride glucose-body mass index is a simple and clinically useful surrogate marker for insulin resistance in nondiabetic individuals. PLoS ONE. 2016;11(3): e0149731. Jin A, Wang S, Li J, et al. Mediation of systemic inflammation on insulin resistance and prognosis of nondiabetic patients with ischemic stroke. Stroke. 2023;54(3):759–69. Johnson A, Bulgarelli L, Shen L, Gayles A, Shammout A, Horng S, Pollard TJ, Hao S, Moody B, Gow B, et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci Data. 2023;10(1):1 Xu YX, Pu SD, Zhang YT, Tong XW, Sun XT, Shan YY, Gao XY. Insulin resistance is associated with the presence and severity of retinopathy in patients with type 2 diabetes. Clin Exp Ophthalmol. 2023 Dec 22. doi: 10.1111/ceo.14344. Epub ahead of print. Tian WB, Zhang WS, Jiang CQ, Jin YL, Lam TH, Cheng KK, Xu L. Association of insulin resistance and glycemic measures with major abnormal electrocardiogram in older Chinese: Cross-sectional analysis based on the Guangzhou Biobank Cohort study. Diabetes Res Clin Pract. 2023 Dec 7;207:111046. doi: 10.1016/j.diabres.2023.111046. Raimi TH, Dele-Ojo BF, Dada SA, et al. Triglyceride-Glucose Index and Related Parameters Predicted Metabolic Syndrome in Nigerians. Metab Syndr Relat Disord. 2021;19(2):76-82. doi:10.1089/met.2020.0092 Zhang Y, Zhang C, Jiang L, Xu L, Tian J, Zhao X, Wang D, Zhang Y, Sun K, Zhang C, Xu B, Zhao W, Hui R, Gao R, Wang J, Feng X, Yuan J, Song L. An elevated triglyceride-glucose index predicts adverse outcomes and interacts with the treatment strategy in patients with three-vessel disease. Cardiovasc Diabetol. 2023 Dec 6;22(1):333. doi: 10.1186/s12933-023-02063-4. Huang X, Cheng H, Yuan S, Ling Y, Tan S, Tang Y, Niu C, Lyu J. Triglyceride-glucose index as a valuable predictor for aged 65-years and above in critical delirium patients: evidence from a multi-center study. BMC Geriatr. 2023 Oct 30;23(1):701. doi: 10.1186/s12877-023-04420-0. Tian N, Song L, Hou T, Fa W, Dong Y, Liu R, Ren Y, Liu C, Zhu M, Zhang H, Wang Y, Cong L, Du Y, Qiu C. Association of Triglyceride-Glucose Index With Cognitive Function and Brain Atrophy: A Population-Based Study. Am J Geriatr Psychiatry. 2023 Sep 16:S1064-7481(23)00423-2. doi: 10.1016/j.jagp.2023.09.007. Lim J, Kim J, Koo SH, Kwon GC. Comparison of triglyceride glucose index, and related parameters to predict insulin resistance in Korean adults: An analysis of the 2007-2010 Korean National Health and Nutrition Examination Survey. PLoS One. 2019 Mar 7;14(3):e0212963. doi: 10.1371/journal.pone.0212963. Li Z, Zhang M, Han L, Fu L, Wu Y, Chen H, Feng L. Counterintuitive relationship between the triglyceride glucose index and diabetic foot in diabetes patients: A cross-sectional study. PLoS One. 2023 Nov 3;18(11):e0293872. doi: 10.1371/journal.pone.0293872. Argoty-Pantoja AD, Velázquez-Cruz R, Meneses-León J, Salmerón J, Rivera-Paredez B. Triglyceride-glucose index is associated with hypertension incidence up to 13 years of follow-up in mexican adults. Lipids Health Dis. 2023 Sep 27;22(1):162. doi: 10.1186/s12944-023-01925-w. Shen FC, Lin HY, Tsai WC, Kuo IC, Chen YK, Chao YL, Niu SW, Hung CC, Chang JM. Non-insulin-based insulin resistance indices for predicting all-cause mortality and renal outcomes in patients with stage 1-4 chronic kidney disease: another paradox. Front Nutr. 2023 May 15;10:1136284. doi: 10.3389/fnut.2023.1136284. Dou J, Guo C, Wang Y, Peng Z, Wu R, Li Q, Zhao H, Song S, Sun X, Wei J. Association between triglyceride glucose-body mass and one-year all-cause mortality of patients with heart failure: a retrospective study utilizing the MIMIC-IV database. Cardiovasc Diabetol. 2023 Nov 8;22(1):309. doi: 10.1186/s12933-023-02047-4. PMID: 37940979; PMCID: PMC10634170. Matulewicz N, Karczewska-Kupczewska M. Insulin resistance and chronic inflammation. Postepy Hig Med Dosw (Online).2016;70(0):1245-1258. Published 2016 Dec 20. Pilika K, Roshi E. Insulin resistance in early vs late nutrition and complications of sirs in neurosurgical intensive care unit (ICU). Med Arch. 2015;69(1):46-48. doi:10.5455/medarh.2015.69.46-48 Cuesta JM, Singer M. The stress response and critical illness: a review. Crit. care Med. 2012;40:3283–3289. doi: 10.1097/CCM.0b013e31826567eb. Van den Berghe G. Insulin therapy for the critically ill patient. Clin Cornerstone. 2003;5(2):56-63. doi:10.1016/s1098-3597(03)90018-4 Mizock BA. Alterations in fuel metabolism in critical illness: hyperglycaemia. Best Pract Res Clin Endocrinol Metab. 2001;15(4):533-551. doi:10.1053/beem.2001.0168 Treskes N, Koekkoek WAC, van Zanten ARH. The Effect of Nutrition on Early Stress-Induced Hyperglycemia, Serum Insulin Levels, and Exogenous Insulin Administration in Critically Ill Patients With Septic Shock: A Prospective Observational Study. Shock. 2019;52(4):e31-e38. doi:10.1097/SHK.0000000000001287 Singer P. Preserving the quality of life: nutrition in the ICU. Crit Care. 2019;23(Suppl 1):139. Published 2019 Jun 14. doi:10.1186/s13054-019-2415-8 Rusavy Z, Sramek V, Lacigova S, Novak I, Tesinsky P, Macdonald IA. Influence of insulin on glucose metabolism and energy expenditure in septic patients. Crit Care. 2004;8(4):R213-R220. doi:10.1186/cc2868 Matulewicz N, Karczewska-Kupczewska M. Insulin resistance and chronic inflammation. Postepy Hig Med Dosw (Online). 2016;70(0):1245-1258. Published 2016 Dec 20. doi: 10.5604/01.3001.0010.5809. Bear DE, Wandrag L, Merriweather JL, et al. The role of nutritional support in the physical and functional recovery of critically ill patients: a narrative review. Crit Care. 2017;21(1):226. doi: 10.1186/s13054-017-1810-2 Mira JC, Brakenridge SC, Moldawer LL, Moore FA. Persistent inflammation, immunosuppression and catabolism syndrome. Crit Care Clin. 2017;33(2):245–258. doi: 10.1016/j.ccc.2016.12.001. Khan SH, Sobia F, Niazi NK, Manzoor SM, Fazal N, Ahmad F. Metabolic clustering of risk factors: evaluation of Triglyceride-glucose index (TyG index) for evaluation of insulin resistance. Diabetol Metab Syndr. 2018;10:74. doi: 10.1186/s13098-018-0376-8. eCollection 2018. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3839347","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":266091942,"identity":"11ca1ce5-c5f2-4789-aac1-e11772e77979","order_by":0,"name":"Yuqing Fu","email":"","orcid":"","institution":"Department of Cardiology, The Eighth Affiliated Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Yuqing","middleName":"","lastName":"Fu","suffix":""},{"id":266091943,"identity":"96a1a518-1ded-4425-82d5-0edb1070f665","order_by":1,"name":"Cong Xu","email":"","orcid":"","institution":"Department of Cardiology, The Eighth Affiliated Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Cong","middleName":"","lastName":"Xu","suffix":""},{"id":266091944,"identity":"17c623cb-ce2e-490d-8d66-c0d70bd4148a","order_by":2,"name":"Yanan Tang","email":"","orcid":"","institution":"Department of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Yanan","middleName":"","lastName":"Tang","suffix":""},{"id":266091947,"identity":"5cb12668-f5e5-4156-8f5f-14d867042143","order_by":3,"name":"Yuewei Li","email":"","orcid":"","institution":"Department of Respiratory and Intensive Care, Sun Yat-sen Memorial Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Yuewei","middleName":"","lastName":"Li","suffix":""},{"id":266091949,"identity":"96fea1b0-a487-4672-a1d6-9e560a73be51","order_by":4,"name":"Guifu Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYLCCBBsGOQYGxsYDMAEJwlrSGIyBWhpI0MKQxpDYAKSI02JwI/2axIOEw+lr2w8Dbflz2N7gAPPB2zwMdnm4teSUSSQkHM7ddiax4QBj2+HEDQfYkq15GJKLcWkxu5GTJpH4A6jlAEhLw+EEgwM8ZtI8DAfATsWpBWhLutn5hzCH8X8joCX9GEhLgtkNoC0MbIcZNxzgYcOrxf7MG2aLhIR0w203gLYktqUnzjzMZmw5xyAZpxbJ9vSHN38kWMubnU9/+ODDH2t7vuPND2+8qbDDqYWBgccAwU5gaGZgYAaxDHApBwH2B8i8OnxKR8EoGAWjYIQCAJddYojY2mvsAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Cardiology, The Eighth Affiliated Hospital, Sun Yat-sen University","correspondingAuthor":true,"prefix":"","firstName":"Guifu","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2024-01-06 09:14:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3839347/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3839347/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49440926,"identity":"e782ea77-48c0-486b-81b1-598f544375e6","added_by":"auto","created_at":"2024-01-10 22:02:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":138186,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of patient selection\u003c/p\u003e","description":"","filename":"fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-3839347/v1/0f97d50784e60460682431f7.png"},{"id":49440415,"identity":"1b47811c-6ab9-438b-ac7e-2aed65da2522","added_by":"auto","created_at":"2024-01-10 21:54:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1046088,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival analysis curves for all-cause mortality\u003c/p\u003e\n\u003cp\u003eTyG-BMI index: Q1 (3.58–218.59), Q2 (218.68–262.02), Q3 (262.03–319.21), Q4 (319.39–1021.87). Kaplan–Meier curves showing the cumulative probability of all-cause mortality according to groups at 365-day(A) and 30-day (B)\u003c/p\u003e","description":"","filename":"fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-3839347/v1/8a8c46b196969364c538ee1d.png"},{"id":49440418,"identity":"46be876e-fdfc-4752-b2ae-44c920276fe2","added_by":"auto","created_at":"2024-01-10 21:54:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":561368,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline regression analysis of TyG-BMI index with in all-cause mortality at 30-day(A) and 365-day (B)\u003c/p\u003e","description":"","filename":"fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-3839347/v1/e9d5d26fb744f901f85007ac.png"},{"id":49440416,"identity":"32eb6be8-a8e0-4020-b34b-1ad96f9749a8","added_by":"auto","created_at":"2024-01-10 21:54:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2470279,"visible":true,"origin":"","legend":"\u003cp\u003eforest plot of subgroup analysis.\u003c/p\u003e","description":"","filename":"fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-3839347/v1/cbe5d3c3913aa5b2e4c2a32e.png"},{"id":52003822,"identity":"a95dc7a5-919e-426b-914a-63e4d857a50e","added_by":"auto","created_at":"2024-03-05 08:32:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1460956,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3839347/v1/8820e73d-202d-4715-b3fa-9dc294f0ac22.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between the triglyceride glucose body mass index and long-term mortality in ICU patients: a cohort study of over 3000 patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDespite significant advancements in the treatment of patients in intensive care units (ICU) over the last decade, a substantial number of these patients continue to succumb to their illnesses. The high mortality rate and associated hospital costs in ICUs have become pressing public health concerns worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e][\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In recent years, numerous indicators have been developed to assess the severity and in-hospital mortality of ICU patients[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], yet studies evaluating the long-term mortality post-discharge remain scarce.\u003c/p\u003e \u003cp\u003eInsulin resistance (IR) is typified by reduced insulin sensitivity in peripheral tissues and is linked to various pathological conditions[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Critically ill patients, often in a state of heightened immune response, are predisposed to develop IR. Meanwhile, the Triglyceride Glucose (TyG) index, calculated as ln[TG (mg/dL) \u0026times; FBG (mg/dL)/2], has risen in prominence for evaluating IR, with many studies investigating its prognostic relationship with diseases when combined with body weight[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e][\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e][\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This combined measure, known as the TyG-BMI index, aims to serve as a direct and potential marker for IR. By factoring in BMI, the TyG-BMI index may more accurately reflect the impact of obesity on IR[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], potentially identifying IR more effectively than other surrogate markers, given the established role of obesity as a risk factor for IR.\u003c/p\u003e \u003cp\u003eIn summary, the TyG-BMI index could be a superior marker of insulin resistance. ICU patients often exhibit increased IR due to an elevated inflammatory state[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], thus underscoring the need to investigate the prognostic value of TyG-BMI in these individuals. However, research into the long-term mortality associated with the TyG-BMI index in ICU survivors is yet to be conducted.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eThe present study utilized the Medical Information Mart for Intensive Care (MIMIC-IV) electronic database (version 2.2), developed through a collaboration between the Massachusetts Institute of Technology (MIT) and Beth Israel Deaconess Medical Center (BIDMC)[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This database contains detailed information on patients who received inpatient treatment at BIDMC from 2008 to 2019. The author Yuqing Fu fulfilled all the necessary requirements to access the database More information on the database is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://lcp.mit.edu/mimic\u003c/span\u003e\u003cspan address=\"https://lcp.mit.edu/mimic\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Inclusion criteria were all admitted ICU patients, while exclusion criteria included those with ICU stays shorter than 24 hours or lacking admission day blood glucose, triglycerides, or BMI data. Only the first admission was considered for patients with multiple admissions. The flowchart for patient screening is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePatient Characteristics\u003c/h2\u003e \u003cp\u003eRelevant medical data were extracted from MIMIC-IV using Structured Query Language (SQL). The extracted information included demographics, comorbidities (based on ICD-9 or ICD-10 codes), laboratory indicators, and disease severity scores. The TyG-BMI index was calculated using the formula: ln[TG (mg/dL) \u0026times; FBG (mg/dL)/2] \u0026times; BMI. Laboratory values were taken from the first 24 hours after admission, using average values in case of multiple readings. Variables missing more than 20% of data were excluded, while those with less than 20% missing data were imputed using the missForest package in R software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cp\u003eThe primary endpoint was 365-day all-cause mortality. The secondary endpoint is the 30-day mortality rate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData were analyzed using t-tests or ANOVA for continuous variables and chi-square tests or Fisher\u0026rsquo;s exact test for categorical variables. Kaplan-Meier analysis, along with log-rank tests, was used to compare mortality rates across TyG-BMI quartiles. Cox regression models were used to analyze the association between TyG-BMI and mortality, with various adjustments for confounding factors. Restricted cubic spline analysis was used to explore the dose-response relationship between TyG-BMI and mortality risk. Subgroup analyses were conducted to assess the index\u0026rsquo;s prognostic value across different patient categories. All analyses were performed using R (version 4.2.2) and SPSS (Version 26.0), with a P-value of \u0026lt;\u0026thinsp;0.05 considered significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eInstitutional Review Board Statement\u003c/h2\u003e \u003cp\u003eThe study was approved by the IRBs of both MIT and BIDMC, with informed consent obtained for the original data collection. All procedures adhered to the ethical standards of the institutional and national research committee and the 1964 Helsinki declaration and its later amendments.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eBaseline Characteristics\u003c/h2\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e lists the baseline characteristics for ICU patients. Older age groups exhibited lower TyG-BMI levels, while higher levels were associated with increased white blood cell count, neutrophils, uric acid, lipids, and creatine kinase. Mortality rates at 30 days were not significantly different, but there was a gradual decrease in 365-day mortality across TyG-BMI quartiles (p\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eBaseline characteristics of included participants stratified by quartiles of TyG-BMI index.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCategories\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003egroup1(n\u0026thinsp;=\u0026thinsp;804)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003egroup 2(n\u0026thinsp;=\u0026thinsp;804)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003egroup3(n\u0026thinsp;=\u0026thinsp;804)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003egroup4(n\u0026thinsp;=\u0026thinsp;804)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOverall\u003c/p\u003e\n\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;3216)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep\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\u003eDemographic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale, n(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e457 (56.84%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e490 (60.95%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e532 (66.17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e469 (58.33%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1948 (60.57%)\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\u003eWeight,kg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62.02 (1,107)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e76.7 (46.4,110.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90.58 (55.4,135.65)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e112.35 (63,345)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82.96 (1,345)\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\u003eHeight,cm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e168 (122,203)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e170 (137,196)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e173 (132,196)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e170 (127,198)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e170 (122,203)\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\u003eBMI,kg/m2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.98 (0.42,33.77)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.61 (19.31,35.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.73 (22.58,41.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38.54 (27.34,121.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.5 (0.42,121.04)\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\u003eAge, years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66 (18,94)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66 (19,93)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64 (20,95)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60 (20,91)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64 (18,95)\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\u003eEthnicity,n(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAsian,n(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (1.87%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (1.49%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (0.37%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (0.12%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31 (0.96%)\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\u003eBlack,n(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47 (5.85%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46 (5.72%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60 (7.46%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62 (7.71%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e215 (6.69%)\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\u003eOther,n(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e280 (34.83%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e278 (34.58%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e296 (36.82%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e282 (35.07%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1136 (35.32%)\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\u003eWhite,n(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e462 (57.46%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e468 (58.21%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e445 (55.35%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e459 (57.09%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1834 (57.03%)\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\u003eICU admission\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSOFA score\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (3,8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (3,9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (3,10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (4,11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (3,9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOASIS score\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35 (29,41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35 (29,40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36 (30,42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37 (31,43)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36 (30,42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVital signs\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\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\u003eHR, bmp\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e88.5 (75,104)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90 (75,107)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90 (78,107)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93.5 (80,110)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90 (77,107)\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\u003eNBPd, mmHg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69 (58,83)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69 (58,83)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70 (58,83)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68 (57,82)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69 (58,83)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.198\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNBPs, mmHg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e123 (105,142)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e123 (106.75,141)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e124 (108,141)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e122 (104,140)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e123 (106,141)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.311\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSpO2, %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e99 (96,100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e98 (95,100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e98 (95,100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e97 (94,99)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e98 (95,100)\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\u003eTemperature,,\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36.78 (36.44,37.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36.89 (36.5,37.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36.83 (36.5,37.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36.94 (36.61,37.39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36.83 (36.5,37.28)\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\u003eLaboratory tests\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWBC,K/\u0026micro;L,\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.9 (0.3,302.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.5 (0.1,235)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.4 (0.1,96.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.9 (0.1,243.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.9 (0.1,302.5)\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,m/\u0026micro;L,\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.61 (1.15,6.15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.7 (1.16,6.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.79 (1.4,5.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.9 (1.53,6.82)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.74 (1.15,6.82)\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\u003eNeutrophil count,K/\u0026micro;L,\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.55 (0,59.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.43 (0,52.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.31 (0,67.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.56 (0.54,47.97)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.81 (0,67.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLymphocytes,K/\u0026micro;L,\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.01 (0.01,6.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.03 (0,222.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.16 (0,85.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.08 (0,222.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.06 (0,222.85)\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\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePlatelet,K/\u0026micro;L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e193 (6,1418)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e184.5 (7,701)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e191 (10,1050)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e198 (6,693)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e192 (6,1418)\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\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHemoglobin,g/dL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.9 (4.1,18.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.1 (4.1,19.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.25 (4.5,18.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.4 (5.4,18.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.2 (4.1,19.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRdw,fL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.3 (11.4,28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.3 (11.5,27.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.3 (11.1,28.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.7 (11.6,25.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.4 (11.1,28.5)\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\u003eHematocrit\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33 (12.7,54.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.4 (14.1,58.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.9 (14.4,54.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34.95 (16.9,56.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.8 (12.7,58.8)\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\u003eAlbumin,g/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (1.1,4.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (0.8,5.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.9 (1.2,5.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.9 (1,4.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.9 (0.8,5.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSodium,mEq/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e139 (103,165)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e139 (108,155)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e139 (106,165)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e138 (115,169)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e139 (103,169)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.016\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePotassium,mEq/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (1.9,9.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.1 (1.8,8.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.1 (2.4,8.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.2 (2.3,9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.1 (1.8,9.2)\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\u003eCalciumtotal,mg/dl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.2 (5.1,14.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.3 (2.5,14.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.2 (3.7,13.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.2 (3.4,15.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.3 (2.5,15.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.091\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChloride,mg/dl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e104 (64,135)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e104 (77,131)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e105 (70,125)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e103 (74,135)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e104 (64,135)\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\u003e117 (24,737)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e129 (32,754)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e140 (30,1456)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e157.5 (51,1016)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e134 (24,1456)\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\u003e5.6 (4.2,15.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.7 (4.4,13.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.9 (4.4,16.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.2 (4,16.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.8 (4,16.6)\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\u003eTG,mg/dL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93 (70,133.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e122 (86,172)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e153.5 (103.75,228)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e205 (130.75,337.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e133 (90,212)\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\u003eAniongap,mmol/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (6,47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (2,49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (3,43)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (5,37)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (2,49)\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\u003eTT,s\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.45 (13.6,150)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.2 (14.1,150)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.4 (11.6,150)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.5 (11.5,150)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.85 (11.5,150)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.506\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePT,s\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.8 (9.6,130.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (9.2,130.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.2 (9.8,150)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.2 (9.6,117.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (9.2,150)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.029\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFibrinogen,mg/dL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e294 (41,1123)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e319 (36,1020)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e320 (35,1167)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e364 (48,1466)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e323 (35,1466)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDdimer,mg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3527 (644,21240)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3513 (591,12069)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2882 (666,7462)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4385 (290,21509)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3472.5 (290,21509)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.802\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eALT,IU/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25 (1,9582)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31 (3,6616)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33 (5,15018)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35 (3,7392)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31 (1,15018)\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\u003eAST,IU/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39 (0,17600)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45 (5,16074)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48 (8,15108)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49 (6,28275)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45 (0,28275)\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\u003eUreanitrogen,mg/dl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18 (3,147)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19 (3,160)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21 (3,186)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23 (1,200)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20 (1,200)\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\u003eCreatinine,mg/dL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.9 (0.1,19.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (0.2,15.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.1 (0.3,18.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.2 (0.2,33.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (0.1,33.1)\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\u003eUricacid,mg/dl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.3 (0.6,23.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.45 (0,16.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.3 (1.4,15.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.4 (1.8,15.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.7 (0,23.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.081\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLD,U/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e279 (97,20270)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e327 (67,15850)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e344.5 (56,20500)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e348 (107,19780)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e323 (56,20500)\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\u003eCK,U/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e161 (5,148600)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e149 (4,209100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e228 (11,191820)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e225 (9,472680)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e189 (4,472680)\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\u003eCK-MB,ng/ml\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (1,423)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (1,493)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (1,458)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (1,594)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (1,594)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.26\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ecTnT, ng/m,ean(SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.12 (0.01,23.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.12 (0.01,24.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.15 (0.01,19.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1 (0.01,24.62)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.12 (0.01,24.62)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.097\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNT-proBNP,pg/mL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1999 (45,57512)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3289.5 (53,53475)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2744 (26,47825)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2068.5 (30,64845)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2687 (26,64845)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.194\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTyG index\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.66 (5.75,11.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (7.34,11.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.31 (6.95,13.87)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.74 (7.76,13.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.13 (5.75,13.87)\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\u003eTyG-BMI index\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e193.76 (3.58,218.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e240.09 (218.69,262.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e287.72 (262.04,319.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e377.31 (319.4,1021.87)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e262.03 (3.58,1021.87)\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\u003eComorbidities\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\u0026nbsp;\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\u003e312 (38.81%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e325 (40.42%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e343 (42.66%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e369 (45.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1349 (41.95%)\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\u003eDiabetes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e113 (14.05%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e162 (20.15%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e220 (27.36%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e330 (41.04%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e825 (25.65%)\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\u003eHF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e217 (26.99%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e211 (26.24%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e228 (28.36%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e240 (29.85%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e896 (27.86%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.387\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAMI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e76 (9.45%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e84 (10.45%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e105 (13.06%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e76 (9.45%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e341 (10.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.061\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCancer\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e107 (13.31%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e109 (13.56%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e75 (9.33%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51 (6.34%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e342 (10.63%)\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\u003eCKD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e105 (13.06%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e112 (13.93%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e130 (16.17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e135 (16.79%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e482 (14.99%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.112\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAKI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e291 (36.19%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e349 (43.41%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e411 (51.12%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e501 (62.31%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1552 (48.26%)\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\u003eSepsis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e194 (24.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e198 (24.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e222 (27.61)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e920 (28.61)\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\u003eDecompensated Cirrhosis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e79 (9.83%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e89 (11.07%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80 (9.95%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93 (11.57%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e341 (10.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.605\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHepatitis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53 (6.59%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43 (5.35%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59 (7.34%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63 (7.84%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e218 (6.78%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.215\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTuberculosis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 (3.73%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25 (3.11%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37 (4.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34 (4.23%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e126 (3.92%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.444\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiastroke\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e96 (11.94%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e98 (12.19%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e75 (9.33%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e75 (9.33%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e344 (10.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.097\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHyperlipidemia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e198 (24.63%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e242 (30.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e285 (35.45%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e243 (30.22%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e968 (30.1%)\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\u003eAF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e240 (29.85%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e236 (29.35%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e249 (30.97%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e244 (30.35%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e969 (30.13%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.908\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedicine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAntibiotic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e703 (87.44%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e709 (88.18%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e726 (90.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e746 (92.79%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2884 (89.68%)\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\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlucocorticoids\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e143 (17.79%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e174 (21.64%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e162 (20.15%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e169 (21.02%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e648 (20.15%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.233\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNephrotoxic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e682 (84.83%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e712 (88.56%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e715 (88.93%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e686 (85.32%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2795 (86.91%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.022\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eImmunosuppressant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34 (4.23%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41 (5.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39 (4.85%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33 (4.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e147 (4.57%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.735\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertensive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e606 (75.37%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e651 (80.97%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e686 (85.32%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e692 (86.07%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2635 (81.93%)\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\u003eEvents\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 days death,n,(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e191 (23.76%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e169 (21.02%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e162 (20.15%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e167 (20.77%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e689 (21.42%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.301\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e365 days death,n,(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e342(42.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e299(37.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e276 (34.33%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e270 (33.58%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1187 (36.91%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003ePrimary Outcomes\u003c/h2\u003e\n\u003cp\u003eKaplan-Meier curves showed significantly higher 365-day mortality in the highest TyG-BMI quartile compared to the others (p\u0026thinsp;=\u0026thinsp;0.001), with no significant difference in 30-day mortality (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.085) (in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eThe restricted cubic spline (RCS) analysis delineated an L-shaped association between the TyG-BMI index and all-cause mortality across a span of 365 days, with the index serving as a continuous variable (in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Insights from the restricted cubic spline suggest that the relationship between the TyG-BMI index and 30-day mortality does not adhere to a nonlinear trend(\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.297). In contrast, a U-shaped correlation with long-term mortality over 365 days emerges, identifying a pivotal inflection at a TyG-BMI value of 355.975. Notably, to the left of this inflection, the TyG-BMI index exhibits a negative association with long-term survival; however, beyond this threshold, the index seems to exert a protective influence against long-term mortality, with statistical significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\n\u003cp\u003eIn Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, univariable Cox regression analysis was performed to examine the association between the TyG-BMI index and the mortality rate at 365 days. Variables with clinical significance disease were incorporated into the multivariable Cox proportional hazards model. The model 1 solely included the TyG-BMI index without any further adjustments(Q2 HR:0.85,95%CI:0.73\u0026ndash;0.99, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039, Q3 HR: 0.78,95%CI:0.66\u0026ndash;0.91, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, Q3 HR: 0.76, 95%CI: 0.65\u0026ndash;0.89, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). In Model 2, adjustments were made for age, gender, and race.(Q2 HR:0.85,95%CI:0.73\u0026ndash;0.99, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037, Q3 HR: 0.79,95%CI:0.67\u0026ndash;0.93, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004, Q3 HR: 0.84, 95%CI: 0.72\u0026ndash;0.99, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038)Model 3 incorporated further adjustments for laboratory tests and comorbidities such as Hypertension, Diabetes, HF(Heart Feature),DC, Hepatitis, Pneumonia, Diastroke, Spesis,AKI,WBC,creatinine.( Q2 HR:0.81,95%CI:0.68\u0026ndash;0.96, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016, Q3 HR: 0.72,95%CI:0.57\u0026ndash;0.87, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, Q3 HR: 0.66, 95%CI: 0.46\u0026ndash;0.88, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006). The reference category for all models was the lowest quartile of the TyG-BMI index.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Taba\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cp\u003eTable 2\u003c/p\u003e\n\u003cp\u003eAssociation between serum Klothoand all-cause mortality in chronic kidney disease.\u003c/p\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCategories\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eModel 1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eModel 2\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eModel 3\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\u003eTyG-BMI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEvents(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHR (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP-value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHR (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP-value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHR (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP-value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ1(N\u0026thinsp;=\u0026thinsp;804)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e342(42.5%)\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\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\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\u003eQ2(N\u0026thinsp;=\u0026thinsp;804)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e299(37.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.85[0.73,0.99]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.039\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.85[0.73,0.99]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.037\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.81[0.68,0.96]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.016\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ3(N\u0026thinsp;=\u0026thinsp;804)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e276(34.33%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.78[0.66,0.91]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.79[0.67,0.93]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.72[0.57,0.87]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ4(N\u0026thinsp;=\u0026thinsp;804)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e270(33.58%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.76[0.65,0.89]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.84[0.72,0.99]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.038\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.66[0.46,0.88]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003eData were presentedas hazard ratios (95% confidence intervals), P-value.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003eModel 1did not adjust for any covariate.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003eModel 2 was adjusted for age (continuous), sex (male or female), and race/ethnicity.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003eModel 3was adjusted for model2 and additional covariates, including hypertension (yes or no), age(༞65or\u0026thinsp;\u0026le;\u0026thinsp;65), HF(yes or no), CKD(yes or no), diabetes(yes or no), AKI(yes or no), DC(yes or no), pneumonia(yes or no), diastroke(yes or no), sepsis(yes or no), WBC(continuous), Creatinine(continuous).\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, subgroup analysis revealed that the TyG-BMI index demonstrated a more pronounced protective effect in older individuals (over 65 years of age),and patients with hypertension, and there were observed interactions with gender, age, and the presence of diabetes.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tabb\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cp\u003eTable 3\u003c/p\u003e\n\u003cp\u003eSubgroup analysis\u003c/p\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003esubgroups\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCase\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eQ1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eQ2\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eQ3\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eQ4\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eP for Interaction\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHR(95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHR(95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHR(95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\" align=\"left\"\u003e\n\u003cp\u003eAge,years\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e476\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1736\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\u003e0.741(0.562,0.976)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.033\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.910(0.700,1.181)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.477\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.761(0.588,0.986)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.039\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026le;\u0026thinsp;65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e710\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1480\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\u003e0.857,(0.708,1.037)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.112\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.628(0.509,0.775)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.603(0.477,0.763)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\" align=\"left\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\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\u003e682\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1948\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\u003e0.724(0.586,0.893)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.650(0.526,0.803)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.694(0.554,0.871)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.08\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\u003e504\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1268\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\u003e0.937(0.739,1.187)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.589\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.835(0.648,1.076)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.164\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.620(0.477,0.807)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\" align=\"left\"\u003e\n\u003cp\u003eDiabetes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYES\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e332\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e825\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\u003e0.854(0.605,1.205)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.369\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.695(0.500,0.967\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.031\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.467(0.335,0.650)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.016\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e854\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2391\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\u003e0.794(0.665,0.949)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.719(0.595,0.868)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.808(0.662,0.986)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.036\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\" align=\"left\"\u003e\n\u003cp\u003ehypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYES\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e485\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1349\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\u003e0.741(0.58,0.947)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.563(0.435,0.730)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.587(0.4490.768)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.069\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e701\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1867\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\u003e0.875(0.713,1.075)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.205\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.851(0.691,1.049)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.131\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.741(0.593,0.926)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\" align=\"left\"\u003e\n\u003cp\u003eHF\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYES\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e388\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e896\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\u003e0.761(0.578,1.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.053\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.676(0.512,0.894)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.540(0.402,0.727)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.297\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e798\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2320\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\u003e0.852(0.704,1.032)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e101\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.765(0.627.935)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.756(0.613,0.934)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\" align=\"left\"\u003e\n\u003cp\u003eAKI\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYES\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e459\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1552\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\u003e0.930(0.7481.156)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.513\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.849(0.684,1.053)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.137\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.709(0.570,0.884)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.132\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e727\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1664\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\u003e0.689(0.546,0.869)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.571(0.4410.741)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.667(0.499,0.890)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\" align=\"left\"\u003e\n\u003cp\u003eDC\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYES\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e179\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2341\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\u003e0.984(0.633,1.528)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.942\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.052(0.664,1.666)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.830\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.963(0.612,1.515)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.871\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.106\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2875\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\u003e0.789(0.666,0.934)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.681(0.572,0.811)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.626(0.520,0.755)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\" align=\"left\"\u003e\n\u003cp\u003ePneumonia\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYES\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e611\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1345\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\u003e0.755(0.602,0.946)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.688(0.545,0.869)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.6680.5250.851\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.829\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e575\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1871\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\u003e0.856(0.685,1.069)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.170\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.728(0.578,0.917)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.661(0.518,0.844)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ediastroke\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"10\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYES\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e138\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e344\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\u003e0.963(0.621,1.495)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.868\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.588(0.356,0.974)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.049\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.579(0.343,0.978)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.051\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.266\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1048\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2872\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\u003e0.792(0.669,0.938)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.745(0.627,0.885)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.673(0.561,0.807)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003espesis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"10\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e469\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e920\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\u003e0.797(0.606,1.049)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.106\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.826(0.632,1.081)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.164\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.674(0.515,0.880)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.468\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e717\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2296\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\u003e0.824(0.680,0.998)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.048\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.665(0.541,0.817)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.663(0.529,\u0026nbsp;0.831)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur investigation represents the inaugural exploration into the correlation between the TyG-BMI index and the long-term survival rates of patients in Intensive Care Units (ICUs), encompassing a cohort of over three thousand individuals to bolster the robustness of our findings. Within the context of this study, we discerned a substantial association between an elevated TyG-BMI index and a decrement in in-hospital mortality among critically ill patients, positioning the TyG-BMI index as a protective factor in this demographic. In models adjusted for a multitude of confounding variables, this protective correlation was further amplified. Moreover, when analyzed as a continuous variable, the TyG-BMI index exhibited a U-shaped relationship with long-term mortality rates.\u003c/p\u003e \u003cp\u003eIn reality, the Triglyceride-Glucose (TyG) index has emerged in recent years as a putative marker of insulin resistance(IR). Given its relatively recent inception, research into the TyG index, while burgeoning, still lacks substantial empirical evidence to corroborate its association with disease pathogenesis.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e][\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e][\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] Contemporary research posits that the IR acts as a risk factor for a variety of diseases, including cardiovascular diseases[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], cerebrovascular events[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e][\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], the onset of hypertension[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and poor prognosis in oncological outcomes. However, in critically ill populations, the applicability of IR markers in predicting adverse events remains a contentious issue. For instance, researchers have identified the Triglyceride-Glucose (TyG) index as a protective factor in the prognosis of diabetic foot outcomes.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] Concurrently, scholarly articles have reported that the Triglyceride-Glucose-Body Mass Index (TyG-BMI) demonstrates a protective role in patients with chronic kidney disease[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] or heart failure[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, based on our research considerations, the TyG-BMI index, which reflects nutritional status as well as the level of insulin resistance, may have a complex relationship with patient survival. The findings presented in this article echo this perspective, demonstrating a protective effect of the TyG-BMI index on ICU patients when incorporated as a quartile into Cox regression models. When analyzed as a continuous variable using restricted cubic spline analysis, the emergence of a U-shaped relationship suggests the existence of an optimal TyG-BMI range. Exceeding this range, the long-term survival rate of ICU patients begins to decline; however, this downward trend is considerably less pronounced than in intervals with lower TyG-BMI index values.\u003c/p\u003e \u003cp\u003eSimultaneously, our subgroup analysis revealed that the protective effect of the TyG-BMI index is more pronounced in populations with comorbidities such as diabetes, hypertension, or heart failure. This protective association was not observed in groups with comorbidities of other systems. This finding, to some extent, underscores the unique relationship of the TyG-BMI index with the cardiovascular system and highlights its protective role in critically ill cohorts. We speculate that this may be related to the elevated levels of inflammation present in patients with critical illness.\u003c/p\u003e \u003cp\u003eInsulin resistance, to a certain extent, reflects the body\u0026rsquo;s level of inflammation, and an appropriate inflammatory response is crucial for critically ill patients, especially those with concurrent cardiovascular conditions. Additionally, the nutritional status can significantly impact the survival outcomes of critically ill patients. Within the ICU setting, sustained insulin resistance may exacerbate systemic inflammatory responses[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e][\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e][\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e][\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. From a physiological standpoint, a certain degree of insulin resistance and inflammation might serve as an adaptive mechanism to acute trauma or infectious challenges. Optimal insulin resistance levels allow glucose to be redirected into intracellular stores, acting as a crucial mechanism to preserve energy reserves during periods of intense physiological stress[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e][\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Moreover, a subset of ICU patients may demonstrate a distinct survival advantage, potentially attributable to a more robust metabolic response. Elevated TyG-BMI indices in these patients may indicate enhanced nutritional reserves and metabolic vigor, potentially endowing them with an increased capacity to endure the hardships of prolonged disease and the demands of ongoing medical interventions[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e][\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. From this perspective, an increased TyG-BMI may not signify a dire prognosis but rather suggest the potential for greater physiological resilience and recovery.\u003c/p\u003e \u003cp\u003eCertainly, given the retrospective cohort of our study, there are numerous limitations inherent to its design. It is possible that the utility of the TyG-BMI index differs in a general, healthy population, a hypothesis that necessitates further investigation for validation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eTyG-BMI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTriglyceride-Glucose-Body Mass Index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBody mass index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDecompensated Cirrhosis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHF\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHeart Failure\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAKI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcute Kidney Injury\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe greatly acknowledge the diligent efforts of the personnel responsible for the design and maintenance of the MIMIV database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFYQ, XC, WGF were responsible for the study concept and study design. Data extraction was undertaken by FYQ, WGF and TYN were responsible for data analysis and drafting of the manuscript. Critical revision of the manuscript for important intellectual content: LYW. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work did not receive any funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first author can be contacted to receive the datasets generated and utilized in this work upon reasonable request and with MIMIC\u0026rsquo;s permission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLee J, Cho YJ, Kim SJ, Yoon HI, Park JS, Lee CT, Lee JH, Lee YJ. 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The Effect of Nutrition on Early Stress-Induced Hyperglycemia, Serum Insulin Levels, and Exogenous Insulin Administration in Critically Ill Patients With Septic Shock: A Prospective Observational Study. Shock. 2019;52(4):e31-e38. doi:10.1097/SHK.0000000000001287\u003c/li\u003e\n\u003cli\u003eSinger P. Preserving the quality of life: nutrition in the ICU. Crit Care. 2019;23(Suppl 1):139. Published 2019 Jun 14. doi:10.1186/s13054-019-2415-8\u003c/li\u003e\n\u003cli\u003eRusavy Z, Sramek V, Lacigova S, Novak I, Tesinsky P, Macdonald IA. Influence of insulin on glucose metabolism and energy expenditure in septic patients. Crit Care. 2004;8(4):R213-R220. doi:10.1186/cc2868\u003c/li\u003e\n\u003cli\u003eMatulewicz N, Karczewska-Kupczewska M. Insulin resistance and chronic inflammation. Postepy Hig Med Dosw (Online). 2016;70(0):1245-1258. Published 2016 Dec 20. doi: 10.5604/01.3001.0010.5809.\u003c/li\u003e\n\u003cli\u003eBear DE, Wandrag L, Merriweather JL, et al. The role of nutritional support in the physical and functional recovery of critically ill patients: a narrative review. Crit Care. 2017;21(1):226. doi: 10.1186/s13054-017-1810-2\u003c/li\u003e\n\u003cli\u003eMira JC, Brakenridge SC, Moldawer LL, Moore FA. Persistent inflammation, immunosuppression and catabolism syndrome. Crit Care Clin. 2017;33(2):245\u0026ndash;258. doi: 10.1016/j.ccc.2016.12.001. \u003c/li\u003e\n\u003cli\u003eKhan SH, Sobia F, Niazi NK, Manzoor SM, Fazal N, Ahmad F. Metabolic clustering of risk factors: evaluation of Triglyceride-glucose index (TyG index) for evaluation of insulin resistance. Diabetol Metab Syndr. 2018;10:74. doi: 10.1186/s13098-018-0376-8. eCollection 2018.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Insulin resistance, Prognosis, Triglyceride Glucose-Body Mass Index (TyG-BMI), Critical care","lastPublishedDoi":"10.21203/rs.3.rs-3839347/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3839347/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe Triglyceride Glucose (TyG) index has recently been considered an accurate surrogate biomarker for assessing insulin resistance (IR). The TyG-BMI index, integrating the Body Mass Index (BMI), has been recognized by numerous studies as a superior representation of IR status. This research aimed to investigate the relationship between the TyG-BMI index and long-term mortality risk in critically ill patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ePatient data for this study were sourced from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, from which the TyG-BMI indexes were extracted. The primary endpoint was all-cause mortality within one year. Kaplan-Meier survival analysis was utilized to compare the primary endpoint across quartiles. Restricted cubic splines and Cox proportional hazards analyses were employed to explore the association between the TyG-BMI index and the endpoint.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 3,216 patients admitted to the ICU were included in the study. Kaplan-Meier analysis revealed that patients with higher TyG-BMI index values had a significantly reduced risk of death (log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, restricted cubic spline analysis indicated a U-shaped relationship between the TyG-BMI index and long-term mortality. Furthermore, multivariable Cox proportional hazard analysis showed that the highest quartile of the TyG-BMI index, compared to the lowest quartile, had a hazard ratio (HR) of 0.66(95% CI: 0.46, 0.88; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for one-year mortality, suggesting a protective effect.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAmong critically ill patients, the highest quartile of the TyG-BMI index was associated with a lower rate of long-term mortality. The TyG-BMI index also demonstrated a U-shaped relationship with long-term mortality, suggesting the existence of an optimal TyG-BMI range that may confer protective effects within a certain interval for critically ill patients.\u003c/p\u003e","manuscriptTitle":"Association between the triglyceride glucose body mass index and long-term mortality in ICU patients: a cohort study of over 3000 patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-10 21:54:10","doi":"10.21203/rs.3.rs-3839347/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":"161b827f-13a4-4848-95c0-2824d6437c4b","owner":[],"postedDate":"January 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28035057,"name":"Health sciences/Diseases/Endocrine system and metabolic diseases"},{"id":28035058,"name":"Health sciences/Diseases/Metabolic disorders"},{"id":28035059,"name":"Health sciences/Diseases/Nutrition disorders"}],"tags":[],"updatedAt":"2024-03-05T08:32:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-10 21:54:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3839347","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3839347","identity":"rs-3839347","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}
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