Erythrocyte Stress Index Promotes The Development of Type 2 Diabetes To Heart Failure: Result From Two Cohorts

Erythrocyte Stress Index Promotes The Development of Type 2 Diabetes To Heart Failure: Result From Two Cohorts | 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 Erythrocyte Stress Index Promotes The Development of Type 2 Diabetes To Heart Failure: Result From Two Cohorts Tianye Gao, Jie Li, Lin Zhang, Jingyi Lin, Xiangqin Ou, Ying Zhou, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7492293/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 Aim: To assess whether the erythrocyte stress index (ESI), a novel indicator to characterize erythrocytes, promotes the type 2 diabetes (T2D) patients developed into heart failure. Methods: Two cohorts were contained, the Medical Information Mart for Intensive Care (MIMIC) database, and the Tianjin HF with Integrated Treatment (TJHFIT). Among the 15,813 T2D patients with follow-up time (mean of 1,532 days and 959 days in the two cohorts), 3,711 finally developed HF. ESI was compared with two indicators associated with HF onset: red blood cell distribution width coefficient of variation (RDWCV) and estimated plasma volume status (ePVS). ESI was stratification with quartiles (ESIQ). The Cox analysis, restricted cubic spline (RCS), and Kaplan–Meier (KM) curves were applied. Results: The incidence of HF in T2D patients across ESI quartiles was 17%, 22%, 27%, and 29%, respectively. In MIMIC, the adjusted hazard ratios (aHR) with 95% confidence interval (CI) of ESI, RDWCV, and ePVS were 1.27 (1.20, 1.35), 1.07 (1.05, 1.10), and 1.05 (1.02, 1.07), the ESI had the highest aHR. Restricted cubic splines demonstrated that ESI, RDWCV, and ePVS exhibited an S-shaped non-linear relationship, with cut-off values of 4.19 mL/g for ESI. Kaplan-Meier curves indicated that T2D patients with ESI >4.19 mL/g had a higher probability of HF than those with ESI ≤ 4.19mL/g. Notably, ESI remained a significant predictor of HF even in T2D patients with RDWCV, and ePVS lost their predictive value. Conclusion : The ESI served as an independent prognostic marker for the development of HF in patients with T2D. Even though both RDWCV and ePVS lost their predictive value, ESI can still play its role. Health sciences/Cardiology Health sciences/Diseases Health sciences/Endocrinology Health sciences/Medical research Health sciences/Risk factors Erythrocyte stress index Indicator Type 2 diabetes Heart Failure Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Type 2 Diabetes (T2D) is an independent risk factor 1 , 2 of heart failure (HF). T2D-induced erythrocyte dysfunction (e.g., decreased oxygen-carrying capacity) aggravates the clinical symptoms of HF 3 . Although HF shows myelosuppression 4 with erythrocyte dysfunction, few studies focus on whether T2D-induced erythrocyte dysfunction promotes the incidence of HF. Furthermore, erythrocytes will be a potential target for treating cardiovascular disease 5 , and empagliflozin will increase erythropoiesis 6 and decrease the HF risk for T2D patients. So, erythrocyte is a bridge to explore the relationship between T2D and HF for the occurrence, development, and mechanism. There were two questions. Firstly, traditional indicators of erythrocytes can only describe either erythrocyte volume or hemoglobin, such as the red blood cell distribution width coefficient variable (RDWCV), lacking a measure that characterizes both simultaneously. Secondly, although numerous studies have examined erythrocyte indicators on the prognosis of T2D 7 , HF 8 9 , and T2D-HF 10 , few studies have focused on whether these indicators contribute to the progression of T2D to HF. To fill the gap of knowledge, we have coined the concept of “erythrocyte stress index” (ESI) for the first time in this study, bringing in a new method to simultaneously characterize both the erythrocyte volume standard deviation and the hemoglobin. We established an ESI calculation strategy and investigated the association between ESI and T2D-derived HF. Because both RDWCV 11 12 and estimated plasma volume statute (ePVS) 13 – 15 have been verified for HF prognosis. So Both indicators were applied as a reference to evaluate the predictive value of ESI. Specifically, this study contained two cohorts and focuses on the development of HF from T2D: i) whether ESI is an independent marker that promotes the T2D patient's development into HF; ii) whether ESI predictive value is better than the RDWCV and ePVS; iii) when RDWCV and ePVS lose the predictive value, whether ESI still plays the role. 2. Methods 2.1 Participants The study included two prospective cohorts, the Medical Information Mart for Intensive Care (MIMIC) database, and the Tianjin HF of integrated treatment (TJHFIT). MIMIC is free of charge until users pass a test to be approved by the MIMIC-IV database manager. The author (Dr. Lin Zhang) was supported in extracting data for the study (certification number record ID: 55394138). The participants were enrolled between January 1, 2008, and December 31, 2019. TJHFIT was registered ( ChiCTR2300077220 ) which aims to explore the curative effects of combining traditional Chinese and Western medicine in treating HF in the Tianjin region. This study is a part of the above TJHFIT cohort. The participants were enrolled between January 1, 2006, and December 31, 2018, from 43 tertiary hospitals and 39 hospitals in Tianjin, China. 2.2 Definition of T2D and HF The diagnosis of T2D depends on the international classification of diseases (ICD) codes and clinical features. 129 T2D-relevant ICD codes are listed in Table S1 . The clinical features diagnosis of T2D included four criteria: i) patient's discharge diagnosis was T2D), ii) glycated hemoglobin A1c ≥ 6.5%, iii) fasting plasma glucose ≥ 7.0 mmol/L, iv) 2-hour plasma glucose ≥ 11.1 mmol/L during an oral glucose tolerance test. The diagnosis of HF relied on the 2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure 16 , 17 . The diagnosis of HF involved ICD codes ( Table S2 ). 2.3 Calculation Strategy of ePVS and EFI As the previous studies indicated 13 , 18 , the ePVS (mL/g) = \(\:\frac{100-\text{H}\text{e}\text{m}\text{a}\text{t}\text{o}\text{c}\text{r}\text{i}\text{t}}{\text{H}\text{e}\text{m}\text{o}\text{g}\text{l}\text{o}\text{b}\text{i}\text{n}\:(\text{g}/\text{d}\text{L})}\) . The calculation strategy of the ESI is illustrated in the following formula. The ESI is defined as RDWCV divided by mean corpuscular hemoglobin concentration (MCHC). Firstly, RDWCV is determined as the standard deviation of erythrocyte volume (EVSD) divided by the mean corpuscular volume (MCV) 19 . Secondly, MCHC 20 is calculated as the MCH divided by the mean corpuscular volume (MCV). Subsequently, ESI is computed as the ratio of RDWCV to MCHC. Notably, as MCV is a component in both RDWCV and MCHC calculations, it effectively cancels out. Thus, ESI is ultimately equivalent to EVSD divided by MCH. According to the formula, ESI can simultaneously characterize the erythrocyte volume standard deviation and the mean hemoglobin levels. An elevation in ESI indicates an increased erythrocyte volume standard deviation or a reduction in mean hemoglobin content, indicative of a metabolic disturbance in the erythrocyte. 2.4 Other clinical features Three types of clinical features were included, demographics, history of disease, and laboratory tests. The demographic variables consisted of age and sex. The history of disease encompassed hypertension, hyperlipemia, hypercholesterolemia, chronic kidney diseases (CKD), atrial fibrillation (AF), atherosclerotic cardiovascular disease (ASCVD), nicotine dependence, anemia, hyperuricemia, chronic obstructive pulmonary disease (COPD), and length of stay (LOS). The laboratory tests are composed of blood routine and biochemical tests. 2.5 Follow-up time All included individuals were T2D patients. The end-point event was defined as HF. In MIMIC, the follow-up time was defined as the duration from T2D diagnosis to HF onset until December 31, 2022. In TJHFIT, the follow-up time was defined as the duration from T2D diagnosis to HF onset until December 31, 2023. 2.6 Statistical analysis The analytical procedures were conducted using R (version 4.3.1). Covariates with missing data from over 20% of the population were excluded from the analysis. For covariates with missing values ranging between 0 and 19%, multiple interpolation was performed using the mice R package. Within the mice package, a seed value of 3 was specified, and "pmm" was employed as the imputation method. We analyzed the clinical features from the start of follow-up. With the basement of the Cox analysis, the hazard ratio (HR) was conducted in the T2D vs T2D-HF. Confidence intervals (CIs) were employed to illustrate the fluctuation of HR. To investigate the relationship between the ESI and T2D-HF, four models with adjusted parameters were established. Model 1: no covariates included; Model 2: age + sex; Model 3: Model 2 + LOS + history of disease; Model 4: Model 3 + laboratory indicators with differences. To investigate the influence of ESI variation, ESI quartile stratification was employed. In TJHFITM, ESI (mL/g) quartiles (ESIQ) were defined as follows: Q1: [2.89,3.67], Q2: (3.67,3.89], Q3: (3.89,4.22], and Q4: (4.22,13.89]. In MIMIC, ESI (mL/g) quartiles (ESIQ) were defined as follows: Q1: [3.13,3.9], Q2: (3.91,4.16], Q3: (4.17,4.55], and Q4: (4.56,10.46]. 3. Results 3.1 Baseline characteristics Applying the inclusion and exclusion criteria resulted in the enrollment. Among the 15,813 T2D patients with full follow-up time (the mean of TJHFIT is 1,532 days and MIMIC is 959 days), 3,711 developed HF, and 12,102 did not. Specifically, 11,547 T2D patients ( Fig. 1 ) in MIMIC included 2,710 T2D patients who developed HF and the other 8,837 did not. In TJHFIT, 4,266 T2D patients were included 1,001 developed HF, and the other 3,265 T2D patients did not. The baseline characteristics of MIMIC in the 11,547 T2D patients were summarized in Table S3 . ESI was significantly higher in the T2D-HF group (median, 4.24 mL/g) than in the T2D group (median, 4.14 mL/g) (P < 0.001). The T2D-HF group had a higher ePVS(median, 5.86 mL/g), and RDWCV (median,14.10 %) than the T2D group( P < 0.001). However, there was no significant difference in MCH, MCV, and MCHC between the two groups. Among the 4,266 T2D in TJHFIT ( Table S4 ), ESI was significantly higher in the T2D-HF group (median, 4.12 mL/g) than in the T2D group (median, 3.83 mL/g) (P < 0.001). Among the other clinical characteristics, the T2D-HF patients had higher ePVS (median, 4.80 mL/g), RDWCV (median, 13.40 %), MCV (median, 90.90 fL) than T2D, and had lower MCH (median, 29.80 pg), MCHC (median, 329.61 g/dL) than T2D group. To better illustrate the ESI influence on T2D-HF, the ESIQ was also analyzed. In MIMIC, the ( Table 1 ) T2D-HF incidence of ESIQ (Q1-Q4) was 19%, 22%, 26%, and 27%, respectively. The HF incidence in the highest quartile (Q4) of ESI was 1.42 greater than in the lowest quartile (Q1). TJHFIT repeated a similar trend ( Table S5) , the T2D-HF incidence of ESIQ was 15%, 21%, 27%, and 31%, respectively ( P < 0.001). Among the two cohorts, the mean prevalence of HF in ESIQ was 17%, 22%, 27%, and 29%. Table 1 . Baseline characteristics for EFIQ in MIMIC 3.2 Cox regression 3.2.1 T2D vs T2D-HF In TJHFIT, the univariate and adjusted Cox regression analyses ( Table 2 ) indicated the HRs of ESI (95% CIs) of the four models were as follows: 1.25 (1.21, 1.30), 1.25 (1.21, 1.29), 1.24 (1.19, 1.28), and 1.21 (1.17, 1.26). As for the RDWCV, the HRs (95% CIs) for the 4 models were only 1.09 (1.07, 1.10), 1.09 (1.07, 1.10), 1.08 (1.06, 1.10), and 1.07 (1.06, 1.09) respectively ( P < 0.01). In addition, the HRs (95% CIs) of ePVS for the 4 models were only 1.12 (1.09, 1.15), 1.13 (1.09, 1.16), 1.09 (1.05, 1.13), and 1.05 (1.01, 1.09), respectively ( P < 0.01). When setting Q1 as the reference, the HRs of Model 4 for Q2, Q3, and Q4 became 1.38 (1.12, 1.69), 1.80 (1.48, 2.19), 1.81 (1.49, 2.20), respectively ( P < 0.01), indicating that T2D patients with higher ESI were more prone to HF. Furthermore, the HR values of the four models for MCHC were lower than 1. The MIMIC repeated a similar trend ( Table 2 ). In particular, both cohorts indicated the HRs of ESI in each model were much higher than the RDWCV and ePVS, indicating that ESI exhibited better predictive performance than the RDWCV and ePVS. Table 2 . The Cox regression of the T2D-HF patients. 3.2.2 Subgroup analysis Nine subgroups of Cox analysis were processed, sex, year > 60 or not, AF, Anemia, ASCVD, CKD, Hyperlipidemia, Hypertension, and Hyperuricemia. To eliminate the effect of covariates, four models described above were used for adjustment, and the results of ESI, ESIQ, ePVS, RDWCV, and MCHC were detailed ( Table S6-S7 ). In all subgroups, the ESI had the highest HR than RDWCV and ePVS. Among the nine subgroups, four were consistent between the TJHFIT and MIMIC, including sex, AF, Anemia, and CKD ( Fig 2 ), the interaction p-value was not all < 0.05. Especially, the ESI’s result with Model 4 adjusted was summarised ( Fig 2 ). Only the interaction for the anemia subgroup was significant in both MIMIC and TJHFIT. This indicated when ESI increased, the non-anemia patients had a higher HR than anemia patients. 3.3 RCS analysis RCS analysis was applied to optimize the cut-off value for T2D-HF ( Fig. 3 ) in ESI, ePVS, and RDWCV. RCS plots of ESI, ePVS, and RDWCV were not linear and had an S-like wave. According to the density of ( Fig. 3 A ), a majority of patients distribute between 3 and 5 in ESI. And increasing ESI indicated an increasing HR for T2D-HF. The 4.19 is the closest value of HR to 1 for ESI. Additionally, the RCS indicated the threshold for HR value equal to 1 for ESI is 4.19 mL/g( Fig. 3 A ). Similarly, the cut-off values of ePVS and RDWCV were finished, 5.64 mL/g and 13.92%, respectively ( Fig. 3 B-C ). 3.4 Analysis for patients with lower RDWCV and ePVS According to the cut-off value from the above RCS analysis, individuals were classified into a high ESI group (ESI > 4.19 mL/g) or a low ESI group (ESI ≤ 4.19 mL/g). In T2D patients ( Fig. 4 ), the high ESI group had a higher probability of HF than the low group ( P < 0.001).In T2D patients ( Fig. 4 A ), the high ESI group had a higher probability of HF than the low group ( P < 0.001). Similarly in the ESIQ ( Fig. 4 B ), T2D patients with higher quartiles of ESI had a higher probability of HF ( P < 0.01). To evaluate the predictive value in lower RDWCV and ePVS groups, the Cox analysis was finished in the patients with RDWCV < 13.92 or ePVS < 5.64. In the patients with RDWCV < 13.92 ( Fig. 4 C ), even though the RDWCV shows a worse HR value, ESI indicated a significant HR, and the HR value and 95%CI in Model 4 was 1.58 (1.19, 2.08). Similarly, in the patients with ePVS < 5.64 ( Fig. 4 D ), even though ePVS indicated a worse predictive value, both ESI and RDWCV indicated a higher HR, especially ESI with HR value of 95%CI in Model 4 was 1.48 (1.29, 1.69). 4. Discussion Among 15,813 T2D patients, 3,711 finally developed into HF. The multiple Cox regression indicated that ESI, RDWCV, and ePVS were independent risk factors for T2D-HF. ESI had a higher HR than both RDWCV and ePVS. Subgroupp indicated that no-anemia had higher ESI HR than anemia patients. RCS had shown the cutoff values for EFI, ePVS, and RDWCV were 4.19 mL/g, 5.64 mL/g, and 13.92%, respectively. When T2D patients with RDWCV < 13.92% or ePVS 4.19 mL/g) T2D patients had a higher HF probability than the low ESI group (≤ 4.19 mL/g). We validated that ESI was an independent risk factor for T2D patients who developed HF (Table 2 ). Moreover, with the increase in ESI, especially when ESI > 4.19 (Fig. 4 A), the incidence of HF significantly increased. ESI represents novel evidence of erythrocyte dysfunction in T2D-HF. The traditional understanding of erythrocyte dysfunction in T2D has predominantly focused on erythrocyte volume parameters such as RDWCV 21 and MCV 22 , as well as glycosylated hemoglobin levels 22 , rather than other hemoglobin indices. However, as a comprehensive measure integrating erythrocyte volume and hemoglobin, ESI demonstrates superior utility in assessing T2D-derived HF. ESI offers novel insights into the physiological and pathological mechanisms underlying T2D-HF, thereby potentially informing the development of therapeutic strategies. Subgroup analysis in this study showed that with an increase in ESI, the HR non-anemia was higher compared to anemia T2D patients (Fig. 2 ). Therefore, ESI has significant potential in the classified management of the aforementioned subgroup. For instance, RDWCV 23 and MCHC 24 are served in the diagnosis of anemia subtype. Because ESI is derived from RDWCV and MCHC, it may hold promise in diseases associated with anemia. Additionally, ESI in the current study outcompetes RDWCV in predicting incidence 25 and prognosis 26 in HF. However, further research is needed to explore its role as an epidemiological and prognostic marker. Compared with RDWCV and ePVS, the ESI had a higher HR value for T2D-HF. More importantly, ESI still had the prognosis potential when RDWCV and ePVS lost their predictive value. Previous research indicated patients with ePVS > 5.5 mL/g 18 might be in a state of plasma capacity overload. In our research, the T2D patients with ePVS 5.64 mL/g patients. Importantly, ESI still had predictive value, especially in plasma capacity-decreased T2D patients. T2D patients with RDWCV < 13.92% indicated normal changes in red cell volume (normal individuals were no more than 14%), and ESI still had predictive potential even though RDWCV lost the forecast value. So, ESI might still have a predictive value in T2D patients with normal plasma capacity and normal red cell volume changes. Compared to other emerging indicators, ESI possesses three distinctive characteristics: biological significance, cost-effectiveness for patients, and simplicity in clinical application. Another novel index associated with RDWCV, the RDWCV serum albumin ratio (RAR) 27 , has garnered increased attention in recent years. While RAR demonstrates statistical significance, its underlying biological significance warrants further investigation. RDWCV reflects erythrocyte volume changes, while serum albumin primarily indicates nutritional status 28 , raising questions about the biological relevance of RAR. Additionally, RDWCV 29 , 30 belongs to hematology, whereas serum albumin originates in biochemistry, potentially increasing financial burdens on patients. Both RDWCV and MCHC, derived from hematology, offer potential cost-saving benefits for patients. In contrast to costly genetic tests (e.g., dilated cardiomyopathy 31 and acute myocardial infarction 32 , ESI can be readily obtained in clinical settings and is more operationally feasible. This study has highlighted these innovative points: i) ESI serves as an independent epidemic marker in T2D-developed HF with a cut-off value of 4.19 mL/g; ii) the predictive value of ESI is much higher than RDWCV and ePVS; iii) when RDWCV and ePVS lose the predictive value, ESI still plays the role. This study possesses three notable strengths: i) two queue mutual authentication, ii) explores T2D evolved into the HF rather than HF mortality or prognosis, providing a reference for the primary prevention of HF, and iii) various characteristics were included for model adjustment and robust results. However, as previously acknowledged 33 , two limitations merit consideration. Firstly, ESI measurements were conducted only at a single time point, potentially leading to misclassification. However, any misclassification would likely result in underestimation rather than overestimation due to the influence of regression dilution bias 34 . Lastly, we did not finetune the Cox analysis according to medication history. However, many lab tests and history of diseases were already included for adjustment which could substitute the deficiency of medication history. 5. Conclusion ESI promotes T2D developed to HF and was an independent epidemic marker of T2D-derived HF. The ESI had a better predictive value than RDWCV and ePVS. When T2D patients with lower RDWCV or ePVS and both loss forecasting values, ESI can still predict the T2D-HF. ESI might offer a novel sight for T2D-HF prevention and treatment. The value of ESI in other diseases is unknown and deserves further exploration. Declarations Acknowledgments Thanks to Jie Li and Jie Liu for putting forward the idea of the research, Lin Zhang and Jingyi Lin provided the original data and ethics. Authors’ contributions TYG and JL wrote the initial draft. LZ and JYL provided the original data and obtained ethical approval. LZ was responsible for the methodology. XQO, YZ, and YW analyzed and organized the data. JL and JL managed project administration and revised the manuscript. Funding No funding. Ethics approval In TJHFIT, the ethics committee of First Teaching Hospital of Tianjin University of Traditional Chinese Medicine passed the ethics approval ( TYLL2023[K] ). The research has been registered ( ChiCTR2300077220 ). In MIMIC, the author (Dr. Lin Zhang) was supported in extracting data from this database for the study (certification number record ID: 55394138 ). Conflict of interest The authors meet the criteria for authorship as recommended by the International Committee of Medical Journal Editors and did not receive payment related to the development of this manuscript. All authors declares no conflict of interest. Data availability statement The primary data from MIMIC can be obtained from the link ( https://mimic.mit.edu/ ). TJFHIT is not in a public database but the primary data can be obtained by contacting the authors. References Dal Canto, E. et al. Diabetes as a cardiovascular risk factor: An overview of global trends of macro and micro vascular complications. Eur J. Prev. Cardiol Dec. 26 (2_suppl), 25–32. 10.1177/2047487319878371 (2019). Lee, K. S. et al. 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Wang, Y. et al. Fasting triglycerides are positively associated with cardiovascular mortality risk in people with diabetes. Cardiovasc Res May . 2 (3), 826–834. 10.1093/cvr/cvac124 (2023). Tables Table 1 . Baseline characteristics for ESIQ in MIMIC Characteristic Q1, N = 2,931 Q2, N = 2,842 Q3, N = 2,913 Q4, N = 2,861 P value T2D-HF, % 550(19) 632(22) 764(26) 764(27) <0.001 ESI, mL/g 3.75 (3.64, 3.84) 4.04 (3.97, 4.10) 4.33 (4.24, 4.43) 4.94 (4.71, 5.34) <0.001 ePVS, mL/g 5.11 (4.42, 5.99) 5.33 (4.61, 6.21) 5.75 (4.90, 6.69) 6.65 (5.59, 8.00) <0.001 RDWCV, % 12.90 (12.50, 13.20) 13.60 (13.20, 13.90) 14.30 (13.90, 14.70) 16.00 (15.30, 17.10) <0.001 Follow-up time, days 707.00 (129.00, 1,765.00) 719.50 (153.00, 1,714.00) 561.00 (111.00, 1,524.00) 362.00 (78.00, 1,086.00) <0.001 LOS, days 3.00 (2.00, 6.00) 3.00 (2.00, 6.00) 3.00 (2.00, 6.00) 4.00 (2.00, 8.00) <0.001 ASCVD, % 894(31) 926(33) 918(32) 834(29) 0.035 Hypertension, % 1,843(63) 1,768(62) 1,734(60) 1,536(54) <0.001 Hyperlipidemia, % 1,270(43) 1,209(43) 1,188(41) 1,143(40) 0.035 CKD, % 752(26) 886(31) 1,110(38) 1,312(46) <0.001 AF, % 234(8.0) 300(11) 374(13) 416(15) <0.001 Anemia , % 503(17) 583(21) 756(26) 1,173(41) <0.001 Hyperuricemia , % 105(4) 129(5) 194(7) 239(8) <0.001 Sex, (male) % 1,764(60) 1,518(53) 1,445(50) 1,340(47) <0.001 Age, year 63.00 (53.00, 72.00) 65.00 (55.00, 75.00) 66.00 (57.00, 76.00) 66.00 (56.00, 76.00) <0.001 Hematocrit, % 36.20 (32.70, 39.60) 35.80 (32.60, 39.30) 34.60 (31.10, 38.20) 31.90 (28.20, 35.80) <0.001 Platelet count, 10^9/L 226.00 (181.00, 280.00) 223.00 (177.00, 277.00) 221.00 (172.00, 280.00) 227.00 (166.00, 306.00) 0.013 Hemoglobin, g/dL 12.50 (11.20, 13.70) 12.00 (10.90, 13.20) 11.40 (10.30, 12.60) 10.20 (9.00, 11.50) <0.001 WBC, 10^9/L 8.60 (6.60, 11.10) 8.50 (6.50, 11.10) 8.30 (6.30, 11.10) 8.10 (6.00, 11.20) <0.001 MCH, pg 30.70 (29.70, 31.80) 30.00 (29.00, 31.20) 29.40 (28.10, 30.70) 28.10 (26.00, 30.20) <0.001 MCHC, g/L 344.0 (337.0, 353.0) 336.0 (329.0, 343.0) 329.0 (322.0, 337.0) 321.0 (312.0, 330.0) <0.001 MCV, fL 89.00 (86.00, 92.00) 89.00 (86.00, 93.00) 89.00 (85.00, 93.00) 88.00 (82.00, 93.00) <0.001 RBC, 10^12/L 4.04 (3.64, 4.45) 4.00 (3.60, 4.41) 3.89 (3.47, 4.33) 3.69 (3.20, 4.19) <0.001 Creatinine, mg/dL 0.90 (0.70, 1.10) 0.90 (0.80, 1.20) 1.00 (0.80, 1.40) 1.00 (0.80, 1.60) <0.001 Chloride, mEq/L 102.00 (99.00, 105.00) 103.00 (100.00, 106.00) 103.00 (100.00, 106.00) 103.00 (100.00, 106.00) <0.001 Potassium, mEq/L 4.10 (3.80, 4.40) 4.10 (3.80, 4.40) 4.10 (3.80, 4.50) 4.20 (3.80, 4.57) <0.001 Sodium, mEq/L 138.00 (135.23, 140.00) 139.00 (136.00, 141.00) 139.00 (136.00, 141.00) 138.65 (136.00, 141.00) <0.001 Anion gap,mEq/L 14.00 (12.00, 16.00) 14.00 (12.00, 16.00) 14.00 (12.00, 16.00) 14.00 (12.00, 17.00) <0.001 Bicarbonate, mEq/L 26.00 (23.00, 28.00) 26.00 (23.00, 28.00) 25.00 (23.00, 28.00) 25.00 (22.00, 27.00) <0.001 Magnesium, mg/dL 1.90 (1.70, 2.10) 1.90 (1.70, 2.10) 1.90 (1.70, 2.10) 1.90 (1.70, 2.10) 0.036 Phosphate, mg/dL 3.40 (2.90, 3.90) 3.40 (2.90, 4.00) 3.50 (2.90, 4.00) 3.50 (3.00, 4.10) <0.001 Calcium, mg/dL 8.90 (8.48, 9.30) 8.90 (8.40, 9.30) 8.80 (8.40, 9.20) 8.70 (8.20, 9.20) <0.001 #Continuous variables are expressed as interquartile ranges. Categorical variables are expressed as frequency (percentage). T2D-HF, type 2 diabetes - heart failure, ESI, Erythrocyte stress index, ePVS, estimated plasma volume statute, RDWCV, red blood cell distribution width coefficient of variation, LOS, length of stay, ASCVD, atherosclerotic cardiovascular disease, CKD, chronic kidney disease, AF, atrial fibrillation, WBC, white blood cell count, MCH, mean corpuscular hemoglobin, MCHC, mean corpuscular hemoglobin concentration, MCV, mean cell volume, RBC, red blood cell count. Table 2 . The Cox regression of the T2D-HF patients Cohorts Characteristic Model 1 Model 2 Model 3 Model 4 HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value TJHFIT ESI 1.25 (1.21, 1.30) <0.001 1.25 (1.21, 1.29) <0.001 1.24 (1.19, 1.28) <0.001 1.21 (1.17, 1.26) <0.001 ESIQ Q1 ref ref ref ref ref ref ref ref Q2 1.45 (1.18, 1.77) <0.001 1.40 (1.15, 1.72) 0.001 1.38 (1.13, 1.70) 0.002 1.38 (1.12, 1.69) 0.002 Q3 2.03 (1.67, 2.45) <0.001 1.94 (1.60, 2.36) <0.001 1.88 (1.55, 2.28) <0.001 1.80 (1.48, 2.19) <0.001 Q4 2.23 (1.85, 2.69) <0.001 2.13 (1.76, 2.57) <0.001 1.96 (1.61, 2.37) <0.001 1.81 (1.49, 2.20) <0.001 RDWCV 1.09 (1.07, 1.10) <0.001 1.09 (1.07, 1.10) <0.001 1.08 (1.06, 1.10) <0.001 1.07 (1.06, 1.09) <0.001 MCHC 0.99 (0.98, 0.99) <0.001 0.99 (0.98, 0.99) <0.001 0.99 (0.99, 0.99) <0.001 0.99 (0.99, 0.99) <0.001 ePVS 1.12 (1.09, 1.15) <0.001 1.13 (1.09, 1.16) <0.001 1.09 (1.05, 1.13) <0.001 1.05 (1.01, 1.09) 0.014 MIMIC ESI 1.38 (1.31, 1.46) <0.001 1.40 (1.32, 1.47) <0.001 1.30 (1.22, 1.38) <0.001 1.27 (1.20, 1.35) <0.001 ESIQ Q1 ref ref ref ref ref ref ref ref Q2 1.21 (1.08, 1.35) 0.001 1.14 (1.01, 1.27) 0.029 1.09 (0.97, 1.22) 0.15 1.09 (0.97, 1.23) 0.14 Q3 1.60 (1.44, 1.79) <0.001 1.47 (1.32, 1.65) <0.001 1.34 (1.20, 1.49) <0.001 1.34 (1.20, 1.50) <0.001 Q4 2.03 (1.82, 2.26) <0.001 1.98 (1.78, 2.22) <0.001 1.68 (1.49, 1.88) <0.001 1.63 (1.45, 1.83) <0.001 RDWCV 1.11 (1.09, 1.14) <0.001 1.11 (1.09, 1.14) <0.001 1.08 (1.06, 1.11) <0.001 1.07 (1.05, 1.10) <0.001 MCHC 0.99 (0.98, 0.99) <0.001 0.99 (0.98, 0.99) <0.001 0.99 (0.99, 0.99) <0.001 0.99 (0.99, 0.99) <0.001 ePVS 1.11 (1.09, 1.14) <0.001 1.11 (1.08, 1.13) <0.001 1.07 (1.04, 1.09) <0.001 1.05 (1.02, 1.07) <0.001 #In TJHFIT, Model 1: no covariate included, Model 2: age + sex, Model 3: Model 2 + AF + ASCVD + CKD + Hypertension + Hyperlipidemia+ Anemia+ Hyperuricemia+COPD, Model 4: Model 3 + MONO + BASO + EOS + ALB + APTT. #In MIMIC Model 1: no covariate included, Model 2: age + sex, Model 3: Model 2 + AF + Hypertension + CKD + Anemia + ASCVD, Model 4: Model 3 + ALB + Creatinine + Uric acid + INR + NEU. 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1","display":"","copyAsset":false,"role":"figure","size":162269,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe flow chart of this study\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7492293/v1/36e4a4170e234fd7360c8e27.jpg"},{"id":98441089,"identity":"8fe06918-4de8-4e3f-9154-8ac46a7fce1f","added_by":"auto","created_at":"2025-12-17 17:04:51","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":124480,"visible":true,"origin":"","legend":"\u003cp\u003eThe four subgroup Cox analysis in MIMIC and TJHFIT.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7492293/v1/5db4d5424226b821799d77e1.jpg"},{"id":98377895,"identity":"d9fc7b2b-aaaf-421c-ab2b-ae3dec9b4a43","added_by":"auto","created_at":"2025-12-17 07:19:11","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":62180,"visible":true,"origin":"","legend":"\u003cp\u003eThe RCS of ESI, ePVS, and RDWCV. A, The overall RCS plot of ESI. B, The overall RCS plot of ePVS. C, The overall RCS plot of RDWCV.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7492293/v1/a99c2314df441e973c40cd66.jpg"},{"id":98439393,"identity":"af3453e0-94dc-4f9e-8bd8-c99305f76300","added_by":"auto","created_at":"2025-12-17 17:01:46","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":106188,"visible":true,"origin":"","legend":"\u003cp\u003eKM curve for ESI and Cox analysis for patients with normal RDWCV and ePVS. A, The KM curve for ESI high and low group. B, The KM curve for ESIQ. C, Cox analysis for T2D patients with RDWCV \u0026lt; 13.92. D, Cox analysis for T2D patients with ePVS \u0026lt; 5.64. In C and D, the adjusted details for Model 1- 4 were Model 1: no covariate included, Model 2: age + sex, Model 3: Model 2 + AF + ASCVD + CKD + Hypertension + Hyperlipidemia + Anemia + Hyperuricemia, Model 4: Model 3 + Creatinine + Potassium + Sodium + WBC.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7492293/v1/32f78faced475a2d9e5b55a1.jpg"},{"id":102733315,"identity":"94dc2181-cf29-49b2-81c7-5c434baa908c","added_by":"auto","created_at":"2026-02-16 05:40:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1689048,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7492293/v1/6fb27f43-c3ca-439d-b650-b15ad8774771.pdf"},{"id":98441294,"identity":"e8a65565-42f0-470e-af48-6380705db6d0","added_by":"auto","created_at":"2025-12-17 17:05:09","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":136595,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementmaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7492293/v1/c6502858f604abbc496df800.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eErythrocyte Stress Index Promotes The Development of Type 2 Diabetes To Heart Failure: Result From Two Cohorts\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eType 2 Diabetes (T2D) is an independent risk factor \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e of heart failure (HF). T2D-induced erythrocyte dysfunction (e.g., decreased oxygen-carrying capacity) aggravates the clinical symptoms of HF \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Although HF shows myelosuppression \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e with erythrocyte dysfunction, few studies focus on whether T2D-induced erythrocyte dysfunction promotes the incidence of HF. Furthermore, erythrocytes will be a potential target for treating cardiovascular disease \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, and empagliflozin will increase erythropoiesis \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e and decrease the HF risk for T2D patients. So, erythrocyte is a bridge to explore the relationship between T2D and HF for the occurrence, development, and mechanism.\u003c/p\u003e \u003cp\u003eThere were two questions. Firstly, traditional indicators of erythrocytes can only describe either erythrocyte volume or hemoglobin, such as the red blood cell distribution width coefficient variable (RDWCV), lacking a measure that characterizes both simultaneously. Secondly, although numerous studies have examined erythrocyte indicators on the prognosis of T2D \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, HF \u003csup\u003e8 9\u003c/sup\u003e, and T2D-HF \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, few studies have focused on whether these indicators contribute to the progression of T2D to HF.\u003c/p\u003e \u003cp\u003eTo fill the gap of knowledge, we have coined the concept of \u0026ldquo;erythrocyte stress index\u0026rdquo; (ESI) for the first time in this study, bringing in a new method to simultaneously characterize both the erythrocyte volume standard deviation and the hemoglobin. We established an ESI calculation strategy and investigated the association between ESI and T2D-derived HF. Because both RDWCV \u003csup\u003e11 12\u003c/sup\u003e and estimated plasma volume statute (ePVS) \u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e have been verified for HF prognosis. So Both indicators were applied as a reference to evaluate the predictive value of ESI.\u003c/p\u003e \u003cp\u003eSpecifically, this study contained two cohorts and focuses on the development of HF from T2D: i) whether ESI is an independent marker that promotes the T2D patient's development into HF; ii) whether ESI predictive value is better than the RDWCV and ePVS; iii) when RDWCV and ePVS lose the predictive value, whether ESI still plays the role.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003eThe study included two prospective cohorts, the Medical Information Mart for Intensive Care (MIMIC) database, and the Tianjin HF of integrated treatment (TJHFIT).\u003c/p\u003e \u003cp\u003eMIMIC is free of charge until users pass a test to be approved by the MIMIC-IV database manager. The author (Dr. Lin Zhang) was supported in extracting data for the study (certification number record ID: 55394138). The participants were enrolled between January 1, 2008, and December 31, 2019.\u003c/p\u003e \u003cp\u003eTJHFIT was registered (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eChiCTR2300077220\u003c/span\u003e) which aims to explore the curative effects of combining traditional Chinese and Western medicine in treating HF in the Tianjin region. This study is a part of the above TJHFIT cohort. The participants were enrolled between January 1, 2006, and December 31, 2018, from 43 tertiary hospitals and 39 hospitals in Tianjin, China.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Definition of T2D and HF\u003c/h2\u003e \u003cp\u003eThe diagnosis of T2D depends on the international classification of diseases (ICD) codes and clinical features. 129 T2D-relevant ICD codes are listed in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e. The clinical features diagnosis of T2D included four criteria: i) patient's discharge diagnosis was T2D), ii) glycated hemoglobin A1c\u0026thinsp;\u0026ge;\u0026thinsp;6.5%, iii) fasting plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L, iv) 2-hour plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L during an oral glucose tolerance test.\u003c/p\u003e \u003cp\u003eThe diagnosis of HF relied on the 2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The diagnosis of HF involved ICD codes (\u003cb\u003eTable S2\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Calculation Strategy of ePVS and EFI\u003c/h2\u003e \u003cp\u003eAs the previous studies indicated \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, the ePVS (mL/g) = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{100-\\text{H}\\text{e}\\text{m}\\text{a}\\text{t}\\text{o}\\text{c}\\text{r}\\text{i}\\text{t}}{\\text{H}\\text{e}\\text{m}\\text{o}\\text{g}\\text{l}\\text{o}\\text{b}\\text{i}\\text{n}\\:(\\text{g}/\\text{d}\\text{L})}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe calculation strategy of the ESI is illustrated in the following formula. The ESI is defined as RDWCV divided by mean corpuscular hemoglobin concentration (MCHC). Firstly, RDWCV is determined as the standard deviation of erythrocyte volume (EVSD) divided by the mean corpuscular volume (MCV) \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Secondly, MCHC \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e is calculated as the MCH divided by the mean corpuscular volume (MCV). Subsequently, ESI is computed as the ratio of RDWCV to MCHC. Notably, as MCV is a component in both RDWCV and MCHC calculations, it effectively cancels out. Thus, ESI is ultimately equivalent to EVSD divided by MCH.\u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"609\" height=\"152\"\u003e\u003c/p\u003e\u003cp\u003eAccording to the formula, ESI can simultaneously characterize the erythrocyte volume standard deviation and the mean hemoglobin levels. An elevation in ESI indicates an increased erythrocyte volume standard deviation or a reduction in mean hemoglobin content, indicative of a metabolic disturbance in the erythrocyte.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Other clinical features\u003c/h2\u003e \u003cp\u003eThree types of clinical features were included, demographics, history of disease, and laboratory tests. The demographic variables consisted of age and sex. The history of disease encompassed hypertension, hyperlipemia, hypercholesterolemia, chronic kidney diseases (CKD), atrial fibrillation (AF), atherosclerotic cardiovascular disease (ASCVD), nicotine dependence, anemia, hyperuricemia, chronic obstructive pulmonary disease (COPD), and length of stay (LOS). The laboratory tests are composed of blood routine and biochemical tests.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Follow-up time\u003c/h2\u003e \u003cp\u003eAll included individuals were T2D patients. The end-point event was defined as HF. In MIMIC, the follow-up time was defined as the duration from T2D diagnosis to HF onset until December 31, 2022. In TJHFIT, the follow-up time was defined as the duration from T2D diagnosis to HF onset until December 31, 2023.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe analytical procedures were conducted using R (version 4.3.1). Covariates with missing data from over 20% of the population were excluded from the analysis. For covariates with missing values ranging between 0 and 19%, multiple interpolation was performed using the \u003cem\u003emice\u003c/em\u003e R package. Within the \u003cem\u003emice\u003c/em\u003e package, a seed value of 3 was specified, and \"pmm\" was employed as the imputation method.\u003c/p\u003e \u003cp\u003eWe analyzed the clinical features from the start of follow-up. With the basement of the Cox analysis, the hazard ratio (HR) was conducted in the T2D vs T2D-HF. Confidence intervals (CIs) were employed to illustrate the fluctuation of HR. To investigate the relationship between the ESI and T2D-HF, four models with adjusted parameters were established. Model 1: no covariates included; Model 2: age\u0026thinsp;+\u0026thinsp;sex; Model 3: Model 2\u0026thinsp;+\u0026thinsp;LOS\u0026thinsp;+\u0026thinsp;history of disease; Model 4: Model 3\u0026thinsp;+\u0026thinsp;laboratory indicators with differences.\u003c/p\u003e \u003cp\u003eTo investigate the influence of ESI variation, ESI quartile stratification was employed. In TJHFITM, ESI (mL/g) quartiles (ESIQ) were defined as follows: Q1: [2.89,3.67], Q2: (3.67,3.89], Q3: (3.89,4.22], and Q4: (4.22,13.89]. In MIMIC, ESI (mL/g) quartiles (ESIQ) were defined as follows: Q1: [3.13,3.9], Q2: (3.91,4.16], Q3: (4.17,4.55], and Q4: (4.56,10.46].\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003ch2\u003e3.1 Baseline characteristics\u003c/h2\u003e\n\u003cp\u003eApplying the inclusion and exclusion criteria resulted in the enrollment. Among the 15,813 T2D patients with full follow-up time (the mean of TJHFIT is 1,532 days and MIMIC is 959 days), 3,711 developed HF, and 12,102 did not. Specifically, 11,547 T2D patients (\u003cstrong\u003eFig. 1\u003c/strong\u003e) in MIMIC included 2,710 T2D patients who developed HF and the other 8,837 did not. In TJHFIT, 4,266 T2D patients were included 1,001 developed HF, and the other 3,265 T2D patients did not.\u003c/p\u003e\n\u003cp\u003eThe baseline characteristics of MIMIC in the 11,547 T2D patients were summarized in \u003cstrong\u003eTable S3\u003c/strong\u003e. ESI was significantly higher in the T2D-HF group (median, 4.24 mL/g) than in the T2D group (median, 4.14 mL/g) (P \u0026lt; 0.001). The T2D-HF group had a higher ePVS(median, 5.86 mL/g), and RDWCV (median,14.10 %) than the T2D group(\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). However, there was no significant difference in MCH, MCV, and MCHC between the two groups. Among the 4,266 T2D in TJHFIT (\u003cstrong\u003eTable S4\u003c/strong\u003e), ESI was significantly higher in the T2D-HF group (median, 4.12 mL/g) than in the T2D group (median, 3.83 mL/g) (P \u0026lt; 0.001). Among the other clinical characteristics, the T2D-HF patients had higher ePVS (median, 4.80 mL/g), RDWCV (median, 13.40 %), MCV (median, 90.90 fL) than T2D, and had lower MCH (median, 29.80 pg), MCHC (median, 329.61 g/dL) than T2D group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo better illustrate the ESI influence on T2D-HF, the ESIQ was also analyzed. In MIMIC, the (\u003cstrong\u003eTable 1\u003c/strong\u003e) T2D-HF incidence of ESIQ (Q1-Q4) was 19%, 22%, 26%, and 27%, respectively. The HF incidence in the highest quartile (Q4) of ESI was 1.42 greater than in the lowest quartile (Q1). TJHFIT repeated a similar trend (\u003cstrong\u003eTable S5)\u003c/strong\u003e, the T2D-HF incidence of ESIQ was 15%, 21%, 27%, and 31%, respectively (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). Among the two cohorts, the mean prevalence of HF in ESIQ was 17%, 22%, 27%, and 29%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Baseline characteristics for EFIQ in MIMIC\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCox\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;regression\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e3.2.1 T2D vs T2D-HF\u003c/h2\u003e\n\u003cp\u003eIn TJHFIT, the univariate and adjusted Cox regression analyses (\u003cstrong\u003eTable 2\u003c/strong\u003e) indicated the HRs of ESI (95% CIs) of the four models were as follows: 1.25 (1.21, 1.30), 1.25 (1.21, 1.29), 1.24 (1.19, 1.28), and 1.21 (1.17, 1.26). As for the RDWCV, the HRs (95% CIs) for the 4 models were only 1.09 (1.07, 1.10), 1.09 (1.07, 1.10), 1.08 (1.06, 1.10), and 1.07 (1.06, 1.09) respectively (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). In addition, the HRs (95% CIs) of ePVS for the 4 models were only 1.12 (1.09, 1.15), 1.13 (1.09, 1.16), 1.09 (1.05, 1.13), and 1.05 (1.01, 1.09), respectively (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). When setting Q1 as the reference, the HRs of Model 4 for Q2, Q3, and Q4 became 1.38 (1.12, 1.69), 1.80 (1.48, 2.19), 1.81 (1.49, 2.20), respectively (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01), indicating that T2D patients with higher ESI were more prone to HF. Furthermore, the HR values of the four models for MCHC were lower than 1. The MIMIC repeated a similar trend (\u003cstrong\u003eTable 2\u003c/strong\u003e). In particular, both cohorts indicated the HRs of ESI in each model were much higher than the RDWCV and ePVS, indicating that ESI exhibited better predictive performance than the RDWCV and ePVS.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. The Cox regression of the T2D-HF patients.\u003c/p\u003e\n\u003ch2\u003e3.2.2 Subgroup analysis\u003c/h2\u003e\n\u003cp\u003eNine subgroups of Cox analysis were processed, sex, year \u0026gt; 60 or not, AF, Anemia, ASCVD, CKD, Hyperlipidemia, Hypertension, and Hyperuricemia. To eliminate the effect of covariates, four models described above were used for adjustment, and the results of ESI, ESIQ, ePVS, RDWCV, and MCHC were detailed (\u003cstrong\u003eTable S6-S7\u003c/strong\u003e). In all subgroups, the ESI had the highest HR than RDWCV and ePVS.\u003c/p\u003e\n\u003cp\u003eAmong the nine subgroups, four were consistent between the TJHFIT and MIMIC, including sex, AF, Anemia, and CKD (\u003cstrong\u003eFig 2\u003c/strong\u003e), the interaction p-value was not all \u0026lt; 0.05. Especially, the ESI’s result with Model 4 adjusted was summarised (\u003cstrong\u003eFig 2\u003c/strong\u003e). Only the interaction for the anemia subgroup was significant in both MIMIC and TJHFIT. This indicated when ESI increased, the non-anemia patients had a higher HR than anemia patients.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e3.3 RCS analysis\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eRCS analysis was applied to optimize the cut-off value for T2D-HF (\u003cstrong\u003eFig. 3\u003c/strong\u003e) in ESI, ePVS, and RDWCV. RCS plots of ESI, ePVS, and RDWCV were not linear and had an S-like wave. According to the density of (\u003cstrong\u003eFig. 3 A\u003c/strong\u003e), a majority of patients distribute between 3 and 5 in ESI. And increasing ESI indicated an increasing HR for T2D-HF. The 4.19 is the closest value of HR to 1 for ESI. Additionally, the RCS indicated the threshold for HR value equal to 1 for ESI is 4.19 mL/g(\u003cstrong\u003eFig. 3 A\u003c/strong\u003e). Similarly, the cut-off values of ePVS and RDWCV were finished, 5.64 mL/g and 13.92%, respectively (\u003cstrong\u003eFig. 3 B-C\u003c/strong\u003e).\u003c/p\u003e\n\u003ch2\u003e3.4 Analysis for patients with lower RDWCV and ePVS\u003c/h2\u003e\n\u003cp\u003eAccording to the cut-off value from the above RCS analysis, individuals were classified into a high ESI group (ESI \u0026gt; 4.19 mL/g) or a low ESI group (ESI ≤ 4.19 mL/g). In T2D patients (\u003cstrong\u003eFig. 4\u003c/strong\u003e), the high ESI group had a higher probability of HF than the low group (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001).In T2D patients (\u003cstrong\u003eFig. 4 A\u003c/strong\u003e), the high ESI group had a higher probability of HF than the low group (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). Similarly in the ESIQ (\u003cstrong\u003eFig. 4 B\u003c/strong\u003e), T2D patients with higher quartiles of ESI had a higher probability of HF (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo evaluate the predictive value in lower RDWCV and ePVS groups, the Cox analysis was finished in the patients with RDWCV \u0026lt; 13.92 or ePVS \u0026lt; 5.64. In the patients with RDWCV \u0026lt; 13.92 (\u003cstrong\u003eFig. 4 C\u003c/strong\u003e), even though the RDWCV shows a worse HR value, ESI indicated a significant HR, and the HR value and 95%CI in Model 4 was 1.58 (1.19, 2.08). Similarly, in the patients with ePVS \u0026lt; 5.64 (\u003cstrong\u003eFig. 4 D\u003c/strong\u003e), even though ePVS indicated a worse predictive value, both ESI and RDWCV indicated a higher HR, especially ESI with HR value of 95%CI in Model 4 was 1.48 (1.29, 1.69).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAmong 15,813 T2D patients, 3,711 finally developed into HF. The multiple Cox regression indicated that ESI, RDWCV, and ePVS were independent risk factors for T2D-HF. ESI had a higher HR than both RDWCV and ePVS. Subgroupp indicated that no-anemia had higher ESI HR than anemia patients. RCS had shown the cutoff values for EFI, ePVS, and RDWCV were 4.19 mL/g, 5.64 mL/g, and 13.92%, respectively. When T2D patients with RDWCV\u0026thinsp;\u0026lt;\u0026thinsp;13.92% or ePVS\u0026thinsp;\u0026lt;\u0026thinsp;5.64 mL/g and both indicators lose the predictive value, the ESI could still predict the T2D-HF. High ESI (\u0026gt;\u0026thinsp;4.19 mL/g) T2D patients had a higher HF probability than the low ESI group (\u0026le;\u0026thinsp;4.19 mL/g).\u003c/p\u003e \u003cp\u003eWe validated that ESI was an independent risk factor for T2D patients who developed HF (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Moreover, with the increase in ESI, especially when ESI\u0026thinsp;\u0026gt;\u0026thinsp;4.19 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), the incidence of HF significantly increased. ESI represents novel evidence of erythrocyte dysfunction in T2D-HF. The traditional understanding of erythrocyte dysfunction in T2D has predominantly focused on erythrocyte volume parameters such as RDWCV \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e and MCV \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, as well as glycosylated hemoglobin levels \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, rather than other hemoglobin indices. However, as a comprehensive measure integrating erythrocyte volume and hemoglobin, ESI demonstrates superior utility in assessing T2D-derived HF. ESI offers novel insights into the physiological and pathological mechanisms underlying T2D-HF, thereby potentially informing the development of therapeutic strategies.\u003c/p\u003e \u003cp\u003eSubgroup analysis in this study showed that with an increase in ESI, the HR non-anemia was higher compared to anemia T2D patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Therefore, ESI has significant potential in the classified management of the aforementioned subgroup. For instance, RDWCV \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e and MCHC \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e are served in the diagnosis of anemia subtype. Because ESI is derived from RDWCV and MCHC, it may hold promise in diseases associated with anemia. Additionally, ESI in the current study outcompetes RDWCV in predicting incidence \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e and prognosis \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e in HF. However, further research is needed to explore its role as an epidemiological and prognostic marker.\u003c/p\u003e \u003cp\u003eCompared with RDWCV and ePVS, the ESI had a higher HR value for T2D-HF. More importantly, ESI still had the prognosis potential when RDWCV and ePVS lost their predictive value. Previous research indicated patients with ePVS\u0026thinsp;\u0026gt;\u0026thinsp;5.5 mL/g \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e might be in a state of plasma capacity overload. In our research, the T2D patients with ePVS\u0026thinsp;\u0026lt;\u0026thinsp;5.64 mL/g had a decreased plasma capacity than the \u0026gt;\u0026thinsp;5.64 mL/g patients. Importantly, ESI still had predictive value, especially in plasma capacity-decreased T2D patients. T2D patients with RDWCV\u0026thinsp;\u0026lt;\u0026thinsp;13.92% indicated normal changes in red cell volume (normal individuals were no more than 14%), and ESI still had predictive potential even though RDWCV lost the forecast value. So, ESI might still have a predictive value in T2D patients with normal plasma capacity and normal red cell volume changes.\u003c/p\u003e \u003cp\u003eCompared to other emerging indicators, ESI possesses three distinctive characteristics: biological significance, cost-effectiveness for patients, and simplicity in clinical application. Another novel index associated with RDWCV, the RDWCV serum albumin ratio (RAR) \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, has garnered increased attention in recent years. While RAR demonstrates statistical significance, its underlying biological significance warrants further investigation. RDWCV reflects erythrocyte volume changes, while serum albumin primarily indicates nutritional status \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, raising questions about the biological relevance of RAR. Additionally, RDWCV \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e belongs to hematology, whereas serum albumin originates in biochemistry, potentially increasing financial burdens on patients. Both RDWCV and MCHC, derived from hematology, offer potential cost-saving benefits for patients. In contrast to costly genetic tests (e.g., dilated cardiomyopathy \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e and acute myocardial infarction \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, ESI can be readily obtained in clinical settings and is more operationally feasible.\u003c/p\u003e \u003cp\u003eThis study has highlighted these innovative points: i) ESI serves as an independent epidemic marker in T2D-developed HF with a cut-off value of 4.19 mL/g; ii) the predictive value of ESI is much higher than RDWCV and ePVS; iii) when RDWCV and ePVS lose the predictive value, ESI still plays the role. This study possesses three notable strengths: i) two queue mutual authentication, ii) explores T2D evolved into the HF rather than HF mortality or prognosis, providing a reference for the primary prevention of HF, and iii) various characteristics were included for model adjustment and robust results.\u003c/p\u003e \u003cp\u003eHowever, as previously acknowledged \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, two limitations merit consideration. Firstly, ESI measurements were conducted only at a single time point, potentially leading to misclassification. However, any misclassification would likely result in underestimation rather than overestimation due to the influence of regression dilution bias \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Lastly, we did not finetune the Cox analysis according to medication history. However, many lab tests and history of diseases were already included for adjustment which could substitute the deficiency of medication history.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eESI promotes T2D developed to HF and was an independent epidemic marker of T2D-derived HF. The ESI had a better predictive value than RDWCV and ePVS. When T2D patients with lower RDWCV or ePVS and both loss forecasting values, ESI can still predict the T2D-HF. ESI might offer a novel sight for T2D-HF prevention and treatment. The value of ESI in other diseases is unknown and deserves further exploration.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThanks to Jie Li and Jie Liu for putting forward the idea of the research, Lin Zhang and Jingyi Lin provided the original data and ethics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTYG and JL wrote the initial draft. LZ and JYL provided the original data and obtained ethical approval. LZ was responsible for the methodology. XQO, YZ, and YW analyzed and organized the data. JL and JL managed project administration and revised the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn TJHFIT, the ethics committee of First Teaching Hospital of Tianjin University of Traditional Chinese Medicine passed the ethics approval (\u003cstrong\u003eTYLL2023[K]\u003c/strong\u003e). The research has been registered (\u003cstrong\u003e\u003cu\u003eChiCTR2300077220\u003c/u\u003e\u003c/strong\u003e). In MIMIC, the author (Dr. Lin Zhang) was supported in extracting data from this database for the study (certification number record ID: \u003cstrong\u003e\u003cu\u003e55394138\u003c/u\u003e\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors meet the criteria for authorship as recommended by the International Committee of Medical Journal Editors and did not receive payment related to the development of this manuscript. All authors declares no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary data from MIMIC can be obtained from the link (\u003cstrong\u003ehttps://mimic.mit.edu/\u003c/strong\u003e). TJFHIT is not in a public database but the primary data can be obtained by contacting the authors.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDal Canto, E. et al. Diabetes as a cardiovascular risk factor: An overview of global trends of macro and micro vascular complications. \u003cem\u003eEur J. Prev. Cardiol Dec.\u003c/em\u003e \u003cb\u003e26\u003c/b\u003e (2_suppl), 25\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/2047487319878371\u003c/span\u003e\u003cspan address=\"10.1177/2047487319878371\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, K. S. et al. 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Fasting triglycerides are positively associated with cardiovascular mortality risk in people with diabetes. \u003cem\u003eCardiovasc Res May\u003c/em\u003e. \u003cb\u003e2\u003c/b\u003e (3), 826\u0026ndash;834. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/cvr/cvac124\u003c/span\u003e\u003cspan address=\"10.1093/cvr/cvac124\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Baseline characteristics for ESIQ in MIMIC\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"91%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1, N = 2,931 \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ2, N = 2,842\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3, N = 2,913\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ4, N = 2,861 \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT2D-HF, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e550(19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e632(22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e764(26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e764(27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eESI, mL/g\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e3.75 (3.64, 3.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e4.04 (3.97, 4.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e4.33 (4.24, 4.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e4.94 (4.71, 5.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eePVS, mL/g\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e5.11 (4.42, 5.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e5.33 (4.61, 6.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e5.75 (4.90, 6.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e6.65 (5.59, 8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRDWCV, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e12.90 (12.50, 13.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e13.60 (13.20, 13.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e14.30 (13.90, 14.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e16.00 (15.30, 17.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFollow-up time, days\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e707.00 (129.00, 1,765.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e719.50 (153.00, 1,714.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e561.00 (111.00, 1,524.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e362.00 (78.00, 1,086.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLOS, days\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e3.00 (2.00, 6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e3.00 (2.00, 6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e3.00 (2.00, 6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e4.00 (2.00, 8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eASCVD, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e894(31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e926(33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e918(32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e834(29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1,843(63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1,768(62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1,734(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1,536(54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHyperlipidemia, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1,270(43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1,209(43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1,188(41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1,143(40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCKD, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e752(26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e886(31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1,110(38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1,312(46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAF, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e234(8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e300(11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e374(13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e416(15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnemia\u003c/strong\u003e\u003cstrong\u003e, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e503(17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e583(21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e756(26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1,173(41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHyperuricemia\u003c/strong\u003e\u003cstrong\u003e, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e105(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e129(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e194(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e239(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex, (male) %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1,764(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1,518(53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1,445(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1,340(47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, year\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e63.00 (53.00, 72.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e65.00 (55.00, 75.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e66.00 (57.00, 76.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e66.00 (56.00, 76.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHematocrit, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e36.20 (32.70, 39.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e35.80 (32.60, 39.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e34.60 (31.10, 38.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e31.90 (28.20, 35.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatelet count, 10^9/L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e226.00 (181.00, 280.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e223.00 (177.00, 277.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e221.00 (172.00, 280.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e227.00 (166.00, 306.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHemoglobin, g/dL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e12.50 (11.20, 13.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e12.00 (10.90, 13.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e11.40 (10.30, 12.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e10.20 (9.00, 11.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWBC, 10^9/L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e8.60 (6.60, 11.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e8.50 (6.50, 11.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e8.30 (6.30, 11.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e8.10 (6.00, 11.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMCH, pg\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e30.70 (29.70, 31.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e30.00 (29.00, 31.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e29.40 (28.10, 30.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e28.10 (26.00, 30.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMCHC, g/L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e344.0 (337.0, 353.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e336.0 (329.0, 343.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e329.0 (322.0, 337.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e321.0 (312.0, 330.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMCV, fL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e89.00 (86.00, 92.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e89.00 (86.00, 93.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e89.00 (85.00, 93.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e88.00 (82.00, 93.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRBC, 10^12/L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e4.04 (3.64, 4.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e4.00 (3.60, 4.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e3.89 (3.47, 4.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e3.69 (3.20, 4.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCreatinine, mg/dL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.90 (0.70, 1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.90 (0.80, 1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1.00 (0.80, 1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1.00 (0.80, 1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChloride, mEq/L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e102.00 (99.00, 105.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e103.00 (100.00, 106.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e103.00 (100.00, 106.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e103.00 (100.00, 106.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePotassium, mEq/L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e4.10 (3.80, 4.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e4.10 (3.80, 4.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e4.10 (3.80, 4.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e4.20 (3.80, 4.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSodium, mEq/L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e138.00 (135.23, 140.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e139.00 (136.00, 141.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e139.00 (136.00, 141.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e138.65 (136.00, 141.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnion gap,mEq/L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e14.00 (12.00, 16.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e14.00 (12.00, 16.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e14.00 (12.00, 16.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e14.00 (12.00, 17.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBicarbonate, mEq/L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e26.00 (23.00, 28.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e26.00 (23.00, 28.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e25.00 (23.00, 28.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e25.00 (22.00, 27.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMagnesium, mg/dL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1.90 (1.70, 2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1.90 (1.70, 2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1.90 (1.70, 2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1.90 (1.70, 2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhosphate, mg/dL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e3.40 (2.90, 3.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e3.40 (2.90, 4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e3.50 (2.90, 4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e3.50 (3.00, 4.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCalcium, mg/dL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e8.90 (8.48, 9.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e8.90 (8.40, 9.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e8.80 (8.40, 9.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e8.70 (8.20, 9.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e#Continuous variables are expressed as interquartile ranges. Categorical variables are expressed as frequency (percentage). T2D-HF, type 2 diabetes - heart failure, ESI, Erythrocyte stress index, ePVS, estimated plasma volume statute, RDWCV, red blood cell distribution width coefficient of variation, LOS, length of stay, ASCVD, atherosclerotic cardiovascular disease, CKD, chronic kidney disease, AF, atrial fibrillation, WBC, white blood cell count, MCH, mean corpuscular hemoglobin, MCHC, mean corpuscular hemoglobin concentration, MCV, mean cell volume, RBC, red blood cell count.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. The Cox regression of the T2D-HF patients\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCohorts\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTJHFIT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eESI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.25 (1.21, 1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.25 (1.21, 1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.24 (1.19, 1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.21 (1.17, 1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eESIQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;Q1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;Q2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.45 (1.18, 1.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.40 (1.15, 1.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.38 (1.13, 1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.38 (1.12, 1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;Q3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e2.03 (1.67, 2.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.94 (1.60, 2.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.88 (1.55, 2.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.80 (1.48, 2.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;Q4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e2.23 (1.85, 2.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e2.13 (1.76, 2.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.96 (1.61, 2.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.81 (1.49, 2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eRDWCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.09 (1.07, 1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.09 (1.07, 1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.08 (1.06, 1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.07 (1.06, 1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eMCHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.99 (0.98, 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.99 (0.98, 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.99 (0.99, 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.99 (0.99, 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eePVS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.12 (1.09, 1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.13 (1.09, 1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.09 (1.05, 1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.05 (1.01, 1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMIMIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eESI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.38 (1.31, 1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.40 (1.32, 1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.30 (1.22, 1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.27 (1.20, 1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eESIQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;Q1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;Q2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.21 (1.08, 1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.14 (1.01, 1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.09 (0.97, 1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.09 (0.97, 1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;Q3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.60 (1.44, 1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.47 (1.32, 1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.34 (1.20, 1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.34 (1.20, 1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;Q4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e2.03 (1.82, 2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.98 (1.78, 2.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.68 (1.49, 1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.63 (1.45, 1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eRDWCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.11 (1.09, 1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.11 (1.09, 1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.08 (1.06, 1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.07 (1.05, 1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eMCHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.99 (0.98, 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.99 (0.98, 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.99 (0.99, 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.99 (0.99, 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eePVS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.11 (1.09, 1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.11 (1.08, 1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.07 (1.04, 1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.05 (1.02, 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e#In TJHFIT, Model 1: no covariate included, Model 2: age + sex, Model 3: Model 2 + AF + ASCVD + CKD + Hypertension + Hyperlipidemia+ Anemia+ Hyperuricemia+COPD, Model 4: Model 3 + MONO + BASO + EOS + ALB + APTT.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e#In MIMIC Model 1: no covariate included, Model 2: age + sex, Model 3: Model 2 + AF + Hypertension + CKD + Anemia + ASCVD, Model 4: Model 3 + ALB + Creatinine + Uric acid + INR + NEU.\u003c/p\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":"Erythrocyte stress index, Indicator, Type 2 diabetes, Heart Failure","lastPublishedDoi":"10.21203/rs.3.rs-7492293/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7492293/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eAim:\u003c/strong\u003eTo assess whether the erythrocyte stress index (ESI), a novel indicator to characterize erythrocytes, promotes the type 2 diabetes (T2D) patients developed into heart failure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eTwo cohorts were contained, the Medical Information Mart for Intensive Care (MIMIC) database, and the Tianjin HF with Integrated Treatment (TJHFIT). Among the 15,813 T2D patients with follow-up time (mean of 1,532 days and 959 days in the two cohorts), 3,711 finally developed HF. ESI was compared with two indicators associated with HF onset: red blood cell distribution width coefficient of variation (RDWCV) and estimated plasma volume status (ePVS). ESI was stratification with quartiles (ESIQ). The Cox analysis, restricted cubic spline (RCS), and Kaplan–Meier (KM) curves were applied.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The incidence of HF in T2D patients across ESI quartiles was 17%, 22%, 27%, and 29%, respectively. In MIMIC, the adjusted hazard ratios (aHR) with 95% confidence interval (CI) of ESI, RDWCV, and ePVS were 1.27 (1.20, 1.35), 1.07 (1.05, 1.10), and 1.05 (1.02, 1.07), the ESI had the highest aHR. Restricted cubic splines demonstrated that ESI, RDWCV, and ePVS exhibited an S-shaped non-linear relationship, with cut-off values of 4.19 mL/g for ESI. Kaplan-Meier curves indicated that T2D patients with ESI \u0026gt;4.19 mL/g had a higher probability of HF than those with ESI ≤ 4.19mL/g. Notably, ESI remained a significant predictor of HF even in T2D patients with RDWCV, and ePVS lost their predictive value.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: The ESI served as an independent prognostic marker for the development of HF in patients with T2D. Even though both RDWCV and ePVS lost their predictive value, ESI can still play its role.\u003c/p\u003e","manuscriptTitle":"Erythrocyte Stress Index Promotes The Development of Type 2 Diabetes To Heart Failure: Result From Two Cohorts","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-17 07:19:06","doi":"10.21203/rs.3.rs-7492293/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":"084caf87-b1f5-4156-9f69-6859add6830a","owner":[],"postedDate":"December 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59765295,"name":"Health sciences/Cardiology"},{"id":59765296,"name":"Health sciences/Diseases"},{"id":59765297,"name":"Health sciences/Endocrinology"},{"id":59765298,"name":"Health sciences/Medical research"},{"id":59765299,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-02-16T05:38:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-17 07:19:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7492293","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7492293","identity":"rs-7492293","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}