Combined hemoglobin glycation index and RDW/LDL-C ratio predict severe sepsis and/or septic shock in diabetic patients with Enterobacteriaceae bloodstream infections

Combined hemoglobin glycation index and RDW/LDL-C ratio predict severe sepsis and/or septic shock in diabetic patients with Enterobacteriaceae bloodstream infections | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Combined hemoglobin glycation index and RDW/LDL-C ratio predict severe sepsis and/or septic shock in diabetic patients with Enterobacteriaceae bloodstream infections Peng Zhou, Fenfen Xu, Qiaofei Zheng, Shixiao Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8876408/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Objective To investigate the predictive value of hemoglobin glycation index (HGI) and red cell distribution width/low-density lipoprotein cholesterol ratio (RDW/LDL-C) for progression to severe sepsis and/or septic shock following Enterobacteriaceae bloodstream infection in diabetic patients. Methods This retrospective study included 203 diabetic patients with concomitant Enterobacteriaceae bloodstream infections. Clinical data were collected, and the HGI and RDW/LDL-C were calculated. Patients were grouped into quartiles (Q1-Q4) based on combined indicator quartiles. Multivariate logistic regression analysis was performed to assess the association with severe infection risk, and predictive performance was evaluated using ROC curves. Results Patients in the severe group exhibited significantly higher HGI and RDW/LDL-C levels compared to the non-severe group (both p < 0.05). Multivariate analysis showed that after adjusting for source of infection (Model 1) and comorbidities (Model 2), with Q1 as the reference group, Q2 and Q4 were independent predictors of severe sepsis and/or septic shock, respectively. ROC curve analysis indicated that the combined HGI-RDW/LDL-C index had an area under the curve (AUC) of 0.760 (95% CI: 0.693–0.826) for predicting severe sepsis and/or septic shock. Furthermore, the proportion of Klebsiella pneumoniae infections and antibiotic resistance patterns exhibited specific distributions within the high combined index group. Conclusion The HGI-RDW/LDL-C composite index serves as an independent risk factor for progression to severe sepsis and/or septic shock following Enterobacteriaceae bloodstream infection in diabetic patients, demonstrating good predictive value. Type 2 diabetes mellitus Enterobacteriaceae Bloodstream infections HGI-RDW/LDL-C Figures Figure 1 Figure 2 Figure 3 Introduction Patients with diabetes may have a higher risk of bloodstream infections, typically exhibiting poorer clinical outcomes, longer hospital stays, and a significantly increased risk of mortality [ 1 , 2 ] . Diabetic patients experience impaired immune function, microcirculatory disorders, and reduced tissue repair capacity due to prolonged hyperglycemia. This puts patients particularly vulnerable to infections, leading to acute systemic infections that may advance to sepsis and multiple organ failure. Research demonstrates that individuals with diabetes possess a 2 to 6-fold increased risk of acquiring bloodstream infections relative to non-diabetic individuals [ 3 ] , and the mortality rate for severe sepsis ranges from approximately 10% to 40% [ 4 , 5 ] . Research conducted by Hernando Gómez et al. demonstrated that hospitalized patients with type 2 diabetes and sepsis who are administered metformin experience decreased mortality rates within 90 days [ 6 ] . In recent years, the increasing prevalence of diabetes has rendered diabetic patients with concurrent bloodstream infections a significant focus and problem in clinical management. Blood glucose control serves as a core indicator in diabetes management. HbA1c is a metabolic byproduct resulting from the attachment of glucose to hemoglobin in the body. It accurately reflects a diabetic patient's blood glucose control over the preceding 2 to 3 months [ 7 ] . The hemoglobin glycation index (HGI) is a novel metric proposed by Hempe et al . in 2002 to quantify variations in glycated hemoglobin levels among individual patients [ 8 ] . He et al analyzed the trajectory of the dynamic HGI and its impact on survival in patients with sepsis. Trajectory analysis indicated that patients in the rising trajectory group faced markedly higher death risks at both 28 days and 90 days compared to the stable group, whereas patients in the descending trajectory group demonstrated significantly lower mortality risks [ 9 ] . HGI was independently associated with the risk of sepsis-related encephalopathy and short-term prognosis, serving as a biomarker for prognostic risk stratification [ 10 ] . Red blood cell distribution width (RDW) reflects red blood cell volume heterogeneity. Elevated RDW was associated with acute or chronic heart failure, acute pancreatitis, sepsis, acute pulmonary embolism, and acute kidney injury [ 11 ] . Elevated RDW was independently associated with increased short-term and long-term all-cause mortality in patients with acute pancreatitis and sepsis [ 12 ] . Studies suggested that RDW-CV may serve as a simple and useful biomarker for detecting tubular injury in patients with type 2 diabetes [ 13 ] . Additionally, most diabetic patients exhibit dyslipidemia. Low-density lipoprotein cholesterol (LDL-C) is a lipoprotein that transports cholesterol. Studies indicated that LDL-C levels were closely associated with cardiovascular risk in diabetic patients [ 14 ] . Feng et al. demonstrated that among hospitalized patients with infections, lower measured LDL-C levels were significantly associated with increased risks of sepsis and ICU admission [ 15 ] . Currently, most relevant studies focus on comparing the epidemiology of bloodstream infections between diabetic and non-diabetic patients. However, in-depth exploration of the association between different metabolic levels and the prognosis of bloodstream infections caused by various pathogens in diabetic patients remains limited. Therefore, this study aims to systematically analyze the relationship between blood glucose, inflammation-metabolism level and the severity of clinical outcomes in diabetic patients following Enterobacteriaceae bloodstream infections. This analysis seeks to provide evidence-based support for optimizing glucose and lipid management strategies and improving clinical outcomes in diabetic patient population. Materials and methods Study population This study enrolled 203 patients with type 2 diabetes diagnosed with K. pneumoniae or E. coli bloodstream infections admitted to Taizhou Hospital in Zhejiang Province from January 2023 to October 2025 through the departments of Emergency Medicine, Infectious Diseases, Endocrinology, and Critical Care Medicine. Inclusion Criteria: (1) Patients with diabetes must meet satisfy the diagnostic criteria specified in the Chinese Guidelines for the Prevention and Treatment of Type 2 Diabetes (2020 Edition), with comprehensive admission blood glucose records and regular blood glucose monitoring records during hospitalization. (2) Fulfilled the diagnostic criteria for bloodstream infection: a. At least one blood culture with detection of the corresponding pathogen; b. Clinical evidence of infection ( e.g. , fever > 38°C, chills, or hypotension). Exclusion criteria: Age < 18 years old, pregnant women, incomplete case records, transfer to another hospital during the study period. The definitions of sepsis and septic shock were based on the Third International Consensus Definition of Sepsis and septic shock (Sepsis-3) [ 16 ] . Sepsis accompanied by hypotension, hypoperfusion, or organ failure was designated as severe sepsis [ 17 ] . We categorized the participants into two categories based on clinical diagnostic information and patient severity: the severe sepsis and/or septic shock group and the non-severe sepsis and/or septic shock group. The study was approved by the Helsinki ethics committee at our institution. Microbiological methods All specimen collection and testing were conducted in accordance with the Chinese Guidelines for Clinical Laboratory Operations. Two separate sets of blood cultures were routinely collected, with 10 mL of blood collected into one aerobic and one anaerobic bottle. Blood cultures were incubated in the BacT/Alert system (bioMérieux, France) for a duration of 5 days. The isolates were identified as K. pneumoniae and E. coli using MALDI-TOF mass spectrometry (bioMérieux, France). Antibiotic susceptibility was tested using the VITEK 2 compact system (bioMérieux Vitek Inc., France) with microbroth dilution. Partial susceptibility tests were performed by the disk diffusion method on Mueller-Hinton agar. Antibiotic susceptibility results were interpreted according to Clinical and Laboratory Standards Institute (CLSI) guidelines. E. coli ATCC 25922 and K. pneumoniae ATCC 700603 served as quality control strains. Data Collection Review the electronic medical record system to collect and administer all patient information. Recorded clinical information includes gender, age, underlying conditions, primary infection site, clinical scores, comorbidities, laboratory test results, microbiological data, medication history, and clinical outcomes. Laboratory results included all blood parameters measured concurrently with the collection of the patient's first positive blood culture after admission, encompassing procalcitonin, C-reactive protein, lactate, oxygenation index, complete blood count, biochemical profile, coagulation function, and other relevant diagnostic indicators. Using fasting plasma glucose (FPG) values as the independent variable and measured HbA1c values as the dependent variable for all patients, we assessed the linear relationship between the two. A linear regression equation was then established to calculate predicted HbA1c values (Predicted HbA1c = 7.969 + 0.008 × FPG). and calculated the HGI value (HGI = measured HbA1c value - predicted HbA1c value) [ 18 ] . Patients were divided into four quartile groups based on the combination of HGI and RDW/LDL-C: Q1 (low HGI and low RDW/LDL-C), Q2 (low HGI and high RDW/LDL-C), Q3 (high HGI and low RDW/LDL-C), and Q4 (high HGI and high RDW/LDL-C). Statistical analysis This study employed SPSS 23.0 (SPSS Inc., Chicago, IL, USA) for statistical analysis. Graphs and charts were generated using R software. Non-normally distributed variables were expressed as median (interquartile range) [M (P25, P75)], with intergroup comparisons performed using the Mann-Whitney U test. Categorical variables were characterized by frequencies and percentages, with intergroup comparisons using the chi-square (χ²) test or Fisher's exact test. Factors with statistically significant differences in univariate analysis were incorporated into a multivariate logistic regression model to identify independent predictors. All tests were two-sided, with p < 0.05 deemed statistically significant. Results This study encompassed 203 patients, consisting of 71 individuals in the non-severe sepsis/septic shock group and 132 individuals in the severe sepsis/septic shock group. Demographic and clinical characteristics were presented in Table 1 . No significant differences were observed between groups in age, gender, BMI, smoking, or drinking (all p > 0.05). Among patients in the severe group, the proportion of respiratory tract infections was significantly higher than that in the non-severe group (14.4% vs. 2.8%, p = 0.019), while the proportion of primary infections was significantly lower (17.4% vs. 31.0%, p = 0.041). No statistically significant difference in bacterial makeup ( K. pneumoniae vs. E. coli ) was observed between the two groups. The use of hypoglycemic agents prior to admission was also slightly lower in the severe group (61.4% vs. 76.1%, p = 0.050). Patients in the severe group more frequently received carbapenem antibiotics (75.8% vs. 57.7%, p = 0.013), presented with thrombocytopenia (15.9% vs. 1.4%, p = 0.003), ICU admission (26.5% vs. 4.2%, p < 0.001), and mortality in hospital (9.8% vs. 0%, p = 0.015) were significantly higher than in the non-severe group. Table 1 Demographics, clinical characteristics of diabetic patients with Enterobacteriaceae bloodstream infections Characteristics Non- serious sepsis and/ or septic shock group (n = 71) Serious sepsis and/ or septic shock group (n = 132) p value Age (years) 66.0 (57.0–74.0) 65.0 (57.0- 74.2) 0.785 Gender, n (%) 0.566 Male 28 (39.4) 59 (44.7) Female 43 (60.6) 73 (55.3) BMI 24.1 (22.4–26.8) 24.4 (21.8–27.0) 0.889 Smoking, n (%) 12 (16.9) 20 (15.2) 0.901 Drinking, n (%) 8 (11.3) 12 (9.1) 0.803 Clinical symptoms Fever 38 (53.5) 82 (62.1) 0.299 Nausea 5 (7.0) 7 (5.3) 0.850 Weakness 10 (14.1) 15 (11.4) 0.735 Abdominal.pain 8 (11.3) 15 (11.4) 1.000 Bacteria, n (%) 1.000 K. pneumoniae 40 (56.3) 75 (56.8) E. coli 31 (43.7) 57 (43.2) ESBLs 14 (19.7) 33 (25.0) 0.499 Source.of.infection, n (%) Respiratory system 2(2.8) 19 (14.4) 0.019 Urinary.tract 27 (38.0) 58 (43.9) 0.506 Original.infection 22 (31.0) 23 (17.4) 0.041 Liver- gallbladder and pancreas 15(21.1) 32(24.2) 0.743 Others 2(2.8) 2(1.5) 0.612 Infection.type, n (%) 0.821 Community-acquired 68 (95.8) 124 (93.9) Hospital-acquired 3 (4.2) 8 (6.1) Comorbidities, n(%) Hypertension 44 (62.0) 65 (49.2) 0.113 Tumor 13 (18.3) 12 (9.1) 0.093 Liver cirrhosis 5 (7.0) 9 (6.8) 1.000 Cardiovascular 25 (35.2) 64 (48.5) 0.095 Chronic pulmonary disease 4 (5.6) 5 (3.8) 0.801 Use of hypoglycemic agents before admission 54 (76.1) 81 (61.4) 0.050 Complications.of.diabetes (%) Diabetic kidney disease 7 (9.9) 18 (13.6) 0.577 Diabetic cardiovascular diseases 7 (9.9) 13 (9.8) 1.000 Diabetic neuropathy 7 (9.9) 15 (11.4) 0.927 Diabetic ketoacidosis 14 (19.7) 24 (18.2) 0.937 Diabetic retinopathy 5 (7.0) 14 (10.6) 0.563 Diabetic.foot ulcers 1 (1.4) 4 (3.0) 0.813 Multiple.organ.failure 1 (1.4) 12 (9.1) 0.067 Thrombocytopenia 1(1.4) 21(15.9) 0.003 Antibiotic drugs Carbapenems 41 (57.7) 100 (75.8) 0.013 Cephalosporins 10 (14.1) 12 (9.1) 0.393 Beta-lactamase inhibitors 49 (69.0) 97 (73.5) 0.608 Quinolones 2 (2.8) 3 (2.3) 1.000 ICU admission 3 (4.2) 35 (26.5) < 0.001 Mortality in hospital 0(0) 13(9.8) 0.015 Hospital length of stay, median (IQR) (days) 10.0 (9.0- 14.5) 12.0 (9.0–16.0) 0.082 Laboratory results (Table 2 ) showed that patients in the severe group had significantly higher levels of Neu%, CRP, PCT, LAC, Cre, D-Dimer, and HbA1c compared to the non-severe group ( p < 0.05 for all), while PLT and ALB were significantly lower ( p < 0.01 for both). Notably, HGI and the RDW/LDL-C ratio were also significantly elevated in the severe group ( p = 0.047 and p < 0.001). Table 2 Laboratory findings of diabetic patients with Enterobacteriaceae bloodstream infections on the collection date of the first positive blood culture Characteristics Non- serious sepsis and/ or septic shock group (n = 71) Serious sepsis and/ or septic shock group (n = 132) p value WBC (10^9/L) 11.7 (8.5–15.0) 12.8 (9.1–17.2) 0.105 Neu % 88.4 (80.4–92.3) 91.0 (87.1–94.2) < 0.001 Hb (g/L) 116.0 (99.0- 131.0) 117.0 (101.8–133.0) 0.825 PLT (10^9/L) 197.0 (154.5- 246.5) 151.0 (99.2- 216.2) 0.001 CRP (mg/L) 110.1 (43.0- 204.1) 173.9 (102.9- 232.2) 0.001 PCT (ng/mL) 1.4 (0.5–11.6) 16.7 (3.4–65.1) < 0.001 LAC (mmol/L) 2.0 (1.4–2.4) 2.5 (1.7–3.5) 0.002 PaO2/FiO2 353.5 (317.8–390.0) 348.0 (272.0- 410.0) 0.419 Cre (µmol/L) 71.0 (56.0–98.0) 85.5 (61.8- 144.5) 0.011 ALB (g/L) 33.2 (29.4–37.8) 29.1 (26.2–31.8) < 0.001 D-Dimer (mg/L) 1.8 (0.8–2.9) 2.9 (1.5–5.9) < 0.001 FIB (g/L) 5.9 (4.8–6.8) 6.0 (5.0- 7.1) 0.576 HbA1c (%) 8.7 (7.4–10.1) 9.6 (7.7–11.8) 0.019 GLU (mg/dL) 187.2 (136.7- 226.8) 194.4 (154.9- 235.3) 0.327 HGI -1.0 (-1.9- 0.6) 0.1(-1.9-2.0) 0.047 RDW/LDL 5.4 (4.4–7.7) 8.3 (5.6–11.4) < 0.001 Abbreviations: WBC white blood cells, Neu% neutrophil ratio, Hb hemoglobin, PLT platelets, CRP C-reactive protein, PCT procalcitonin, LAC Lactic acid, PaO2/FiO2 Partial pressure of arterial oxygen/Fraction of inspired oxygen, ALB albumin, Cr creatinine, FIB fibrinogen, HbA1c Glycated Hemoglobin A1c, GLU glucose Further multivariate analysis (Table 3 ) evaluated the relationship between quartiles of the combined HGI-RDW/LDL-C index and the risk of developing severe sepsis and/or septic shock. Compared with Q1 as the reference, adjusted odds ratios for Q2 and Q4 were significantly elevated after adjusting for source of infection (Model 1) or underlying disease (Model 2). In Model 1, Q2 OR = 4.632, 95% CI: 1.922–11.163; Q4 OR = 9.580, 95% CI: 3.478–26.389; Model 2: Q2 OR = 4.458, 95% CI: 1.820-10.917; Q4 OR = 11.337, 95% CI: 3.909–32.879). ROC curve analysis (Fig. 1 ) showed that the combined HGI-RDW/LDL-C index predicted severe sepsis and/or septic shock with an area under the curve (AUC) of 0.760 (95% CI: 0.693–0.826). Table 3 Univariable and multivariate analysis for diabetic patients of Enterobacteriaceae bloodstream infections with severe sepsis and/ or septic shock HGI-RDW/LDL Unadjusted OR (95%CI) p value Model 1 Model 2 Adjusted OR (95%CI) p value Adjusted OR (95%CI) p value Q1 Reference Reference Reference Q2 4.176(1.801–9.684) 0.001 4.632(1.922–11.163) 0.001 4.458(1.820-10.917) 0.001 Q3 2.041(0.927–4.491) 0.076 2.272(0.982–5.255) 0.055 2.260(0.959–5.326) 0.062 Q4 8.776(3.313–23.247) < 0.001 9.580(3.478–26.389) < 0.001 11.337(3.909–32.879) < 0.001 Model 1 Source.of.infection were included in multivariate analysis Model 2 Comorbidities were included in multivariate analysis Figure 2 A illustrated the distribution differences between K. pneumoniae and E. coli across different quartiles (Q1–Q4) of HGI-RDW/LDL-C levels. K. pneumoniae infections were most prevalent in Q4 (62.0%), while E. coli infections were least common in Q4 (38.8%). Severe sepsis and/or septic shock occurred more frequently at HGI-RDW/LDL levels Q2 and Q4 (as shown in Fig. 2 B). Heatmap analysis (Fig. 3 ) illustrated the antibiotic resistance rates of K. pneumoniae and E. coli across different HGI-RDW/LDL-C level groups. At HGI-RDW/LDL-C levels Q1 and Q2, K. pneumoniae exhibited relatively high resistance rates to multiple antibiotics, particularly second-generation cephalosporins. Additionally, carbapenem-resistant strains emerged at the Q1 level. For E. coli , relatively high resistance rates to cephalosporins were primarily concentrated at the Q2 level. Discussion This retrospective study analyzed clinical data from 203 patients with type 2 diabetes who developed bloodstream infections caused by K. pneumoniae or E. coli . Results indicated that both low HGI/high RDW/LDL-C and high HGI/high RDW/LDL-C profiles were significantly associated with the risk of severe sepsis and/or septic shock. These combined indicators demonstrated independent predictive value in multivariate analysis, suggesting their potential as biomarkers for assessing infection severity in diabetic patients. First, this study further confirms the association between poor glycemic control and adverse infection outcomes. HbA1c levels were significantly elevated in the severe group, consistent with previous research [ 19 ] indicating that prolonged hyperglycemia leads to multiple immune dysfunction, including impaired neutrophil chemotaxis, phagocytosis, and bactericidal activity; compromised lymphocyte proliferation; and monocyte/macrophage dysfunction [ 20 ] . This state of “immune paralysis” prevents diabetic patients from effectively eliminating pathogens, leading to the swift spread and worsening of diseases. Concurrently, microcirculatory dysfunction and tissue hypoxia induced by hyperglycemia foster an environment conducive to pathogen proliferation while hindering tissue healing mechanisms [ 21 ] . Additionally, HGI—as an indicator reflecting the association between individual blood glucose fluctuations and target organ damage, was significantly elevated in the severe group, suggesting that blood glucose fluctuations may influence the progression of the infection more than average blood glucose levels alone. In this study, patients in the severe group exhibited elevated inflammatory markers (CRP, PCT) and signs of organ injury (elevated LAC, BUN, D-Dimer; decreased ALB), signifying uncontrolled systemic infection and multi-organ involvement. Hyperglycemia may be a principal catalyst of this detrimental cycle. Infection stimulates the synthesis of inflammatory markers, including CRP and PCT, while concurrently inhibiting insulin secretion or its receptor interactions. Additionally, substances produced by infections, including lipopolysaccharide (LPS), stimulate Toll-like receptors and autoimmune responses, ultimately resulting in disruptions in energy metabolism and insulin resistance [ 22 ] . Secondly, the RDW/LDL-C ratio, serving as a composite indicator of inflammation and metabolic disorders, demonstrated a strong correlation with infection severity in this study. Elevated RDW typically reflects heightened inflammatory states and oxidative stress. In diabetic patients, prolonged hyperglycemia leads to increased production of reactive oxygen species (ROS), interleukins (IL), and TNF-α, disrupting erythropoiesis in the bone marrow. This results in heterogeneous volumes among newly formed red blood cells, thereby elevating RDW. Conversely, low LDL-C may be associated with lipid metabolism disorders and malnutrition during the acute phase of infection. These findings suggest that an elevated RDW/LDL-C ratio may synergistically with HGI reflect metabolic-inflammatory imbalance during infectious stress, thereby influencing sepsis progression. Additionally, this study is the first to investigate the distribution differences of the combined HGI-RDW/LDL-C indicator across different pathogen infections. K. pneumoniae infections were more prevalent in the high HGI-RDW/LDL-C group, and patients in this group also exhibited distinct antibiotic resistance patterns. This may be related to how hyperglycemia and hyperinflammatory states influence the immune microenvironment, thereby altering pathogen colonization and resistance expression—a finding warranting further investigation. This study has several limitations. First, as a single-center retrospective study, the potential for selection bias may be present. The external validity of these findings requires further validation in multicenter, prospective, large-sample studies. Furthermore, this study did not dynamically monitor the trends of these indicators throughout the hospitalization period or their relationship with treatment response and prognostic evolution, potentially failing to fully reflect the metabolic and inflammatory dynamics during the disease course. Conclusion In summary, among diabetic patients with combined Enterobacteriaceae bloodstream infections, the composite indicator HGI-RDW/LDL-C—reflecting the interaction between blood glucose fluctuations and inflammation-metabolism, is an independent risk factor for progression to severe sepsis and/or septic shock and holds predictive value. This indicator facilitates early identification of high-risk patients, providing a basis for optimizing management strategies for blood glucose, lipids, and anti-infective therapy. Declarations Ethics approval and consent to participate The Institutional Medical Ethics Committee of Taizhou Hospital of Zhejiang Province granted approval for this retrospective study, with a waiver of informed consent because the medical records of the subjects were deidentified from the Medical Records and Statistics Room to ensure patient confidentiality. The use of the raw data and the study’s protocol was permitted by the Institutional Medical Ethics Committee of Taizhou Hospital of Zhejiang Province, and all methods were performed following the relevant guidelines and regulations. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding This work was supported by grants from Traditional Chinese Medicine Science and Technology Project of Zhejiang Province (2024ZL526). Author Contribution P Z had roles in the study design, data analysis, literature search, and writing of the manuscript. SX L had roles in guiding research and clinical management. FF X and QF Z had roles in data collection and interpretation. All authors have read and agreed with the final manuscript. Data Availability The datasets generated and analyzed during the current study are not publicly available due to privacy or ethical restrictions but are available from the corresponding author on reasonable request. References Reimar W, Thomsen HH, Hundborg H-H, Lervang SørenP, Johnsen HC, Schønheyder, Sørensen HT. Diabetes mellitus as a risk and prognostic factor for community-acquired bacteremia due to enterobacteria. Clin Infect Dis. 2005;40:628–31. Yo C-H, Lee M-TG, Gi W-T, Chang S-S, Tsai K-C, Chen S-C, Lee C-C. Prognostic determinants of community-acquired bloodstream infection in type 2 diabetic patients in ED. Am J Emerg Med. 2014;32(12):1450–4. Costantini E, Carlin M, Porta M, Brizzi MF. Type 2 diabetes mellitus and sepsis: state of the art, certainties and missing evidence. 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Critchley JA, Carey IM, Harris T, DeWilde S, Hosking FJ, Cook DG. Glycemic Control and Risk of Infections Among People With Type 1 or Type 2 Diabetes in a Large Primary Care Cohort Study. Diabetes Care. 2018;41(10):2127–35. Sohail MU, Mashood F, Oberbach A, Chennakkandathil S, Schmidt F. The role of pathogens in diabetes pathogenesis and the potential of immunoproteomics as a diagnostic and prognostic tool. Front Microbiol. 2022;13:1042362. Pearson-Stuttard J, Blundell S, Harris T, Cook DG, Critchley J. Diabetes and infection: assessing the association with glycaemic control in population-based studies. lancet Diabetes Endocrinol. 2016;4(2):148–58. Manco M, Putignani L, Bottazzo GF. Gut microbiota, lipopolysaccharides, and innate immunity in the pathogenesis of obesity and cardiovascular risk. Endocr Rev. 2010;31(6):817–44. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 19 Mar, 2026 Reviewers invited by journal 17 Mar, 2026 Editor invited by journal 18 Feb, 2026 Editor assigned by journal 16 Feb, 2026 Submission checks completed at journal 16 Feb, 2026 First submitted to journal 13 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8876408","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":607721765,"identity":"fca486f7-307d-4e5f-a87d-1dd5a83ce750","order_by":0,"name":"Peng Zhou","email":"","orcid":"","institution":"The Third Affiliated Hospital of Zhejiang Chinese Medical University","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Zhou","suffix":""},{"id":607721767,"identity":"76026293-3ef0-4afa-aa82-5a39d06776c2","order_by":1,"name":"Fenfen Xu","email":"","orcid":"","institution":"Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fenfen","middleName":"","lastName":"Xu","suffix":""},{"id":607721768,"identity":"2862da25-d646-4ab5-8166-bcd85662a784","order_by":2,"name":"Qiaofei Zheng","email":"","orcid":"","institution":"Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qiaofei","middleName":"","lastName":"Zheng","suffix":""},{"id":607721770,"identity":"8ae20010-ac2d-4ea7-8f7a-346c152e95e6","order_by":3,"name":"Shixiao Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqklEQVRIiWNgGAWjYBACPgYGxgdQtgFxWtgYGJhhSonXwiZBohb+M2YVH3fUyem2N29g+FGxjQgtEmlpN2eeYTM2O3OsgLHnzG1itDAfu83bxpO47UaOATNjGzFa+A+2Ff9tk6jfdv8NsVoYko8BVRokmN3gIVaLRFqyZG9bguG2M2kFB4nyCz//GcMPP9vq5M2OH9744EcFEVpQwAES1Y+CUTAKRsEowAUAjs43RppBbnQAAAAASUVORK5CYII=","orcid":"","institution":"Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Shixiao","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-02-14 02:53:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8876408/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8876408/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104998952,"identity":"50e1d778-7a31-479f-9816-84d02fbde90b","added_by":"auto","created_at":"2026-03-19 16:32:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":6921,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of HGI-RDW/LDL-C for severe sepsis and/or septic shock following \u003cem\u003eEnterobacteriaceae\u003c/em\u003e bloodstream infection in diabetic patients\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8876408/v1/b97b8c13b11d777782cc6526.png"},{"id":104998950,"identity":"6e8792e8-74a7-42f1-872a-ea7530dfc9fd","added_by":"auto","created_at":"2026-03-19 16:32:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53375,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of different bacterial strains and severe vs. non-severe group in patient cohorts stratified by HGI-RDW/LDL-C levels\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8876408/v1/11f638b720e1eea47fa8bec4.png"},{"id":104998951,"identity":"43f193ee-78c6-4e66-8a08-2714849918dc","added_by":"auto","created_at":"2026-03-19 16:32:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":117067,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap depicting antimicrobial susceptibility profiles of \u003cem\u003eK. pneumoniae\u003c/em\u003e and \u003cem\u003eE. coli\u003c/em\u003e isolates across patient groups stratified by HGI-RDW/LDL-C levels\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8876408/v1/f62677cd730dff8faa3d8fce.png"},{"id":104998978,"identity":"375db89c-3275-434b-b390-3fb5eb68857f","added_by":"auto","created_at":"2026-03-19 16:32:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1160634,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8876408/v1/dd7d940a-a4a8-470c-a029-23838eaaaea0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Combined hemoglobin glycation index and RDW/LDL-C ratio predict severe sepsis and/or septic shock in diabetic patients with Enterobacteriaceae bloodstream infections","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePatients with diabetes may have a higher risk of bloodstream infections, typically exhibiting poorer clinical outcomes, longer hospital stays, and a significantly increased risk of mortality \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Diabetic patients experience impaired immune function, microcirculatory disorders, and reduced tissue repair capacity due to prolonged hyperglycemia. This puts patients particularly vulnerable to infections, leading to acute systemic infections that may advance to sepsis and multiple organ failure. Research demonstrates that individuals with diabetes possess a 2 to 6-fold increased risk of acquiring bloodstream infections relative to non-diabetic individuals\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e, and the mortality rate for severe sepsis ranges from approximately 10% to 40%\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Research conducted by Hernando G\u0026oacute;mez \u003cem\u003eet al.\u003c/em\u003e demonstrated that hospitalized patients with type 2 diabetes and sepsis who are administered metformin experience decreased mortality rates within 90 days \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. In recent years, the increasing prevalence of diabetes has rendered diabetic patients with concurrent bloodstream infections a significant focus and problem in clinical management.\u003c/p\u003e \u003cp\u003eBlood glucose control serves as a core indicator in diabetes management. HbA1c is a metabolic byproduct resulting from the attachment of glucose to hemoglobin in the body. It accurately reflects a diabetic patient's blood glucose control over the preceding 2 to 3 months \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. The hemoglobin glycation index (HGI) is a novel metric proposed by Hempe \u003cem\u003eet al\u003c/em\u003e. in 2002 to quantify variations in glycated hemoglobin levels among individual patients \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. He \u003cem\u003eet al\u003c/em\u003e analyzed the trajectory of the dynamic HGI and its impact on survival in patients with sepsis. Trajectory analysis indicated that patients in the rising trajectory group faced markedly higher death risks at both 28 days and 90 days compared to the stable group, whereas patients in the descending trajectory group demonstrated significantly lower mortality risks \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. HGI was independently associated with the risk of sepsis-related encephalopathy and short-term prognosis, serving as a biomarker for prognostic risk stratification \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRed blood cell distribution width (RDW) reflects red blood cell volume heterogeneity. Elevated RDW was associated with acute or chronic heart failure, acute pancreatitis, sepsis, acute pulmonary embolism, and acute kidney injury \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Elevated RDW was independently associated with increased short-term and long-term all-cause mortality in patients with acute pancreatitis and sepsis \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Studies suggested that RDW-CV may serve as a simple and useful biomarker for detecting tubular injury in patients with type 2 diabetes \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Additionally, most diabetic patients exhibit dyslipidemia. Low-density lipoprotein cholesterol (LDL-C) is a lipoprotein that transports cholesterol. Studies indicated that LDL-C levels were closely associated with cardiovascular risk in diabetic patients \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Feng \u003cem\u003eet al.\u003c/em\u003e demonstrated that among hospitalized patients with infections, lower measured LDL-C levels were significantly associated with increased risks of sepsis and ICU admission \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCurrently, most relevant studies focus on comparing the epidemiology of bloodstream infections between diabetic and non-diabetic patients. However, in-depth exploration of the association between different metabolic levels and the prognosis of bloodstream infections caused by various pathogens in diabetic patients remains limited. Therefore, this study aims to systematically analyze the relationship between blood glucose, inflammation-metabolism level and the severity of clinical outcomes in diabetic patients following \u003cem\u003eEnterobacteriaceae\u003c/em\u003e bloodstream infections. This analysis seeks to provide evidence-based support for optimizing glucose and lipid management strategies and improving clinical outcomes in diabetic patient population.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThis study enrolled 203 patients with type 2 diabetes diagnosed with \u003cem\u003eK. pneumoniae\u003c/em\u003e or \u003cem\u003eE. coli\u003c/em\u003e bloodstream infections admitted to Taizhou Hospital in Zhejiang Province from January 2023 to October 2025 through the departments of Emergency Medicine, Infectious Diseases, Endocrinology, and Critical Care Medicine. Inclusion Criteria: (1) Patients with diabetes must meet satisfy the diagnostic criteria specified in the Chinese Guidelines for the Prevention and Treatment of Type 2 Diabetes (2020 Edition), with comprehensive admission blood glucose records and regular blood glucose monitoring records during hospitalization. (2) Fulfilled the diagnostic criteria for bloodstream infection: a. At least one blood culture with detection of the corresponding pathogen; b. Clinical evidence of infection (\u003cem\u003ee.g.\u003c/em\u003e, fever\u0026thinsp;\u0026gt;\u0026thinsp;38\u0026deg;C, chills, or hypotension). Exclusion criteria: Age\u0026thinsp;\u0026lt;\u0026thinsp;18 years old, pregnant women, incomplete case records, transfer to another hospital during the study period. The definitions of sepsis and septic shock were based on the Third International Consensus Definition of Sepsis and septic shock (Sepsis-3) \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Sepsis accompanied by hypotension, hypoperfusion, or organ failure was designated as severe sepsis \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. We categorized the participants into two categories based on clinical diagnostic information and patient severity: the severe sepsis and/or septic shock group and the non-severe sepsis and/or septic shock group. The study was approved by the Helsinki ethics committee at our institution.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMicrobiological methods\u003c/h3\u003e\n\u003cp\u003e All specimen collection and testing were conducted in accordance with the Chinese Guidelines for Clinical Laboratory Operations. Two separate sets of blood cultures were routinely collected, with 10 mL of blood collected into one aerobic and one anaerobic bottle. Blood cultures were incubated in the BacT/Alert system (bioM\u0026eacute;rieux, France) for a duration of 5 days. The isolates were identified as \u003cem\u003eK. pneumoniae\u003c/em\u003e and \u003cem\u003eE. coli\u003c/em\u003e using MALDI-TOF mass spectrometry (bioM\u0026eacute;rieux, France). Antibiotic susceptibility was tested using the VITEK 2 compact system (bioM\u0026eacute;rieux Vitek Inc., France) with microbroth dilution. Partial susceptibility tests were performed by the disk diffusion method on Mueller-Hinton agar. Antibiotic susceptibility results were interpreted according to Clinical and Laboratory Standards Institute (CLSI) guidelines. \u003cem\u003eE. coli\u003c/em\u003e ATCC 25922 and \u003cem\u003eK. pneumoniae\u003c/em\u003e ATCC 700603 served as quality control strains.\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eReview the electronic medical record system to collect and administer all patient information. Recorded clinical information includes gender, age, underlying conditions, primary infection site, clinical scores, comorbidities, laboratory test results, microbiological data, medication history, and clinical outcomes. Laboratory results included all blood parameters measured concurrently with the collection of the patient's first positive blood culture after admission, encompassing procalcitonin, C-reactive protein, lactate, oxygenation index, complete blood count, biochemical profile, coagulation function, and other relevant diagnostic indicators.\u003c/p\u003e \u003cp\u003eUsing fasting plasma glucose (FPG) values as the independent variable and measured HbA1c values as the dependent variable for all patients, we assessed the linear relationship between the two. A linear regression equation was then established to calculate predicted HbA1c values (Predicted HbA1c\u0026thinsp;=\u0026thinsp;7.969\u0026thinsp;+\u0026thinsp;0.008 \u0026times; FPG). and calculated the HGI value (HGI\u0026thinsp;=\u0026thinsp;measured HbA1c value - predicted HbA1c value) \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Patients were divided into four quartile groups based on the combination of HGI and RDW/LDL-C: Q1 (low HGI and low RDW/LDL-C), Q2 (low HGI and high RDW/LDL-C), Q3 (high HGI and low RDW/LDL-C), and Q4 (high HGI and high RDW/LDL-C).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThis study employed SPSS 23.0 (SPSS Inc., Chicago, IL, USA) for statistical analysis. Graphs and charts were generated using R software. Non-normally distributed variables were expressed as median (interquartile range) [M (P25, P75)], with intergroup comparisons performed using the \u003cem\u003eMann-Whitney U\u003c/em\u003e test. Categorical variables were characterized by frequencies and percentages, with intergroup comparisons using the chi-square (χ\u0026sup2;) test or Fisher's exact test. Factors with statistically significant differences in univariate analysis were incorporated into a multivariate logistic regression model to identify independent predictors. All tests were two-sided, with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 deemed statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThis study encompassed 203 patients, consisting of 71 individuals in the non-severe sepsis/septic shock group and 132 individuals in the severe sepsis/septic shock group. Demographic and clinical characteristics were presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. No significant differences were observed between groups in age, gender, BMI, smoking, or drinking (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Among patients in the severe group, the proportion of respiratory tract infections was significantly higher than that in the non-severe group (14.4% \u003cem\u003evs.\u003c/em\u003e 2.8%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019), while the proportion of primary infections was significantly lower (17.4% \u003cem\u003evs.\u003c/em\u003e 31.0%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041). No statistically significant difference in bacterial makeup (\u003cem\u003eK. pneumoniae vs. E. coli\u003c/em\u003e) was observed between the two groups. The use of hypoglycemic agents prior to admission was also slightly lower in the severe group (61.4% vs. 76.1%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.050). Patients in the severe group more frequently received carbapenem antibiotics (75.8% \u003cem\u003evs.\u003c/em\u003e 57.7%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013), presented with thrombocytopenia (15.9% \u003cem\u003evs.\u003c/em\u003e 1.4%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), ICU admission (26.5% \u003cem\u003evs.\u003c/em\u003e 4.2%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and mortality in hospital (9.8% \u003cem\u003evs.\u003c/em\u003e 0%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015) were significantly higher than in the non-severe group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographics, clinical characteristics of diabetic patients with \u003cem\u003eEnterobacteriaceae\u003c/em\u003e bloodstream infections\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon- serious sepsis and/ or septic shock group (n\u0026thinsp;=\u0026thinsp;71)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSerious sepsis and/ or septic shock group (n\u0026thinsp;=\u0026thinsp;132)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.0 (57.0\u0026ndash;74.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.0 (57.0- 74.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.566\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (39.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (44.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (60.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (55.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.1 (22.4\u0026ndash;26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.4 (21.8\u0026ndash;27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (53.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82 (62.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNausea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeakness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal.pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eK. pneumoniae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (56.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (56.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (43.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (43.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESBLs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource.of.infection, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary.tract\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOriginal.infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver- gallbladder and pancreas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfection.type, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity-acquired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (95.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124 (93.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital-acquired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (62.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (49.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver cirrhosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (35.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64 (48.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic pulmonary disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of hypoglycemic agents before admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (76.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81 (61.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplications.of.diabetes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetic kidney disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetic cardiovascular diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetic neuropathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetic ketoacidosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.937\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetic retinopathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetic.foot ulcers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple.organ.failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThrombocytopenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntibiotic drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbapenems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (57.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (75.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCephalosporins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeta-lactamase inhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (69.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97 (73.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuinolones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMortality in hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital length of stay, median (IQR) (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.0 (9.0- 14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.0 (9.0\u0026ndash;16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLaboratory results (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) showed that patients in the severe group had significantly higher levels of Neu%, CRP, PCT, LAC, Cre, D-Dimer, and HbA1c compared to the non-severe group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all), while PLT and ALB were significantly lower (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for both). Notably, HGI and the RDW/LDL-C ratio were also significantly elevated in the severe group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLaboratory findings of diabetic patients with \u003cem\u003eEnterobacteriaceae\u003c/em\u003e bloodstream infections on the collection date of the first positive blood culture\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon- serious sepsis and/ or septic shock group (n\u0026thinsp;=\u0026thinsp;71)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSerious sepsis and/ or septic shock group (n\u0026thinsp;=\u0026thinsp;132)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.7 (8.5\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.8 (9.1\u0026ndash;17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeu %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88.4 (80.4\u0026ndash;92.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91.0 (87.1\u0026ndash;94.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e116.0 (99.0- 131.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e117.0 (101.8\u0026ndash;133.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT (10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e197.0 (154.5- 246.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e151.0 (99.2- 216.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110.1 (43.0- 204.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e173.9 (102.9- 232.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCT (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.4 (0.5\u0026ndash;11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.7 (3.4\u0026ndash;65.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAC (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.0 (1.4\u0026ndash;2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.5 (1.7\u0026ndash;3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaO2/FiO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e353.5 (317.8\u0026ndash;390.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e348.0 (272.0- 410.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCre (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.0 (56.0\u0026ndash;98.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85.5 (61.8- 144.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.2 (29.4\u0026ndash;37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.1 (26.2\u0026ndash;31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-Dimer (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.8 (0.8\u0026ndash;2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.9 (1.5\u0026ndash;5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFIB (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.9 (4.8\u0026ndash;6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.0 (5.0- 7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.7 (7.4\u0026ndash;10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.6 (7.7\u0026ndash;11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLU (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e187.2 (136.7- 226.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e194.4 (154.9- 235.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.0 (-1.9- 0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1(-1.9-2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDW/LDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.4 (4.4\u0026ndash;7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.3 (5.6\u0026ndash;11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: WBC white blood cells, Neu% neutrophil ratio, Hb hemoglobin, PLT platelets, CRP C-reactive protein, PCT procalcitonin, LAC Lactic acid, PaO2/FiO2 Partial pressure of arterial oxygen/Fraction of inspired oxygen, ALB albumin, Cr creatinine, FIB fibrinogen, HbA1c Glycated Hemoglobin A1c, GLU glucose\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFurther multivariate analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) evaluated the relationship between quartiles of the combined HGI-RDW/LDL-C index and the risk of developing severe sepsis and/or septic shock. Compared with Q1 as the reference, adjusted odds ratios for Q2 and Q4 were significantly elevated after adjusting for source of infection (Model 1) or underlying disease (Model 2). In Model 1, Q2 OR\u0026thinsp;=\u0026thinsp;4.632, 95% CI: 1.922\u0026ndash;11.163; Q4 OR\u0026thinsp;=\u0026thinsp;9.580, 95% CI: 3.478\u0026ndash;26.389; Model 2: Q2 OR\u0026thinsp;=\u0026thinsp;4.458, 95% CI: 1.820-10.917; Q4 OR\u0026thinsp;=\u0026thinsp;11.337, 95% CI: 3.909\u0026ndash;32.879). ROC curve analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) showed that the combined HGI-RDW/LDL-C index predicted severe sepsis and/or septic shock with an area under the curve (AUC) of 0.760 (95% CI: 0.693\u0026ndash;0.826).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariable and multivariate analysis for diabetic patients of \u003cem\u003eEnterobacteriaceae\u003c/em\u003e bloodstream infections with severe sepsis and/ or septic shock\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGI-RDW/LDL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted OR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted OR (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eAdjusted OR (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.176(1.801\u0026ndash;9.684)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.632(1.922\u0026ndash;11.163)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e4.458(1.820-10.917)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.041(0.927\u0026ndash;4.491)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.272(0.982\u0026ndash;5.255)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2.260(0.959\u0026ndash;5.326)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.776(3.313\u0026ndash;23.247)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.580(3.478\u0026ndash;26.389)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e11.337(3.909\u0026ndash;32.879)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel 1 Source.of.infection were included in multivariate analysis\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel 2 Comorbidities were included in multivariate analysis\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA illustrated the distribution differences between \u003cem\u003eK. pneumoniae\u003c/em\u003e and \u003cem\u003eE. coli\u003c/em\u003e across different quartiles (Q1\u0026ndash;Q4) of HGI-RDW/LDL-C levels. \u003cem\u003eK. pneumoniae\u003c/em\u003e infections were most prevalent in Q4 (62.0%), while \u003cem\u003eE. coli\u003c/em\u003e infections were least common in Q4 (38.8%). Severe sepsis and/or septic shock occurred more frequently at HGI-RDW/LDL levels Q2 and Q4 (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHeatmap analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) illustrated the antibiotic resistance rates of \u003cem\u003eK. pneumoniae\u003c/em\u003e and \u003cem\u003eE. coli\u003c/em\u003e across different HGI-RDW/LDL-C level groups. At HGI-RDW/LDL-C levels Q1 and Q2, \u003cem\u003eK. pneumoniae\u003c/em\u003e exhibited relatively high resistance rates to multiple antibiotics, particularly second-generation cephalosporins. Additionally, carbapenem-resistant strains emerged at the Q1 level. For \u003cem\u003eE. coli\u003c/em\u003e, relatively high resistance rates to cephalosporins were primarily concentrated at the Q2 level.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis retrospective study analyzed clinical data from 203 patients with type 2 diabetes who developed bloodstream infections caused by \u003cem\u003eK. pneumoniae\u003c/em\u003e or \u003cem\u003eE. coli\u003c/em\u003e. Results indicated that both low HGI/high RDW/LDL-C and high HGI/high RDW/LDL-C profiles were significantly associated with the risk of severe sepsis and/or septic shock. These combined indicators demonstrated independent predictive value in multivariate analysis, suggesting their potential as biomarkers for assessing infection severity in diabetic patients.\u003c/p\u003e \u003cp\u003eFirst, this study further confirms the association between poor glycemic control and adverse infection outcomes. HbA1c levels were significantly elevated in the severe group, consistent with previous research \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e indicating that prolonged hyperglycemia leads to multiple immune dysfunction, including impaired neutrophil chemotaxis, phagocytosis, and bactericidal activity; compromised lymphocyte proliferation; and monocyte/macrophage dysfunction \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. This state of \u0026ldquo;immune paralysis\u0026rdquo; prevents diabetic patients from effectively eliminating pathogens, leading to the swift spread and worsening of diseases. Concurrently, microcirculatory dysfunction and tissue hypoxia induced by hyperglycemia foster an environment conducive to pathogen proliferation while hindering tissue healing mechanisms \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Additionally, HGI\u0026mdash;as an indicator reflecting the association between individual blood glucose fluctuations and target organ damage, was significantly elevated in the severe group, suggesting that blood glucose fluctuations may influence the progression of the infection more than average blood glucose levels alone.\u003c/p\u003e \u003cp\u003eIn this study, patients in the severe group exhibited elevated inflammatory markers (CRP, PCT) and signs of organ injury (elevated LAC, BUN, D-Dimer; decreased ALB), signifying uncontrolled systemic infection and multi-organ involvement. Hyperglycemia may be a principal catalyst of this detrimental cycle. Infection stimulates the synthesis of inflammatory markers, including CRP and PCT, while concurrently inhibiting insulin secretion or its receptor interactions. Additionally, substances produced by infections, including lipopolysaccharide (LPS), stimulate Toll-like receptors and autoimmune responses, ultimately resulting in disruptions in energy metabolism and insulin resistance \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSecondly, the RDW/LDL-C ratio, serving as a composite indicator of inflammation and metabolic disorders, demonstrated a strong correlation with infection severity in this study. Elevated RDW typically reflects heightened inflammatory states and oxidative stress. In diabetic patients, prolonged hyperglycemia leads to increased production of reactive oxygen species (ROS), interleukins (IL), and TNF-α, disrupting erythropoiesis in the bone marrow. This results in heterogeneous volumes among newly formed red blood cells, thereby elevating RDW. Conversely, low LDL-C may be associated with lipid metabolism disorders and malnutrition during the acute phase of infection. These findings suggest that an elevated RDW/LDL-C ratio may synergistically with HGI reflect metabolic-inflammatory imbalance during infectious stress, thereby influencing sepsis progression.\u003c/p\u003e \u003cp\u003eAdditionally, this study is the first to investigate the distribution differences of the combined HGI-RDW/LDL-C indicator across different pathogen infections. \u003cem\u003eK. pneumoniae\u003c/em\u003e infections were more prevalent in the high HGI-RDW/LDL-C group, and patients in this group also exhibited distinct antibiotic resistance patterns. This may be related to how hyperglycemia and hyperinflammatory states influence the immune microenvironment, thereby altering pathogen colonization and resistance expression\u0026mdash;a finding warranting further investigation.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, as a single-center retrospective study, the potential for selection bias may be present. The external validity of these findings requires further validation in multicenter, prospective, large-sample studies. Furthermore, this study did not dynamically monitor the trends of these indicators throughout the hospitalization period or their relationship with treatment response and prognostic evolution, potentially failing to fully reflect the metabolic and inflammatory dynamics during the disease course.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, among diabetic patients with combined \u003cem\u003eEnterobacteriaceae\u003c/em\u003e bloodstream infections, the composite indicator HGI-RDW/LDL-C\u0026mdash;reflecting the interaction between blood glucose fluctuations and inflammation-metabolism, is an independent risk factor for progression to severe sepsis and/or septic shock and holds predictive value. This indicator facilitates early identification of high-risk patients, providing a basis for optimizing management strategies for blood glucose, lipids, and anti-infective therapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003e The Institutional Medical Ethics Committee of Taizhou Hospital of Zhejiang Province granted approval for this retrospective study, with a waiver of informed consent because the medical records of the subjects were deidentified from the Medical Records and Statistics Room to ensure patient confidentiality. The use of the raw data and the study\u0026rsquo;s protocol was permitted by the Institutional Medical Ethics Committee of Taizhou Hospital of Zhejiang Province, and all methods were performed following the relevant guidelines and regulations.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by grants from Traditional Chinese Medicine Science and Technology Project of Zhejiang Province (2024ZL526).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eP Z had roles in the study design, data analysis, literature search, and writing of the manuscript. SX L had roles in guiding research and clinical management. FF X and QF Z had roles in data collection and interpretation. All authors have read and agreed with the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due to privacy or ethical restrictions but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eReimar W, Thomsen HH, Hundborg H-H, Lervang S\u0026oslash;renP, Johnsen HC, Sch\u0026oslash;nheyder, S\u0026oslash;rensen HT. Diabetes mellitus as a risk and prognostic factor for community-acquired bacteremia due to enterobacteria. Clin Infect Dis. 2005;40:628\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYo C-H, Lee M-TG, Gi W-T, Chang S-S, Tsai K-C, Chen S-C, Lee C-C. Prognostic determinants of community-acquired bloodstream infection in type 2 diabetic patients in ED. Am J Emerg Med. 2014;32(12):1450\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCostantini E, Carlin M, Porta M, Brizzi MF. Type 2 diabetes mellitus and sepsis: state of the art, certainties and missing evidence. Acta Diabetol. 2021;58(9):1139\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaupland KB, Gregson DB, Zygun DA, Doig CJ, Mortis G, Church DL. Severe bloodstream infections: a population-based assessment. Crit Care Med. 2004;32(4):992\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrinsley JS, Egi M, Kiss A, Devendra AN, Schuetz P, Maurer PM, Schultz MJ, van Hooijdonk RT, Kiyoshi M, Mackenzie IM, Annane D, Stow P, Nasraway SA, Holewinski S, Holzinger U, Preiser JC, Vincent JL, Bellomo R. Diabetic status and the relation of the three domains of glycemic control to mortality in critically ill patients: an international multicenter cohort study. Crit Care. 2013;17(2):R37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHernando G, Gaspar DR-P, Priyanka P, Carlos LM-C, Chung-Chou HC, Shu W, Qing L, Brian SZ, Raghavan M, Derek CA, John AK. Association of Metformin Use During Hospitalization and Mortality in Critically Ill Adults With Type 2 Diabetes Mellitus and Sepsis. Crit Care Med. 2022;50(6):935\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavid BS, Sue M, Randie K. Point-of-Care HbA1c in Clinical Practice: Caveats and Considerations for Optimal Use. Diabetes Care. 2024;47(7):1104\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHempe JM, Gomez R, McCarter RJ Jr., Chalew SA. High and low hemoglobin glycation phenotypes in type 1 diabetes: a challenge for interpretation of glycemic control. J Diabetes Complicat. 2002;16(5):313\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe A, Jiang W, Fu J, Xu L, You C, Li S, Yu J, Zheng R. Dynamic HGI trajectories and their impact on survival in patients with sepsis: a machine learning prognostic model. Inflamm research: official J Eur Histamine Res Soc [et al]. 2025;74(1):145.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi M, Sun H, Ma Y, Chi C. Association Between Hemoglobin Glycation Index and Delirium Risk in Sepsis Patients in the Intensive Care Unit. Balkan Med J. 2026;43(1):38\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang L, Yu CH, Guo KP, Huang CZ, Mo LY. Prognostic role of red blood cell distribution width in patients with sepsis: a systematic review and meta-analysis. BMC Immunol. 2020;21(1):40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong Q, Wu X, Taheri FA, Meng L, Wang W, Mo X. Association of red blood cell distribution width with short- and long-term all-cause mortality in patients with acute pancreatitis and sepsis. BMC Gastroenterol. 2025;25(1):539.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiyataka K, Kaneko Y, Hori T, Yamaguchi Y, Tsuji S, Hara T, Yamagami H, Yoshida S, Otoda T, Yuasa T, Kuroda A, Harada T, Miki H, Nakamura S, Endo I, Matsuhisa M, Matsuoka KI, Aihara KI. Red blood cell distribution width is associated with renal tubular injury in individuals with type 2 diabetes. J diabetes Invest. 2025;16(12):2191\u0026ndash;200.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRottura M, Scondotto G, Barbieri MA, Sorbara EE, Nasso C, Marino S, Scoglio R, Mandraffino G, Pallio G, Irrera N, Imbalzano E, Squadrito G, Squadrito F, Arcoraci V. Management of High Cardiovascular Risk in Diabetic Patients: Focus on Low Density Lipoprotein Cholesterol and Appropriate Drug Use in General Practice. Front Cardiovasc Med. 2021;8:749686.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng Q, Wei WQ, Chaugai S, Leon BGC, Mosley JD, Leon DAC, Jiang L, Ihegword A, Shaffer CM, Linton MF, Chung CP, Stein CM. Association Between Low-Density Lipoprotein Cholesterol Levels and Risk for Sepsis Among Patients Admitted to the Hospital With Infection. JAMA Netw Open. 2019;2(1):e187223.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinger M, Deutschman C, Seymour C, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard G, Chiche J, Coopersmith C, Hotchkiss R, Levy M, Marshall J, Martin G, Opal S, Rubenfeld G, van der Poll T, Vincent J, Angus DJJ. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016;315(8):801\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevy MM, Fink MP, Marshall JC, Abraham E, Angus D, Cook D, Cohen J, Opal SM, Vincent JL, Ramsay G. 2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference. Crit Care Med. 2003, 31(4):1250\u0026ndash;1256.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHempe JM, Liu S, Myers L, McCarter RJ, Buse JB, Fonseca V. The hemoglobin glycation index identifies subpopulations with harms or benefits from intensive treatment in the ACCORD trial. Diabetes Care. 2015;38(6):1067\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCritchley JA, Carey IM, Harris T, DeWilde S, Hosking FJ, Cook DG. Glycemic Control and Risk of Infections Among People With Type 1 or Type 2 Diabetes in a Large Primary Care Cohort Study. Diabetes Care. 2018;41(10):2127\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSohail MU, Mashood F, Oberbach A, Chennakkandathil S, Schmidt F. The role of pathogens in diabetes pathogenesis and the potential of immunoproteomics as a diagnostic and prognostic tool. Front Microbiol. 2022;13:1042362.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePearson-Stuttard J, Blundell S, Harris T, Cook DG, Critchley J. Diabetes and infection: assessing the association with glycaemic control in population-based studies. lancet Diabetes Endocrinol. 2016;4(2):148\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManco M, Putignani L, Bottazzo GF. Gut microbiota, lipopolysaccharides, and innate immunity in the pathogenesis of obesity and cardiovascular risk. Endocr Rev. 2010;31(6):817\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Type 2 diabetes mellitus, Enterobacteriaceae, Bloodstream infections, HGI-RDW/LDL-C","lastPublishedDoi":"10.21203/rs.3.rs-8876408/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8876408/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo investigate the predictive value of hemoglobin glycation index (HGI) and red cell distribution width/low-density lipoprotein cholesterol ratio (RDW/LDL-C) for progression to severe sepsis and/or septic shock following \u003cem\u003eEnterobacteriaceae\u003c/em\u003e bloodstream infection in diabetic patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective study included 203 diabetic patients with concomitant \u003cem\u003eEnterobacteriaceae\u003c/em\u003e bloodstream infections. Clinical data were collected, and the HGI and RDW/LDL-C were calculated. Patients were grouped into quartiles (Q1-Q4) based on combined indicator quartiles. Multivariate logistic regression analysis was performed to assess the association with severe infection risk, and predictive performance was evaluated using ROC curves.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePatients in the severe group exhibited significantly higher HGI and RDW/LDL-C levels compared to the non-severe group (both \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Multivariate analysis showed that after adjusting for source of infection (Model 1) and comorbidities (Model 2), with Q1 as the reference group, Q2 and Q4 were independent predictors of severe sepsis and/or septic shock, respectively. ROC curve analysis indicated that the combined HGI-RDW/LDL-C index had an area under the curve (AUC) of 0.760 (95% CI: 0.693\u0026ndash;0.826) for predicting severe sepsis and/or septic shock. Furthermore, the proportion of \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e infections and antibiotic resistance patterns exhibited specific distributions within the high combined index group.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe HGI-RDW/LDL-C composite index serves as an independent risk factor for progression to severe sepsis and/or septic shock following \u003cem\u003eEnterobacteriaceae\u003c/em\u003e bloodstream infection in diabetic patients, demonstrating good predictive value.\u003c/p\u003e","manuscriptTitle":"Combined hemoglobin glycation index and RDW/LDL-C ratio predict severe sepsis and/or septic shock in diabetic patients with Enterobacteriaceae bloodstream infections","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-19 16:31:57","doi":"10.21203/rs.3.rs-8876408/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"60783294309314156285330028745499836237","date":"2026-03-20T01:36:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-17T11:47:45+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-18T08:53:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-16T06:37:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-16T06:35:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Endocrine Disorders","date":"2026-02-14T02:47:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"25db7072-3d44-4d29-bdf9-f16374ec7557","owner":[],"postedDate":"March 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-19T16:31:57+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-19 16:31:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8876408","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8876408","identity":"rs-8876408","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}