Non-linear relationship between the atherogenic index of plasma and ischemic stroke in the diabetic population in ICU: A multicenter retrospective cohort study

Non-linear relationship between the atherogenic index of plasma and ischemic stroke in the diabetic population in ICU: A multicenter retrospective cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Non-linear relationship between the atherogenic index of plasma and ischemic stroke in the diabetic population in ICU: A multicenter retrospective cohort study Liling Wu, Zhihang Su, Xingling Chen, Haofei Hu, Qijun Wan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5638991/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives The Atherogenic Index of Plasma (AIP) value is relationship with the risk of atherosclerosis, a known risk factor for cardiovascular events. However, studies on the correlation between AIP value and ischemic stroke (IS) in the diabetic population in Intensive Care Unit (ICU) are rare. Our study aimed to investigate the relationship between AIP values and IS among diabetic patients in American ICUs. Methods A multicenter retrospective cohort study comprising 3695 patients from the eICU-CRD v2.0 database between 2014 and 2015 in the USA was conducted. We utilized logistic regression model to investigate the correlation between between AIP values and IS among diabetic patients in American ICUs. To detect possible non-linear associations, we combined logistic regression with generalized additive model (GAM). Additionally, we conducted a thorough array of sensitivity and subgroup analyses to verify the robustness of our results. Results The prevalence of IS was 19.46%. The median AIP was 0.52 (interquartile range, 0.29-0.76). Participants with stroke exhibited a significant elevation in AIP levels. In particular, each one-unit elevation in AIP levels was associated with a 40% increased risk of IS (OR=1.4, 95% CI 1.1-2.8, P<0.001). In addition, a non-linear relationship exists between the AIP value and the incidence of IS, with an inflection point at 0.8. The effect sizes (OR) on the left and right sides of the inflection point were 0.9 (95%CI: 0.5-1.8; P= 0.741) and 1.7 (95%CI: 1.2-2.5; P＜0.001 ), respectively. Conclusion The research uncovers a positive, non-linear correlation between the AIP value and IS incidence among diabetic patients in American ICUs. Notably, a significant association between the AIP value and IS emerges when the AIP value is less than 0.8. From a therapeutic perspective, reducing AIP levels below the inflection point seems reasonable. However, the findings require validation through prospective studies. Health sciences/Endocrinology Health sciences/Diseases/Cardiovascular diseases Health sciences/Diseases/Endocrine system and metabolic diseases atherogenic index of plasma ischemic stroke ICU patients multicenter study diabetes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Cardiometabolic disorders, such as type 2 diabetes and stroke, continue to be the leading causes of premature mortality globally ( 1 ). Ischemic stroke (IS), with its significant global impact on mortality, often results in incapacitating events ( 2 ). Atherosclerosis is the primary cause of cerebral infarction, which is the common cause of IS ( 3 , 4 ). Type 2 diabetes mellitus (T2DM), primarily driven by insulin resistance (IR), not only damages vascular endothelial function, leading to plaque formation and rupture in the carotid artery ( 5 , 6 ), but also significantly contributes to the incidence of IS ( 7 ). Cardiometabolic markers, like the atherogenic index of plasma (AIP), integrate high-density lipoprotein (HDL) and triglycerides (TG) levels, providing a more comprehensive insight into dyslipidemia. Previous findings reveal the correlation between the augmentation of carotid artery blood flow velocity and the increase in AIP value. Elevated atherosclerosis index is commonly indicative of exacerbated atherosclerosis, compromised vascular function, and impaired blood flow, thereby heightening the susceptibility to cardiovascular events ( 8 ). In prior studies, it was demonstrated that heightened levels of baseline and long-term updated mean AIP were linked to an increased risk of stroke ( 9 ). Subsequent research revealed the positive correlations between AIP and the risk of prediabetes and T2DM in individuals aged over 45 in China ( 10 ). Furthermore, a higher AIP value identified upon admission was independently and strongly correlated with adverse cardiovascular events in patients diagnosed with T2DM ( 11 ). The diabetic population among ICU patients with IS generally exhibits several notable characteristics: increased morbidity and mortality, a higher incidence of comorbidities, and worse neurological outcomes. Effective management and treatment of diabetic patients with IS in the ICU are crucial to address these challenges. However, it remains unknown whether this association between AIP and IS persists in the diabetic population among ICU patients, who generally exhibit more severe pathophysiological conditions. Therefore, evaluating whether the AIP index can function as a potential predictor for the incidence of stroke among diabetic ICU patients could aid in identifying those at elevated stroke risk, thereby enhancing healthcare management or timely intervention. We hypothesized that AIP index is a correlation between the AIP index an increased risk of IS among DM patients in ICU settings. Hence, our study aims to analyze the eICU Collaborative Research Database v2.0 (eICU-CRD v2.0) database to evaluate the significance of the AIP value in predicting the incidence of stroke among the diabetic population in ICU. Methods Study population This retrospective study investigated data obtained from the eICU-CRD v2.0, which is a multicenter retrospective cohort study. The database comprises medical records of 200,859 ICU patients from 335 ICUs across 208 hospitals (both academic and non-academic) in the USA during the years 2014–2015. ( 12 ). The exclusion criteria were as follows: ( 1 ) patients without DM (n = 181267); ( 2 ) patients without TG or HDL were extracted (n = 15897); ( 3 ) patients with extreme values of log(TG/HDL) (n = 40) (the extreme value = mean ± standard deviation (SD))( 13 ). Finally, 3695 participants with Diabetic were included in this analysis, including 719 participants with stroke and 2976 participants without stroke. Figure 1 illustrates the study design and participant flow. Data collection The variables extracted from eICU-CRD v2.0 included the following: 1) demographic characteristics: body mass index (BMI), age, ethnicity and gender; 2) comorbidities: chronic kidney disease (CKD), hypertension, diabetes mellitus, acute respiratory failure (ARF), ketoacidosis, acute myocardial infarction (AMI), atrial fibrillation (AF), chronic obstructive pulmonary disease (COPD), end-stage renal disease (ESRD), cardiac arrest, congestive heart failure (CHF), sepsis, stroke, gastrointestinal bleeding (GB), and cancer; 3) laboratory parameters: serum albumin (ALB), triglycerides, hemoglobin (HB), platelet count, HDL, serum creatinine, cholesterol, ALT, and lactate; and 4) treatment: nitroglycerin, levofloxacin, glucocorticoids, vancomycin, carbapenem, and mechanical ventilation. AIP was treated as a continuous variable and calculated using the formula: AIP = log(TG/HDL). This calculation was based on the baseline levels of TG and HDL ( 11 ). The outcome variable was stroke in-hospital in participants with diabetic. Statistical analysis Participants were categorized into quintiles based on their AIP values. Continuous variables were presented as mean ± SD or median with interquartile ranges (IQR). Categorical variables were reported as percentages. Differences among the AIP groups were assessed using the χ² test for categorical variables, One-Way ANOVA for normally distributed continuous variables, or the Kruskal-Wallis H test for skewed continuous variables. Prevalence rates were expressed as cumulative prevalence. The association between AIP and stroke was examined using multiple logistic regression analyses, adjusting for key confounding variables, including gender, ethnicity, cardiac arrest, acute myocardial infarction, age, gastrointestinal bleeding, BMI, cancer, antiplatelet drugs, and cholesterol-lowering drugs. Effect sizes were reported as odds ratios (OR) with 95% confidence intervals (CI). Additionally, a Generalized Additive Model (GAM) was used to include the continuous covariate as a curve in the equation (model III) to ensure the robustness of the results. For sensitivity analyses exploring the association between AIP and stroke, participants were included without excluding AIP outliers. Subgroup analyses were performed to evaluate the consistency of the association between AIP and incident stroke across different subgroups defined by age (< 60, ≥ 60 years), gender, BMI (< 18.5, 18.5 to < 23.9, ≥ 23.9 kg/m²), cardiac arrest, and hypertension. Smooth curve fitting and GAM were utilized to illustrate the relationship between AIP and stroke. The inflection point and threshold effect of AIP on stroke incidence were determined using a two-piece linear regression model. Statistical analyses were conducted using the R software ( http://www.R-project.org , The R Foundation) and EmpowerStats ( http://www.empowerstats.com , X&Y Solutions, Inc., Boston, MA). P-values less than 0.05 (two-sided) were considered statistically significant. Statement We identify the institutional and/or licensing committee approving the experiments, including any relevant details; (ii) confirm that all experiments were performed in accordance with relevant guidelines and regulations. Our research raw data comes from the eICU-CRD v2.0 ( https://eicu-crd.mit.edu/ ), an open database. It was approved by the committee for use in research. We confirm that all research was performed in accordance with relevant guidelines/regulations. Informed consent from participants can be waived. Research involving human participants was conducted in accordance with the Declaration of Helsinki. Results Characteristics of the participants The final analysis included 3695 adult participants (Fig. 1). The mean age was 65.1 ± 13.7 years, with 2187 (59.2%) being male. The median AIP stood at 0.52 (interquartile range: 0.29–0.76). Stroke prevalence was 19.46% (719 out of 3695). The participants were categorized by AIP quartiles. Compared with the Q1 group, the highest AIP group (≥ 0.76) exhibited the highest age, platelet count, and ALB levels, along with the lowest BMI, Hb, creatinine, and ALT levels. Additionally, this group had a higher proportion of females, Ketoacidosis, Hypertension, AMI, and AF, as well as a lower proportion of ARF, COPD, cardiac arrest, CHF, GB, CKD, ESRD, sepsis, and cancer. The AIP exhibits a normal distribution, spanning from − 0.6 to 1.7, with a mean of 0.54 (Fig. 2). When stratified by age into 10 intervals, stroke prevalence rises with age and is more prevalent in men than in women (Fig. 3). Marked elevation in the AIP level in participants with stroke We categorized participants into two groups: those who had experienced a stroke and those who had not. Participants who had experienced a stroke exhibited higher AIP levels compared to those who had not (AIP level in stroke group: median 0.58, IQR 0.36–0.81; AIP level in non-stroke group: median 0.50, IQR 0.28–0.74) (Fig. 4A). Additionally, the AIP levels in the stroke group were relatively higher compared to those in the non-stroke group (Supplementary Fig. 1). The prevalence rate of stroke In this study, 719 participants experienced a stroke, resulting in an overall prevalence rate of 19.5% (18.2%-20.7%). In addition, the prevalence rates of those four AIP groups were 14.8% (12.5%-17.1%), 18.5% (16.0%-21.0%), 20.9% (18.3%-23.5%), and 23.9% (21.1%-26.6%) respectively. Participants with elevated AIP levels exhibited a higher prevalence of stroke compared to those with the lowest AIP levels. ( P < 0.0001 for trend) (Table 2 and Fig. 4B). Table 1 The baseline characteristics of participants. Characteristic Q1 Q2 Q3 Q4 P -value Participants 914 913 914 914 Age,years 63.2 ± 13.8 64.2 ± 13.6 66.3 ± 13.4 66.6 ± 13.8 Gender < 0.001 Male 602 (65.9%) 555 (60.8%) 557 (60.9%) 458 (50.1%) Female 312 (34.1%) 358 (39.2%) 357 (39.1%) 456 (49.9%) BMI(kg/m 2 ) 31.3 ± 11.1 31.3 ± 10.9 29.9 ± 11.4 29.6 ± 10.8 < 0.001 Ethnicity(%) 0.049 Black 115 (12.6%) 129 (14.3%) 138 (15.3%) 156 (17.2%) Asian 20 (2.2%) 16 (1.8%) 19 (2.1%) 21 (2.3%) Caucasian 622 (68.3%) 587 (65.1%) 596 (65.9%) 550 (60.6%) Hispanic 99 (10.9%) 81 (9.0%) 84 (9.3%) 103 (11.4%) White 3 (0.3%) 6 (0.7%) 3 (0.3%) 6 (0.7%) Other 52 (5.7%) 83 (9.2%) 64 (7.1%) 71 (7.8%) Comorbid conditions(%) Ketoacidosis 45 (4.9%) 48 (5.3%) 54 (5.9%) 52 (5.7%) 0.793 Hypertension 375 (41.0%) 426 (46.7%) 437 (47.8%) 471 (51.5%) < 0.001 ARF 190 (20.8%) 165 (18.1%) 146 (16.0%) 166 (18.2%) 0.067 COPD 109 (11.9%) 89 (9.7%) 115 (12.6%) 96 (10.5%) 0.202 AMI 152 (16.6%) 165 (18.1%) 160 (17.5%) 154 (16.8%) 0.844 AF 126 (13.8%) 141 (15.4%) 142 (15.5%) 126 (13.8%) 0.546 Cardiac arrest 47 (5.1%) 50 (5.5%) 34 (3.7%) 28 (3.1%) 0.034 CHF 177 (19.4%) 177 (19.4%) 179 (19.6%) 170 (18.6%) 0.954 GB 13 (1.4%) 11 (1.2%) 13 (1.4%) 3 (0.3%) 0.013 CKD 47 (5.1%) 36 (3.9%) 29 (3.2%) 34 (3.7%) 0.177 ESRD 56 (6.1%) 56 (6.1%) 58 (6.3%) 51 (5.6%) 0.915 Sepsis 154 (16.8%) 77 (8.4%) 72 (7.9%) 73 (8.0%) < 0.001 Stroke 135 (14.8%) 169 (18.5%) 191 (20.9%) 218 (23.9%) < 0.001 Cancer 26 (2.8%) 3 (0.3%) 7 (0.8%) 4 (0.4%) < 0.001 Laboratory data Hb(g/dL) 13.7 ± 9.4 12.6 ± 3.0 12.8 ± 4.8 12.8 ± 4.8 < 0.001 Platelet(10^9/L) 231.4 ± 106.4 232.0 ± 94.5 229.3 ± 83.1 238.0 ± 89.2 0.228 ALB(g/dL) 3.1 ± 0.8 3.3 ± 0.7 3.4 ± 0.6 3.4 ± 0.6 < 0.001 Creatinine(mg/dL) 1.3 (0.9–2.2) 1.2 (0.9–1.9) 1.2 (0.9–1.8) 1.1 (0.8–1.7) < 0.001 ALT(U/L) 27.0 (18.0–42.0) 27.5 (18.0–43.0) 26.0 (18.0–39.0) 25.0 (18.0–38.0) 0.242 Lactate(mmol/L) 1.9 (1.2–3.2) 1.9 (1.1–3.2) 1.8 (1.2–2.8) 1.9 (1.1–2.8) < 0.001 Treatment Mechanical ventilation 143 (15.6%) 186 (20.4%) 199 (21.8%) 238 (26.0%) < 0.001 Nitroglycerin 86 (9.6%) 106 (11.6%) 85 (9.3%) 114 (12.5%) 0.277 Glucocorticoids 54 (5.9%) 55 (6.0%) 58 (6.3%) 72 (7.9%) < 0.001 Vancomycin 112 (12.5%) 75 (8.2%) 58 (6.4%) 53 (5.8%) < 0.001 Carbapenem 24 (2.6%) 14 (1.5%) 10 (1.1%) 12 (1.3%) < 0.001 Levofloxacin 44 (4.8%) 29 (3.2%) 32 (3.5%) 29 (3.2%) 0.193 Continuous data are expressed as mean + SD or median(O1–O3). Categorical data are expressed as n(%). One-way ANOVA.Kruskall-Wallis test or chi-square test. BMI, body mass index; ARF,acute respiratory failure; COPD,chronic obstructive pulmonary disease; AMI.acute myocardial infarction; AF.atrial fibrillation; CHF,congestive heart failure; GB,gastrointestinal bleeding; CKD,chronic kidney disease; ESRD,end-stage renal disease; Hb,hemoglobin; ALB,serum albumin; ALT,alanine aminotransferase. Table 2 Prevalence rate of Stroke in DM patients. AIP Participants(n) Stroke (n) Prevalence rate(95%CI)(%) Total 3695 719 19.5 (18.2–20.7) Q1 914 135 14.8 (12.5–17.1) Q2 913 169 18.5(16.0–21.0) Q3 914 191 20.9(18.3–23.5) Q4 914 218 23.9(21.1–26.6) P for trend < 0.001 AIP atherogenic index of plasma; DM diabetes mellitus; n number; Q quarter Relationship between AIP levels and stroke A univariate analysis was carried out on the available data, revealing that age, hypertension, ARF, AMI, AF, ALT, mechanical ventilation were positively linked to stroke, while ketoacidosis, COPD, AMI, Cardiac arrest, CHF, sepsis, creatinine, glucocorticoids, vancomycin and levofloxacin were negatively associated with IS (Supplementary Table 1). The multivariate logistic regression analysis demonstrated a correlation between AIP levels and the occurrence of stroke. In the unadjusted model, a 1-unit increase in AIP was associated with an 80.0% higher risk of stroke (OR = 1.8, 95%CI 1.4–2.2, P<0.001). This association between AIP and stroke remained significant even after adjusting for age, gender, BMI, and ethnicity (OR = 1.6, 95% CI: 1.3-2.0, P < 0.001) (model I), as well as when including baseline characteristics and factors such as cardiac arrest, acute myocardial infarction, gastrointestinal bleeding, cancer, use of antiplatelet drugs, and cholesterol-lowering medications (model II) (OR = 1.4, 95%CI 1.1–1.8, P<0.001) (Table 3 ). Furthermore, when using the lowest quintile as the reference, the highest quintile of AIP was significantly associated with an increased risk of IS. In the crude model, the highest quintile (Q4) had an OR of 1.8 (95% CI: 1.4–2.3, P < 0.001). This association remained significant in Model I (Q4: OR = 1.6, 95% CI: 1.3–2.1, P < 0.001) and Model II (Q4: OR = 1.4, 95% CI: 1.1–1.8, P < 0.001) (Table 3 ). Table 3 Relationship between AIP and stroke in different models Variable Crude model (OR,95%CI, P) Model I(OR,95%CI, P) Model II (OR,95%CI, P) Model III (OR,95%CI, P) AIP 1.8 (1.4, 2.2) < 0.001 1.6 (1.3, 2.0) < 0.001 1.4 (1.1, 1.8) 0.007 1.4 (1.1, 1.8) 0.007 AIP(quartile) Q1 Ref. Ref. Ref. Ref. Q2 1.3 (1.0, 1.7) 0.032 1.3 (1.0, 1.6) 0.083 1.2 (0.9, 1.5) 0.279 1.2 (0.9, 1.5) 0.278 Q3 1.5 (1.2, 1.9) < 0.001 1.4 (1.1, 1.8) 0.007 1.3 (1.0, 1.7) 0.063 1.3 (1.0, 1.7) 0.063 Q4 1.8 (1.4, 2.3) < 0.001 1.6 (1.3, 2.1) < 0.001 1.4 (1.1, 1.8) 0.010 1.4 (1.1, 1.8) 0.009 P for trend < 0.001 < 0.001 0.007 0.007 Crude model: we did not adjust other covariants Model I: we adjusted age, gender, BMI, ethnicity. Model II: we adjusted age, gender, BMI, ethnicity; cardiac arrest; acute myocardial infarction; gastrointestinal bleeding; cancer; Antiplatelet drugs; cholesterol-lowering drug. Model III: All variables listed in Model II were adjusted. However, continuous variables (age, BMI) were adjusted as non-linearity OR, odds ratios; CI: confidence, Ref: reference; AIP: Atherogenic index of plasma Sensitivity analysis The authors utilized a GAM to incorporate the continuous covariate into the equation as a curve within the fully adjusted model. (Model III, OR = 1.4, 95%CI 1.1–2.8, P<0.001) (Table 3 ). In addition, considering excluding outliers in the AIP potentially impacted the relationship between AIP and stroke, the authors also analyzed the relationship of the AIP with stroke in sensitivity analysis without excluding outliers of AIP (Supplementary Table 2). The results from all sensitivity analyses demonstrated the robustness of the relationship between AIP and stroke. We conducted sensitivity analyses to evaluate the impact of gender, age, BMI, and hypertension on the association between AIP levels and stroke. The AIP level remained independently associated with stroke regardless of gender, age, BMI and the presence of hypertension (Table 3 ). The nonlinearity addressed by the GAM model GAM and smooth curve fitting were employed to examine the relationship between AIP and stroke. After adjusting for confounding variables (age, gender, BMI, ethnicity, cardiac arrest, acute myocardial infarction, gastrointestinal bleeding, cancer, antiplatelet drugs and cholesterol-lowering drug), a non-linear relationship between AIP and stroke was identified (log likelihood ratio test P < 0.001) (Fig. 5). The inflection point of AIP was 0.8. To the right of the inflection point, the effect size was 1.7 (95% CI: 1.2–2.5; P < 0.001). Conversely, to the left of the inflection point, no significant association between AIP and stroke was observed (OR = 0.9, 95%CI: 0.5–1.8; P = 0.741) (Table 4 ). Table 4 The result of the two-piecewise linear regression model Stroke (OR,95%CI, P ) Fitting model by standard linear regression 1.4 (1.1, 1.8) 0.007 Fitting model by two-piecewise linear regression Inflection point of AIP 0.8 ≤0.8 1.7 (1.2, 2.5) 0.004 > 0.8 0.9 (0.5, 1.8) 0.741 P for the log-likelihood ratio test 0.150 OR, odds ratios; CI: confidence, Ref: reference; AIP: Atherogenic index of plasma We adjusted age, gender, BMI, ethnicity; cardiac arrest; acute myocardial infarction; gastrointestinal bleeding; cancer; Antiplatelet drugs; cholesterol-lowering drug. The results of subgroup analyses We performed sensitivity analyses to determine whether age, BMI, the gender, the presence of cardiac arrest and hypertension influenced the relationship between the AIP level and the stroke. In Table 6 showed that gender and cardiac arrest could modify the relationship between AIP and stroke ( All P for interaction < 0.05). And a stronger association was observed in males (OR = 2.3, 95%CI: 1.6–3.2) and participants with cardiac arrest (OR = 7.0, 95%CI: 7.0-28.9) (Table 5 ). The AIP level remained independently associated with stroke regardless of the age, BMI and the presence of hypertension (Table 5 ). Table 5 Effect size of API on Stroke in prespecified and exploratory subgroups Characteristic No of participants OR (95%CI) P -value P for interacion Age, years <60 1207 2.1 (1.3, 3.3) 0.0013 0.2984 ≥60 2488 1.6 (1.2, 2.1) 0.0008 Gender 0.0387 Male 2187 2.3 (1.6, 3.2) 0.0377 Female 1508 1.4 (1.0, 1.9) < 0.0001 BMI (kg/m 2 ) 0.5497 <18.5 57 1.3 (1.0, 1.8) 0.0410 ≥18.5, < 23.9 645 1.4 (1.1, 2.4) 0.0198 ≥23.9 2993 1.6 (1.3, 2.1) 0.0002 Cardiac arrest No 3535 1.7 (1.3, 2.1) < 0.0001 0.0400 Yes 160 7.0 (1.7, 28.9) 0.0070 Hypertension No 1969 1.7 (1.2, 2.5) 0.0073 0.8100 Yes 1726 1.6 (1.2, 2.1) 0.0023 Notel: The above model was adjusted for age, gender, BMI, ethnicity; cardiac arrest; acute myocardial infarction; gastrointestinal bleeding; cancer; Antiplatelet drugs; cholesterol-lowering drug. Cl,confidence interval OR,odds ratio; BMI body mass index. Discussion In this retrospective multicenter cohort study, derived from the eICU-CRD v2.0, encompassing 3695 individuals diagnosed with DM. We investigated the correlation between cardiometabolic indices, specifically AIP, and the occurrence of stroke. Our analysis revealed a significant association between elevated AIP levels and heightened stroke occurrence in critically ill DM patients. Even after adjusting for confounding risk factors, AIP maintained a robust correlation with stroke incidence within this patient cohort. In our research, we observed a non-linear correlation between AIP levels and IS. Therefore, our findings suggest that AIP possesses potential utility as a pivotal tool in clinical decision-making and may serve as a stand-alone risk indicator for IS in critically ill patients with DM. The AIP, serving as a simple and easily obtainable marker, combines HDL-C and TG concentrations, offering a more comprehensive insight into dyslipidemia ( 14 ). Earlier investigations have examined the association between AIP and the occurrence of IS. Liu et al. conducted a cohort investigation involving 1,463 patients hospitalized for acute ischemic stroke. Their findings revealed a more pronounced connection linking heightened AIP levels and adverse outcomes in ischemic stroke ( 15 ). More importantly, a high cumulative AIP correlates with an elevated risk of IS, suggesting that continuous monitoring and maintenance of an optimal AIP level could aid in ischemic stroke prevention ( 16 ). Furthermore, a statistical examination within the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study indicated that elevated AIP levels might function as a reliable biomarker for forecasting cardiovascular events in individuals with T2DM( 17 ). Nonetheless, investigations into the relationship between AIP and stroke incidence among diabetic patients in ICU is rare. The increased mortality rate among ICU patients arises from a range of risk factors, particularly notable are cardiovascular events such as stroke and acute myocardial infarction. Diabetes is primarily characterized by insulin resistance and often coexists with cardiovascular diseases ( 18 , 19 ). Therefore, identifying a dependable prognostic and diagnostic marker is crucial for improving preventive healthcare for individuals at high risk. AIP, which evaluates lipid and glucose metabolism, has been regarded as a predictive marker for cardiovascular disease ( 20 , 21 ). In this retrospective cohort study conducted across multiple centers involving 3695 participants, AIP values emerged independently as a risk factor for the incidence of IS. AIP value had a significant correlation with incidence of IS after adjustment for potential confounders (OR = 1.4, 95%CI 1.1–1.8, P<0.001). These findings underscore the importance of maintaining optimal AIP levels to potentially prevent IS. The exact mechanism underlying the between AIP values and IS remains elusive, but this may be related to atherosclerosis ( 16 ). The relationship between AIP value and the risk of IS may be clarified by considering the following factors. First, small dense low-density lipoprotein (sdLDL) refers to a subtype of LDL with a smaller particle size and higher density compared to traditional LDL. It is more prone to penetrate the blood vessel wall and form plaques within the arteries, hence it is considered a risk factor for atherosclerosis and cardiovascular diseases ( 22 , 23 ). AIP acts as a surrogate for sdLDL particles and demonstrates a negative correlation with the size of LDL-C particles( 24 ). An elevation in AIP suggests an augmented presence of sdLDL, thereby facilitating the development of atherosclerotic plaques ( 25 ). AIP plays a pivotal role in modulating the reverse cholesterol transport process, which is implicated in the recycling or elimination of surplus cholesterol. Elevated levels of AIP may indicate excessive storage of triglycerides by adipocytes, culminating in heightened accumulation of cholesterol crystals within the inner layers of atherosclerotic arteries. Consequently, this leads to luminal constriction, obstruction, and ultimately, the formation of atherosclerosis( 26 ).Second, Atherosclerosis heightens the likelihood of various chronic metabolic conditions such as metabolic syndrome, diabetes mellitus, and hypertension, all significant risk factors for ischemic stroke( 27 ). The confluence of these factors may compound the risk of ischemic stroke. Furthermore, atherosclerosis correlates with platelet adhesion, activation, and aggregation, potentially causing disruptions in hemodynamics and subsequent occlusion of cerebral arteries. The present study observed a non-linear relationship between AIP level and IS, with an inflection point at 0.8 after adjusting for confounders. When the AIP level was ≤ 0.8, a 1 unit increase in the AIP ratio was associated with a 70% increase in the incidence of IS (OR = 1.7, 95% CI 1.2–2.5, P = 0.004). However, when the AIP level was > 0.8, there was no correlation between the AIP level and incident IS (OR = 0.9, 95% CI 0.5–1.8, P = 0.741). This may be because other variables, aside from the AIP level, also affected IS. As seen in Table S4, compared to participants with an AIP level ≤ 0.8, those with an AIP level > 0.8 generally had higher BMI, and a higher proportion of males, AMI, AF, and ESRD, which are closely related to IS. When the AIP level is greater than 0.8, the presence of these IS risk factors may weaken the relative effect of the AIP ratio on IS risk. Our study possesses several strengths. Firstly, this study involved a multicenter cohort investigation, encompassing a substantial sample of ICU patients across the USA. Secondly, our study identified a non-linear relationship between AIP values and IS in DM patients in the ICU, concurrently determined the inflection point. Thirdly, to confirm the robustness of our findings, we conducted various sensitivity analyses. These included transforming the AIP values into categorical variables, incorporating continuous covariates into the model using GAM analysis, and examining the relationship between AIP and stroke without excluding AIP outliers in the sensitivity analysis. Fourthly, a subgroup analysis was conducted to verify the robustness of the relationship between AIP values and IS across different participant groups, confirming the consistency of the results. This study presents several limitations. Firstly, our research exclusively concentrates on ICU patients in the USA, limiting the generalizability of our findings to patients in other departments and across diverse regions and ethnicities. Secondly, as a retrospective study, the link between AIP values and IS among ICU patients with diabetes mellitus (DM) requires validation through prospective investigations. Conclusions In summary, elevated AIP levels in ICU patients with DM were linked to an increased risk of IS. This investigation demonstrates a positive and non-linear correlation between AIP levels and IS incidence among DM patients in the ICU. There appears to be a threshold effect in the relationship between AIP levels and IS. AIP levels below 0.8 are positively associated with the incidence of IS. This discovery is expected to assist clinicians in managing AIP levels effectively. Lowering the AIP level below 0.8 could notably mitigate the risk of IS progression. Consequently, utilizing the AIP profile may aid in identifying at-risk patients within a crucial time window, enabling prompt and optimized therapy to improve patient outcomes. Abbreviations AIP Atherogenic Index of Plasma IS ischemic stroke ICU Intensive Care Unit OR effect sizes T2DM Type 2 diabetes mellitus IR insulin resistance HDL high-density lipoprotein TG triglycerides SD standard deviation BMI body mass index CKD chronic kidney disease ARF acute respiratory failure AMI acute myocardial infarction AF atrial fibrillation COPD chronic obstructive pulmonary disease ESRD end-stage renal disease CHF congestive heart failure GB gastrointestinal bleeding ALB serum albumin HB hemoglobin IQR interquartile ranges OR odds ratios CI confidence intervals ACCORD Action to Control Cardiovascular Risk in Diabetes sdLDL small dense low-density lipoprotein Declarations Data Availability statement The original data was obtained from eICU-CRD v2.0 (https://eicu-crd.mit.edu/), an open database. Therefore, informed consent can be waived. The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors. Ethics statement Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. Authors’ contributors LLW and ZHS contributed to the conception and design of the study. XLC was responsible for data analysis, LLW, ZHS, and HFH were responsible for data interpretation. LLW and ZHS wrote the original draft and QJW verified the data. All authors were involved in the reviewing and editing of the manuscript and approved the final version. Funding This study was supported by National Natural Science Foundation of China (grant number. 82100710), Guangdong Basic and Applied Basic Research Foundation (grant number.2020A1515110398), Shenzhen Science and Technology Program (grant number. RCBS20210609103234061), Shenzhen High-level Hospital Construction Fund and Shenzhen Key Medical Discipline Construction Fund (grant number. SZXK009) and Sanming Project of Medicine in Shenzhen (grant number. SZSM202211013) Acknowledgements None. Conflict of interest Author XC was employed by Empower U, X&Y Solutions Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. References Zhao Y, Zhuang Z, Li Y, Xiao W, Song Z, et al. Elevated blood remnant cholesterol and triglycerides are causally related to the risks of cardiometabolic multimorbidity. Nature communications . (2024); 1(15):2451. Liu Y, Kong Y, Yan Y , Hui P. Explore the value of carotid ultrasound radiomics nomogram in predicting ischemic stroke risk in patients with type 2 diabetes mellitus. Frontiers in endocrinology . (2024); 15):1357580. Koton S, Pike JR, Johansen M, Knopman DS, Lakshminarayan K, et al. Association of Ischemic Stroke Incidence, Severity, and Recurrence With Dementia in the Atherosclerosis Risk in Communities Cohort Study. JAMA neurology . (2022); 3(79):271-280. Johansen MC, Chen J, Schneider ALC, Carlson J, Haight T, et al. Association Between Ischemic Stroke Subtype and Stroke Severity: The Atherosclerosis Risk in Communities Study. Neurology . (2023); 9(101):e913-e921. Li W, Chen D, Tao Y, Lu Z , Wang D. Association between triglyceride-glucose index and carotid atherosclerosis detected by ultrasonography. Cardiovascular diabetology . (2022); 1(21):137. Wakino S, Minakuchi H, Miya K, Takamatsu N, Tada H, et al. Aldosterone and Insulin Resistance: Vicious Combination in Patients on Maintenance Hemodialysis. Therapeutic apheresis and dialysis : official peer-reviewed journal of the International Society for Apheresis, the Japanese Society for Apheresis, the Japanese Society for Dialysis Therapy . (2018); 2(22):142-151. Stitziel NO, Kanter JE , Bornfeldt KE. Emerging Targets for Cardiovascular Disease Prevention in Diabetes. Trends in molecular medicine . (2020); 8(26):744-757. Pala AA , Urcun YS. Effect of calculated plasma osmolality and atherogenic index of plasma on carotid artery blood flow velocities. Vascular . (2021); 4(29):527-534. Zhang Y, Chen S, Tian X, Xu Q, Xia X, et al. Elevated atherogenic index of plasma associated with stroke risk in general Chinese. Endocrine . (2024), 10.1007/s12020-023-03677-0 Jiang L, Li L, Xu Z, Tang Y, Zhai Y, et al. Non-linear associations of atherogenic index of plasma with prediabetes and type 2 diabetes mellitus among Chinese adults aged 45 years and above: a cross-sectional study from CHARLS. Frontiers in endocrinology . (2024); 15):1360874. Ma X, Sun Y, Cheng Y, Shen H, Gao F, et al. Prognostic impact of the atherogenic index of plasma in type 2 diabetes mellitus patients with acute coronary syndrome undergoing percutaneous coronary intervention. Lipids in health and disease . (2020); 1(19):240. Pollard TJ, Johnson AEW, Raffa JD, Celi LA, Mark RG, et al. The eICU Collaborative Research Database, a freely available multi-center database for critical care research. Scientific data . (2018); 5):180178. Wu L, Pu H, Zhang M, Hu H , Wan Q. Non-linear relationship between the body roundness index and incident type 2 diabetes in Japan: a secondary retrospective analysis. Journal of translational medicine . (2022); 1(20):110. Yan H, Zhou Q, Wang Y, Tu Y, Zhao Y, et al. Associations between cardiometabolic indices and the risk of diabetic kidney disease in patients with type 2 diabetes. Cardiovascular diabetology . (2024); 1(23):142. Liu H, Liu K, Pei L, Li S, Zhao J, et al. Atherogenic Index of Plasma Predicts Outcomes in Acute Ischemic Stroke. Frontiers in neurology . (2021); 12):741754. Zheng H, Wu K, Wu W, Chen G, Chen Z, et al. Relationship between the cumulative exposure to atherogenic index of plasma and ischemic stroke: a retrospective cohort study. Cardiovascular diabetology . (2023); 1(22):313. Fu L, Zhou Y, Sun J, Zhu Z, Xing Z, et al. Atherogenic index of plasma is associated with major adverse cardiovascular events in patients with type 2 diabetes mellitus. Cardiovascular diabetology . (2021); 1(20):201. Beckman JA, Paneni F, Cosentino F , Creager MA. Diabetes and vascular disease: pathophysiology, clinical consequences, and medical therapy: part II. European heart journal . (2013); 31(34):2444-52. Booth GL, Kapral MK, Fung K , Tu JV. Relation between age and cardiovascular disease in men and women with diabetes compared with non-diabetic people: a population-based retrospective cohort study. Lancet (London, England) . (2006); 9529(368):29-36. Leong DP, Joseph PG, McKee M, Anand SS, Teo KK, et al. Reducing the Global Burden of Cardiovascular Disease, Part 2: Prevention and Treatment of Cardiovascular Disease. Circ Res . (2017); 6(121):695-710. Elam MB, Ginsberg HN, Lovato LC, Corson M, Largay J, et al. Association of Fenofibrate Therapy With Long-term Cardiovascular Risk in Statin-Treated Patients With Type 2 Diabetes. JAMA cardiology . (2017); 4(2):370-380. Higashioka M, Sakata S, Honda T, Hata J, Yoshida D, et al. Small Dense Low-Density Lipoprotein Cholesterol and the Risk of Coronary Heart Disease in a Japanese Community. Journal of atherosclerosis and thrombosis . (2020); 7(27):669-682. Higashioka M, Sakata S, Honda T, Hata J, Shibata M, et al. The Association of Small Dense Low-Density Lipoprotein Cholesterol and Coronary Heart Disease in Subjects at High Cardiovascular Risk. Journal of atherosclerosis and thrombosis . (2021); 1(28):79-89. Davì G , Patrono C. Platelet activation and atherothrombosis. N Engl J Med . (2007); 24(357):2482-94. Si Y, Fan W, Han C, Liu J , Sun L. Atherogenic Index of Plasma, Triglyceride-Glucose Index and Monocyte-to-Lymphocyte Ratio for Predicting Subclinical Coronary Artery Disease. The American journal of the medical sciences . (2021); 3(362):285-290. Lioy B, Webb RJ , Amirabdollahian F. The Association between the Atherogenic Index of Plasma and Cardiometabolic Risk Factors: A Review. Healthcare (Basel, Switzerland) . (2023); 7(11): Walter K. What Is Acute Ischemic Stroke? Jama . (2022); 9(327):885. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5638991","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":392392564,"identity":"56344097-85f6-433a-8fdf-d0ca9d09725b","order_by":0,"name":"Liling Wu","email":"","orcid":"","institution":"Department of Nephrology, The First Affiliated Hospital of Shenzhen University, Shenzhen 518000, Guangdong Province","correspondingAuthor":false,"prefix":"","firstName":"Liling","middleName":"","lastName":"Wu","suffix":""},{"id":392392565,"identity":"b8e70beb-984d-4b1f-8c25-15ce7c9dd5cc","order_by":1,"name":"Zhihang Su","email":"","orcid":"","institution":"Department of Nephrology, The First Affiliated Hospital of Shenzhen University, Shenzhen 518000, Guangdong Province","correspondingAuthor":false,"prefix":"","firstName":"Zhihang","middleName":"","lastName":"Su","suffix":""},{"id":392392566,"identity":"4b2edcbc-1d82-4931-ba6c-889a250583b9","order_by":2,"name":"Xingling Chen","email":"","orcid":"","institution":"Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan","correspondingAuthor":false,"prefix":"","firstName":"Xingling","middleName":"","lastName":"Chen","suffix":""},{"id":392392567,"identity":"4739b291-19a1-4fde-b746-3f155dbc2494","order_by":3,"name":"Haofei Hu","email":"","orcid":"","institution":"Department of Nephrology, The First Affiliated Hospital of Shenzhen University, Shenzhen 518000, Guangdong Province","correspondingAuthor":false,"prefix":"","firstName":"Haofei","middleName":"","lastName":"Hu","suffix":""},{"id":392392568,"identity":"ae999f68-b75e-44f8-aa5b-5c8faf311fc6","order_by":4,"name":"Qijun Wan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYDACZjBpI8fPcPgAQwIJWtKMJRuPJRCpBQIOJ244fMaAOLUGx3nMJH7uYGZsOHbm84eHO+wY+Nu78VtmcJjHTLL3DBszY8/ZbRKJZ5IZJM6c3UBQiwRvGw8bs8TZbQyJbcwMBhK5hLVI/m2T4GGTf/P4Q2JbPXFapHnbDCR4GM4wSCS2HSasRfIwW7G1bFuCgQTDMTOgluM8BP3Cd/7wxptv2/7X7z9w+PHHn23Vcvztvfi1KBzgQI0OHrzKQUC+gf0BQUWjYBSMglEwwgEAkcxJOJJldooAAAAASUVORK5CYII=","orcid":"","institution":"Department of Nephrology, The First Affiliated Hospital of Shenzhen University, Shenzhen 518000, Guangdong Province","correspondingAuthor":true,"prefix":"","firstName":"Qijun","middleName":"","lastName":"Wan","suffix":""}],"badges":[],"createdAt":"2024-12-13 14:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5638991/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5638991/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72336197,"identity":"8d5d0968-6a0c-49ff-b9cb-c532b8930f33","added_by":"auto","created_at":"2024-12-25 15:46:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":19767,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy design and participant flow\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5638991/v1/8708c275adbd22c745fdb33a.png"},{"id":72336198,"identity":"83c5ce99-6060-4750-a7ab-8989f019e69f","added_by":"auto","created_at":"2024-12-25 15:46:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":11009,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of AIP.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt presented a skewed distribution while being in the range from -0.6 to 1.7.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5638991/v1/488b918559299579cddd4d03.png"},{"id":72336199,"identity":"a96e918c-88ef-4e51-9023-b22f338b112e","added_by":"auto","created_at":"2024-12-25 15:46:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":9348,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStroke prevalence of age stratification by 10 intervals in DM patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn age stratification by 10 intervals, the prevalence of stroke increases with age and is more common in men than women.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5638991/v1/59c5a22366157d82270afeaf.png"},{"id":72336200,"identity":"8be44925-41a5-4529-bbc2-d7203b454c2b","added_by":"auto","created_at":"2024-12-25 15:46:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":19250,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eElevation of AIP and its relationship with incident Stroke in DM patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Levels of AIP in baseline were determined in DM patients categorized by stroke. Horizontal lines represent the median values and interquartile ranges. Kruskal-Wallistest. ***, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001. (B)\u003cstrong\u003e \u003c/strong\u003ePrevalence of Pre-DM stratified according to AIP categories. Linear regression analysis, \u003cem\u003eP \u003c/em\u003efor trend \u0026lt;0.001.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5638991/v1/36ff10606c4aec0f912e291e.png"},{"id":72336208,"identity":"76edaea2-be70-475d-8a37-cb4127e80041","added_by":"auto","created_at":"2024-12-25 15:46:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":30492,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of AIP with the risk of Stoke in DM patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe non-linear relationship between AIP and incident of Stoke in DM patients. A non-linear relationship between them was detected after adjusting age, gender, BMI, ethnicity; cardiac arrest; acute myocardial infarction; gastrointestinal bleeding; cancer; Antiplatelet drugs; cholesterol-lowering drug.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5638991/v1/3588d4ed3610501a6c5c4626.png"},{"id":90071351,"identity":"813a8dd7-439f-468c-8eb7-a20b192ef84f","added_by":"auto","created_at":"2025-08-28 07:02:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1846280,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5638991/v1/345a2e86-99db-4cd2-8197-4528bc53865f.pdf"},{"id":72337022,"identity":"f9a9c3bf-d558-4a0d-bfdd-7e137ae60a49","added_by":"auto","created_at":"2024-12-25 15:54:45","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":41243,"visible":true,"origin":"","legend":"","description":"","filename":"STable.docx","url":"https://assets-eu.researchsquare.com/files/rs-5638991/v1/f32415d6869b3e44ccff7a70.docx"},{"id":72336211,"identity":"7cb7caf6-d00c-461a-a5e1-6cd8a87d25f7","added_by":"auto","created_at":"2024-12-25 15:46:45","extension":"ppt","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":131072,"visible":true,"origin":"","legend":"","description":"","filename":"SF.ppt","url":"https://assets-eu.researchsquare.com/files/rs-5638991/v1/09ffbbe981079b3640e97d00.ppt"}],"financialInterests":"No competing interests reported.","formattedTitle":"Non-linear relationship between the atherogenic index of plasma and ischemic stroke in the diabetic population in ICU: A multicenter retrospective cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiometabolic disorders, such as type 2 diabetes and stroke, continue to be the leading causes of premature mortality globally (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Ischemic stroke (IS), with its significant global impact on mortality, often results in incapacitating events (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Atherosclerosis is the primary cause of cerebral infarction, which is the common cause of IS (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Type 2 diabetes mellitus (T2DM), primarily driven by insulin resistance (IR), not only damages vascular endothelial function, leading to plaque formation and rupture in the carotid artery (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), but also significantly contributes to the incidence of IS (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCardiometabolic markers, like the atherogenic index of plasma (AIP), integrate high-density lipoprotein (HDL) and triglycerides (TG) levels, providing a more comprehensive insight into dyslipidemia. Previous findings reveal the correlation between the augmentation of carotid artery blood flow velocity and the increase in AIP value. Elevated atherosclerosis index is commonly indicative of exacerbated atherosclerosis, compromised vascular function, and impaired blood flow, thereby heightening the susceptibility to cardiovascular events (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In prior studies, it was demonstrated that heightened levels of baseline and long-term updated mean AIP were linked to an increased risk of stroke (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Subsequent research revealed the positive correlations between AIP and the risk of prediabetes and T2DM in individuals aged over 45 in China (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Furthermore, a higher AIP value identified upon admission was independently and strongly correlated with adverse cardiovascular events in patients diagnosed with T2DM (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe diabetic population among ICU patients with IS generally exhibits several notable characteristics: increased morbidity and mortality, a higher incidence of comorbidities, and worse neurological outcomes. Effective management and treatment of diabetic patients with IS in the ICU are crucial to address these challenges. However, it remains unknown whether this association between AIP and IS persists in the diabetic population among ICU patients, who generally exhibit more severe pathophysiological conditions. Therefore, evaluating whether the AIP index can function as a potential predictor for the incidence of stroke among diabetic ICU patients could aid in identifying those at elevated stroke risk, thereby enhancing healthcare management or timely intervention. We hypothesized that AIP index is a correlation between the AIP index an increased risk of IS among DM patients in ICU settings. Hence, our study aims to analyze the eICU Collaborative Research Database v2.0 (eICU-CRD v2.0) database to evaluate the significance of the AIP value in predicting the incidence of stroke among the diabetic population in ICU.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy population\u003c/h2\u003e\n \u003cp\u003eThis retrospective study investigated data obtained from the eICU-CRD v2.0, which is a multicenter retrospective cohort study. The database comprises medical records of 200,859 ICU patients from 335 ICUs across 208 hospitals (both academic and non-academic) in the USA during the years 2014\u0026ndash;2015. (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e). The exclusion criteria were as follows: (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e) patients without DM (n\u0026thinsp;=\u0026thinsp;181267); (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e) patients without TG or HDL were extracted (n\u0026thinsp;=\u0026thinsp;15897); (\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e) patients with extreme values of log(TG/HDL) (n\u0026thinsp;=\u0026thinsp;40) (the extreme value\u0026thinsp;=\u0026thinsp;mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD))(\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e). Finally, 3695 participants with Diabetic were included in this analysis, including 719 participants with stroke and 2976 participants without stroke. Figure\u0026nbsp;1 illustrates the study design and participant flow.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eThe variables extracted from eICU-CRD v2.0 included the following: 1) demographic characteristics: body mass index (BMI), age, ethnicity and gender; 2) comorbidities: chronic kidney disease (CKD), hypertension, diabetes mellitus, acute respiratory failure (ARF), ketoacidosis, acute myocardial infarction (AMI), atrial fibrillation (AF), chronic obstructive pulmonary disease (COPD), end-stage renal disease (ESRD), cardiac arrest, congestive heart failure (CHF), sepsis, stroke, gastrointestinal bleeding (GB), and cancer; 3) laboratory parameters: serum albumin (ALB), triglycerides, hemoglobin (HB), platelet count, HDL, serum creatinine, cholesterol, ALT, and lactate; and 4) treatment: nitroglycerin, levofloxacin, glucocorticoids, vancomycin, carbapenem, and mechanical ventilation. AIP was treated as a continuous variable and calculated using the formula: AIP\u0026thinsp;=\u0026thinsp;log(TG/HDL). This calculation was based on the baseline levels of TG and HDL (\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e). The outcome variable was stroke in-hospital in participants with diabetic.\u003c/p\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eParticipants were categorized into quintiles based on their AIP values. Continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median with interquartile ranges (IQR). Categorical variables were reported as percentages. Differences among the AIP groups were assessed using the \u0026chi;\u0026sup2; test for categorical variables, One-Way ANOVA for normally distributed continuous variables, or the Kruskal-Wallis H test for skewed continuous variables. Prevalence rates were expressed as cumulative prevalence.\u003c/p\u003e\n \u003cp\u003eThe association between AIP and stroke was examined using multiple logistic regression analyses, adjusting for key confounding variables, including gender, ethnicity, cardiac arrest, acute myocardial infarction, age, gastrointestinal bleeding, BMI, cancer, antiplatelet drugs, and cholesterol-lowering drugs. Effect sizes were reported as odds ratios (OR) with 95% confidence intervals (CI). Additionally, a Generalized Additive Model (GAM) was used to include the continuous covariate as a curve in the equation (model III) to ensure the robustness of the results. For sensitivity analyses exploring the association between AIP and stroke, participants were included without excluding AIP outliers.\u003c/p\u003e\n \u003cp\u003eSubgroup analyses were performed to evaluate the consistency of the association between AIP and incident stroke across different subgroups defined by age (\u0026lt;\u0026thinsp;60, \u0026ge;\u0026thinsp;60 years), gender, BMI (\u0026lt;\u0026thinsp;18.5, 18.5 to \u0026lt;\u0026thinsp;23.9, \u0026ge;\u0026thinsp;23.9 kg/m\u0026sup2;), cardiac arrest, and hypertension. Smooth curve fitting and GAM were utilized to illustrate the relationship between AIP and stroke. The inflection point and threshold effect of AIP on stroke incidence were determined using a two-piece linear regression model.\u003c/p\u003e\n \u003cp\u003eStatistical analyses were conducted using the R software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org\u003c/span\u003e\u003c/span\u003e, The R Foundation) and EmpowerStats (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.empowerstats.com\u003c/span\u003e\u003c/span\u003e, X\u0026amp;Y Solutions, Inc., Boston, MA). P-values less than 0.05 (two-sided) were considered statistically significant.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eStatement\u003c/h3\u003e\n\u003cp\u003eWe identify the institutional and/or licensing committee approving the experiments, including any relevant details; (ii) confirm that all experiments were performed in accordance with relevant guidelines and regulations. Our research raw data comes from the eICU-CRD v2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://eicu-crd.mit.edu/\u003c/span\u003e\u003c/span\u003e), an open database. It was approved by the committee for use in research. We confirm that all research was performed in accordance with relevant guidelines/regulations. Informed consent from participants can be waived. Research involving human participants was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eCharacteristics of the participants\u003c/h2\u003e\n \u003cp\u003eThe final analysis included 3695 adult participants (Fig.\u0026nbsp;1). The mean age was 65.1\u0026thinsp;\u0026plusmn;\u0026thinsp;13.7 years, with 2187 (59.2%) being male. The median AIP stood at 0.52 (interquartile range: 0.29\u0026ndash;0.76). Stroke prevalence was 19.46% (719 out of 3695). The participants were categorized by AIP quartiles. Compared with the Q1 group, the highest AIP group (\u0026ge;\u0026thinsp;0.76) exhibited the highest age, platelet count, and ALB levels, along with the lowest BMI, Hb, creatinine, and ALT levels. Additionally, this group had a higher proportion of females, Ketoacidosis, Hypertension, AMI, and AF, as well as a lower proportion of ARF, COPD, cardiac arrest, CHF, GB, CKD, ESRD, sepsis, and cancer. The AIP exhibits a normal distribution, spanning from \u0026minus;\u0026thinsp;0.6 to 1.7, with a mean of 0.54 (Fig.\u0026nbsp;2). When stratified by age into 10 intervals, stroke prevalence rises with age and is more prevalent in men than in women (Fig.\u0026nbsp;3).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eMarked elevation in the AIP level in participants with stroke\u003c/h3\u003e\n\u003cp\u003eWe categorized participants into two groups: those who had experienced a stroke and those who had not. Participants who had experienced a stroke exhibited higher AIP levels compared to those who had not (AIP level in stroke group: median 0.58, IQR 0.36\u0026ndash;0.81; AIP level in non-stroke group: median 0.50, IQR 0.28\u0026ndash;0.74) (Fig.\u0026nbsp;4A). Additionally, the AIP levels in the stroke group were relatively higher compared to those in the non-stroke group (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e\n\u003ch3\u003eThe prevalence rate of stroke\u003c/h3\u003e\n\u003cp\u003eIn this study, 719 participants experienced a stroke, resulting in an overall prevalence rate of 19.5% (18.2%-20.7%). In addition, the prevalence rates of those four AIP groups were 14.8% (12.5%-17.1%), 18.5% (16.0%-21.0%), 20.9% (18.3%-23.5%), and 23.9% (21.1%-26.6%) respectively. Participants with elevated AIP levels exhibited a higher prevalence of stroke compared to those with the lowest AIP levels. (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 for trend) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig. 4B).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe baseline characteristics of participants.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParticipants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e914\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e913\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e914\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e914\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge,years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e63.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e64.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e66.3\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e66.6\u0026thinsp;\u0026plusmn;\u0026thinsp;13.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e602 (65.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e555 (60.8%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e557 (60.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e458 (50.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e312 (34.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e358 (39.2%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e357 (39.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e456 (49.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e31.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e31.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e29.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e29.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthnicity(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.049\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e115 (12.6%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e129 (14.3%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e138 (15.3%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e156 (17.2%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e20 (2.2%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e16 (1.8%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e19 (2.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e21 (2.3%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaucasian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e622 (68.3%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e587 (65.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e596 (65.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e550 (60.6%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e99 (10.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e81 (9.0%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e84 (9.3%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e103 (11.4%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3 (0.3%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6 (0.7%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3 (0.3%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6 (0.7%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e52 (5.7%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e83 (9.2%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e64 (7.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e71 (7.8%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComorbid conditions(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKetoacidosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e45 (4.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e48 (5.3%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e54 (5.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e52 (5.7%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.793\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e375 (41.0%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e426 (46.7%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e437 (47.8%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e471 (51.5%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eARF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e190 (20.8%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e165 (18.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e146 (16.0%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e166 (18.2%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.067\u003c/strong\u003e\u003c/p\u003e\n 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(15.5%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e126 (13.8%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCardiac arrest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e47 (5.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e50 (5.5%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e34 (3.7%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e28 (3.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n 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align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e135 (14.8%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e169 (18.5%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e191 (20.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e218 (23.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e26 (2.8%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3 (0.3%)\u003c/strong\u003e\u003c/p\u003e\n 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\u003cp\u003e\u003cstrong\u003e231.4\u0026thinsp;\u0026plusmn;\u0026thinsp;106.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e232.0\u0026thinsp;\u0026plusmn;\u0026thinsp;94.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e229.3\u0026thinsp;\u0026plusmn;\u0026thinsp;83.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e238.0\u0026thinsp;\u0026plusmn;\u0026thinsp;89.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.228\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALB(g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCreatinine(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.3 (0.9\u0026ndash;2.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.2 (0.9\u0026ndash;1.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.2 (0.9\u0026ndash;1.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.1 (0.8\u0026ndash;1.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALT(U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e27.0 (18.0\u0026ndash;42.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e27.5 (18.0\u0026ndash;43.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e26.0 (18.0\u0026ndash;39.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e25.0 (18.0\u0026ndash;38.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.242\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLactate(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.9 (1.2\u0026ndash;3.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.9 (1.1\u0026ndash;3.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.8 (1.2\u0026ndash;2.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.9 (1.1\u0026ndash;2.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMechanical ventilation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e143 (15.6%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e186 (20.4%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e199 (21.8%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e238 (26.0%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNitroglycerin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e86 (9.6%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e106 (11.6%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e85 (9.3%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e114 (12.5%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.277\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlucocorticoids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e54 (5.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e55 (6.0%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e58 (6.3%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e72 (7.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVancomycin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e112 (12.5%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e75 (8.2%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e58 (6.4%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e53 (5.8%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCarbapenem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e24 (2.6%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e14 (1.5%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e10 (1.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e12 (1.3%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLevofloxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e44 (4.8%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e29 (3.2%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e32 (3.5%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e29 (3.2%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.193\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eContinuous data are expressed as mean\u0026thinsp;+\u0026thinsp;SD or median(O1\u0026ndash;O3).\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eCategorical data are expressed as n(%).\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eOne-way ANOVA.Kruskall-Wallis test or chi-square test.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eBMI, body mass index; ARF,acute respiratory failure; COPD,chronic obstructive pulmonary disease; AMI.acute myocardial infarction; AF.atrial fibrillation; CHF,congestive heart failure; GB,gastrointestinal bleeding; CKD,chronic kidney disease; ESRD,end-stage renal disease; Hb,hemoglobin; ALB,serum albumin; ALT,alanine aminotransferase.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePrevalence rate of Stroke in DM patients.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAIP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParticipants(n)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStroke (n)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePrevalence rate(95%CI)(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.5 (18.2\u0026ndash;20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.8 (12.5\u0026ndash;17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.5(16.0\u0026ndash;21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.9(18.3\u0026ndash;23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.9(21.1\u0026ndash;26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eAIP atherogenic index of plasma; DM diabetes mellitus; n number; Q quarter\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eRelationship between AIP levels and stroke\u003c/h2\u003e\n \u003cp\u003eA univariate analysis was carried out on the available data, revealing that age, hypertension, ARF, AMI, AF, ALT, mechanical ventilation were positively linked to stroke, while ketoacidosis, COPD, AMI, Cardiac arrest, CHF, sepsis, creatinine, glucocorticoids, vancomycin and levofloxacin were negatively associated with IS (Supplementary Table\u0026nbsp;1).\u003c/p\u003e\n \u003cp\u003eThe multivariate logistic regression analysis demonstrated a correlation between AIP levels and the occurrence of stroke. In the unadjusted model, a 1-unit increase in AIP was associated with an 80.0% higher risk of stroke (OR\u0026thinsp;=\u0026thinsp;1.8, 95%CI 1.4\u0026ndash;2.2, P\u0026lt;0.001). This association between AIP and stroke remained significant even after adjusting for age, gender, BMI, and ethnicity (OR\u0026thinsp;=\u0026thinsp;1.6, 95% CI: 1.3-2.0, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (model I), as well as when including baseline characteristics and factors such as cardiac arrest, acute myocardial infarction, gastrointestinal bleeding, cancer, use of antiplatelet drugs, and cholesterol-lowering medications (model II) (OR\u0026thinsp;=\u0026thinsp;1.4, 95%CI 1.1\u0026ndash;1.8, P\u0026lt;0.001) (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Furthermore, when using the lowest quintile as the reference, the highest quintile of AIP was significantly associated with an increased risk of IS. In the crude model, the highest quintile (Q4) had an OR of 1.8 (95% CI: 1.4\u0026ndash;2.3, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This association remained significant in Model I (Q4: OR\u0026thinsp;=\u0026thinsp;1.6, 95% CI: 1.3\u0026ndash;2.1, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Model II (Q4: OR\u0026thinsp;=\u0026thinsp;1.4, 95% CI: 1.1\u0026ndash;1.8, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRelationship between AIP and stroke in different models\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCrude model (OR,95%CI, P)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel I(OR,95%CI, P)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel II (OR,95%CI, P)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel III (OR,95%CI, P)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8 (1.4, 2.2)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6 (1.3, 2.0)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4 (1.1, 1.8) 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4 (1.1, 1.8) 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIP(quartile)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3 (1.0, 1.7) 0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3 (1.0, 1.6) 0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2 (0.9, 1.5) 0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2 (0.9, 1.5) 0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5 (1.2, 1.9)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4 (1.1, 1.8) 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3 (1.0, 1.7) 0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3 (1.0, 1.7) 0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8 (1.4, 2.3)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6 (1.3, 2.1)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4 (1.1, 1.8) 0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4 (1.1, 1.8) 0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e \u003cstrong\u003efor trend\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eCrude model: we did not adjust other covariants\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eModel I: we adjusted age, gender, BMI, ethnicity.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eModel II: we adjusted age, gender, BMI, ethnicity; cardiac arrest; acute myocardial infarction; gastrointestinal bleeding; cancer; Antiplatelet drugs; cholesterol-lowering drug.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eModel III: All variables listed in Model II were adjusted. However, continuous variables (age, BMI) were adjusted as non-linearity\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eOR, odds ratios; CI: confidence, Ref: reference; AIP: Atherogenic index of plasma\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eSensitivity analysis\u003c/h2\u003e\n \u003cp\u003eThe authors utilized a GAM to incorporate the continuous covariate into the equation as a curve within the fully adjusted model. (Model III, OR\u0026thinsp;=\u0026thinsp;1.4, 95%CI 1.1\u0026ndash;2.8, P\u0026lt;0.001) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). In addition, considering excluding outliers in the AIP potentially impacted the relationship between AIP and stroke, the authors also analyzed the relationship of the AIP with stroke in sensitivity analysis without excluding outliers of AIP (Supplementary Table\u0026nbsp;2). The results from all sensitivity analyses demonstrated the robustness of the relationship between AIP and stroke. We conducted sensitivity analyses to evaluate the impact of gender, age, BMI, and hypertension on the association between AIP levels and stroke. The AIP level remained independently associated with stroke regardless of gender, age, BMI and the presence of hypertension (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eThe nonlinearity addressed by the GAM model\u003c/h2\u003e\n \u003cp\u003eGAM and smooth curve fitting were employed to examine the relationship between AIP and stroke. After adjusting for confounding variables (age, gender, BMI, ethnicity, cardiac arrest, acute myocardial infarction, gastrointestinal bleeding, cancer, antiplatelet drugs and cholesterol-lowering drug), a non-linear relationship between AIP and stroke was identified (log likelihood ratio test \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig. 5). The inflection point of AIP was 0.8. To the right of the inflection point, the effect size was 1.7 (95% CI: 1.2\u0026ndash;2.5; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, to the left of the inflection point, no significant association between AIP and stroke was observed (OR\u0026thinsp;=\u0026thinsp;0.9, 95%CI: 0.5\u0026ndash;1.8; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.741) (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe result of the two-piecewise linear regression model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStroke (OR,95%CI, \u003cem\u003eP\u003c/em\u003e )\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFitting model by standard linear regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.4 (1.1, 1.8) 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFitting model by two-piecewise linear regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInflection point of AIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.7 (1.2, 2.5) 0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9 (0.5, 1.8) 0.741\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for the log-likelihood ratio test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003eOR, odds ratios; CI: confidence, Ref: reference; AIP: Atherogenic index of plasma\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003eWe adjusted age, gender, BMI, ethnicity; cardiac arrest; acute myocardial infarction; gastrointestinal bleeding; cancer; Antiplatelet drugs; cholesterol-lowering drug.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eThe results of subgroup analyses\u003c/h2\u003e\n \u003cp\u003eWe performed sensitivity analyses to determine whether age, BMI, the gender, the presence of cardiac arrest and hypertension influenced the relationship between the AIP level and the stroke. In Table 6 showed that gender and cardiac arrest could modify the relationship between AIP and stroke ( All P for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05). And a stronger association was observed in males (OR\u0026thinsp;=\u0026thinsp;2.3, 95%CI: 1.6\u0026ndash;3.2) and participants with cardiac arrest (OR\u0026thinsp;=\u0026thinsp;7.0, 95%CI: 7.0-28.9) (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The AIP level remained independently associated with stroke regardless of the age, BMI and the presence of hypertension (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEffect size of API on Stroke in prespecified and exploratory subgroups\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo of participants\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for interacion\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003cp\u003e\u0026lt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.1 (1.3, 3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2984\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.6 (1.2, 2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0387\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.3 (1.6, 3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.4 (1.0, 1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5497\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.3 (1.0, 1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;18.5, \u0026lt; 23.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.4 (1.1, 2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;23.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.6 (1.3, 2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCardiac arrest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.7 (1.3, 2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0400\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.0 (1.7, 28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.7 (1.2, 2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.6 (1.2, 2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNotel: The above model was adjusted for age, gender, BMI, ethnicity; cardiac arrest; acute myocardial infarction; gastrointestinal bleeding; cancer; Antiplatelet drugs; cholesterol-lowering drug.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eCl,confidence interval OR,odds ratio; BMI body mass index.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective multicenter cohort study, derived from the eICU-CRD v2.0, encompassing 3695 individuals diagnosed with DM. We investigated the correlation between cardiometabolic indices, specifically AIP, and the occurrence of stroke. Our analysis revealed a significant association between elevated AIP levels and heightened stroke occurrence in critically ill DM patients. Even after adjusting for confounding risk factors, AIP maintained a robust correlation with stroke incidence within this patient cohort. In our research, we observed a non-linear correlation between AIP levels and IS. Therefore, our findings suggest that AIP possesses potential utility as a pivotal tool in clinical decision-making and may serve as a stand-alone risk indicator for IS in critically ill patients with DM.\u003c/p\u003e \u003cp\u003eThe AIP, serving as a simple and easily obtainable marker, combines HDL-C and TG concentrations, offering a more comprehensive insight into dyslipidemia (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Earlier investigations have examined the association between AIP and the occurrence of IS. Liu et al. conducted a cohort investigation involving 1,463 patients hospitalized for acute ischemic stroke. Their findings revealed a more pronounced connection linking heightened AIP levels and adverse outcomes in ischemic stroke (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). More importantly, a high cumulative AIP correlates with an elevated risk of IS, suggesting that continuous monitoring and maintenance of an optimal AIP level could aid in ischemic stroke prevention (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Furthermore, a statistical examination within the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study indicated that elevated AIP levels might function as a reliable biomarker for forecasting cardiovascular events in individuals with T2DM(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Nonetheless, investigations into the relationship between AIP and stroke incidence among diabetic patients in ICU is rare.\u003c/p\u003e \u003cp\u003eThe increased mortality rate among ICU patients arises from a range of risk factors, particularly notable are cardiovascular events such as stroke and acute myocardial infarction. Diabetes is primarily characterized by insulin resistance and often coexists with cardiovascular diseases (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Therefore, identifying a dependable prognostic and diagnostic marker is crucial for improving preventive healthcare for individuals at high risk. AIP, which evaluates lipid and glucose metabolism, has been regarded as a predictive marker for cardiovascular disease (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). In this retrospective cohort study conducted across multiple centers involving 3695 participants, AIP values emerged independently as a risk factor for the incidence of IS. AIP value had a significant correlation with incidence of IS after adjustment for potential confounders (OR\u0026thinsp;=\u0026thinsp;1.4, 95%CI 1.1\u0026ndash;1.8, P\u0026lt;0.001). These findings underscore the importance of maintaining optimal AIP levels to potentially prevent IS.\u003c/p\u003e \u003cp\u003eThe exact mechanism underlying the between AIP values and IS remains elusive, but this may be related to atherosclerosis (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The relationship between AIP value and the risk of IS may be clarified by considering the following factors. First, small dense low-density lipoprotein (sdLDL) refers to a subtype of LDL with a smaller particle size and higher density compared to traditional LDL. It is more prone to penetrate the blood vessel wall and form plaques within the arteries, hence it is considered a risk factor for atherosclerosis and cardiovascular diseases (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). AIP acts as a surrogate for sdLDL particles and demonstrates a negative correlation with the size of LDL-C particles(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). An elevation in AIP suggests an augmented presence of sdLDL, thereby facilitating the development of atherosclerotic plaques (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). AIP plays a pivotal role in modulating the reverse cholesterol transport process, which is implicated in the recycling or elimination of surplus cholesterol. Elevated levels of AIP may indicate excessive storage of triglycerides by adipocytes, culminating in heightened accumulation of cholesterol crystals within the inner layers of atherosclerotic arteries. Consequently, this leads to luminal constriction, obstruction, and ultimately, the formation of atherosclerosis(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).Second, Atherosclerosis heightens the likelihood of various chronic metabolic conditions such as metabolic syndrome, diabetes mellitus, and hypertension, all significant risk factors for ischemic stroke(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The confluence of these factors may compound the risk of ischemic stroke. Furthermore, atherosclerosis correlates with platelet adhesion, activation, and aggregation, potentially causing disruptions in hemodynamics and subsequent occlusion of cerebral arteries.\u003c/p\u003e \u003cp\u003eThe present study observed a non-linear relationship between AIP level and IS, with an inflection point at 0.8 after adjusting for confounders. When the AIP level was \u0026le;\u0026thinsp;0.8, a 1 unit increase in the AIP ratio was associated with a 70% increase in the incidence of IS (OR\u0026thinsp;=\u0026thinsp;1.7, 95% CI 1.2\u0026ndash;2.5, P\u0026thinsp;=\u0026thinsp;0.004). However, when the AIP level was \u0026gt;\u0026thinsp;0.8, there was no correlation between the AIP level and incident IS (OR\u0026thinsp;=\u0026thinsp;0.9, 95% CI 0.5\u0026ndash;1.8, P\u0026thinsp;=\u0026thinsp;0.741). This may be because other variables, aside from the AIP level, also affected IS. As seen in Table S4, compared to participants with an AIP level\u0026thinsp;\u0026le;\u0026thinsp;0.8, those with an AIP level\u0026thinsp;\u0026gt;\u0026thinsp;0.8 generally had higher BMI, and a higher proportion of males, AMI, AF, and ESRD, which are closely related to IS. When the AIP level is greater than 0.8, the presence of these IS risk factors may weaken the relative effect of the AIP ratio on IS risk.\u003c/p\u003e \u003cp\u003eOur study possesses several strengths. Firstly, this study involved a multicenter cohort investigation, encompassing a substantial sample of ICU patients across the USA. Secondly, our study identified a non-linear relationship between AIP values and IS in DM patients in the ICU, concurrently determined the inflection point. Thirdly, to confirm the robustness of our findings, we conducted various sensitivity analyses. These included transforming the AIP values into categorical variables, incorporating continuous covariates into the model using GAM analysis, and examining the relationship between AIP and stroke without excluding AIP outliers in the sensitivity analysis. Fourthly, a subgroup analysis was conducted to verify the robustness of the relationship between AIP values and IS across different participant groups, confirming the consistency of the results.\u003c/p\u003e \u003cp\u003eThis study presents several limitations. Firstly, our research exclusively concentrates on ICU patients in the USA, limiting the generalizability of our findings to patients in other departments and across diverse regions and ethnicities. Secondly, as a retrospective study, the link between AIP values and IS among ICU patients with diabetes mellitus (DM) requires validation through prospective investigations.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, elevated AIP levels in ICU patients with DM were linked to an increased risk of IS. This investigation demonstrates a positive and non-linear correlation between AIP levels and IS incidence among DM patients in the ICU. There appears to be a threshold effect in the relationship between AIP levels and IS. AIP levels below 0.8 are positively associated with the incidence of IS. This discovery is expected to assist clinicians in managing AIP levels effectively. Lowering the AIP level below 0.8 could notably mitigate the risk of IS progression. Consequently, utilizing the AIP profile may aid in identifying at-risk patients within a crucial time window, enabling prompt and optimized therapy to improve patient outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAIP \u0026nbsp; \u0026nbsp; \u0026nbsp; Atherogenic Index of Plasma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;ischemic stroke\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eICU \u0026nbsp; \u0026nbsp; \u0026nbsp;Intensive Care Unit\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOR \u0026nbsp; \u0026nbsp; \u0026nbsp; effect sizes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eT2DM \u0026nbsp; \u0026nbsp;Type 2 diabetes mellitus\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;insulin resistance\u003c/p\u003e\n\u003cp\u003eHDL \u0026nbsp; \u0026nbsp; \u0026nbsp;high-density lipoprotein\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTG \u0026nbsp; \u0026nbsp; \u0026nbsp; triglycerides\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSD \u0026nbsp; \u0026nbsp; \u0026nbsp; standard deviation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBMI \u0026nbsp; \u0026nbsp; \u0026nbsp;body mass index \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCKD \u0026nbsp; \u0026nbsp; \u0026nbsp;chronic kidney disease\u003c/p\u003e\n\u003cp\u003eARF \u0026nbsp; \u0026nbsp; \u0026nbsp; acute respiratory failure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAMI \u0026nbsp; \u0026nbsp; \u0026nbsp; acute myocardial infarction\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAF \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;atrial fibrillation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCOPD \u0026nbsp; \u0026nbsp; chronic obstructive pulmonary disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eESRD \u0026nbsp; \u0026nbsp; end-stage renal disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCHF \u0026nbsp; \u0026nbsp; \u0026nbsp;congestive heart failure\u003c/p\u003e\n\u003cp\u003eGB \u0026nbsp; \u0026nbsp; \u0026nbsp; gastrointestinal bleeding\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eALB \u0026nbsp; \u0026nbsp; \u0026nbsp;serum albumin\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHB \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;hemoglobin\u003c/p\u003e\n\u003cp\u003eIQR \u0026nbsp; \u0026nbsp; \u0026nbsp; interquartile ranges\u003c/p\u003e\n\u003cp\u003eOR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;odds ratios\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; confidence intervals\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eACCORD \u0026nbsp;Action to Control Cardiovascular Risk in Diabetes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003esdLDL \u0026nbsp; \u0026nbsp;small dense low-density lipoprotein\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original data was obtained from\u0026nbsp;eICU-CRD v2.0 (https://eicu-crd.mit.edu/), an open database. Therefore, informed consent can be waived.\u0026nbsp;The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLLW and ZHS contributed to the conception and design of the study. XLC was responsible for data analysis, LLW, ZHS, and HFH were responsible for data interpretation. LLW and ZHS wrote the original draft and QJW verified the data. All authors were involved in the reviewing and editing of the manuscript and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by National Natural Science Foundation of China (grant number. 82100710), Guangdong Basic and Applied Basic Research Foundation (grant number.2020A1515110398), Shenzhen Science and Technology Program (grant number. RCBS20210609103234061),\u0026nbsp;Shenzhen High-level Hospital Construction Fund and\u0026nbsp;Shenzhen Key Medical Discipline Construction Fund (grant number. SZXK009) and Sanming Project of Medicine in Shenzhen (grant number. SZSM202211013)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthor XC was employed by Empower U, X\u0026amp;Y Solutions Inc.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher’s note\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eZhao Y, Zhuang Z, Li Y, Xiao W, Song Z, et al. Elevated blood remnant cholesterol and triglycerides are causally related to the risks of cardiometabolic multimorbidity. \u003cem\u003eNature communications\u003c/em\u003e. (2024); 1(15):2451.\u003c/li\u003e\n \u003cli\u003eLiu Y, Kong Y, Yan Y , Hui P. Explore the value of carotid ultrasound radiomics nomogram in predicting ischemic stroke risk in patients with type 2 diabetes mellitus. \u003cem\u003eFrontiers in endocrinology\u003c/em\u003e. (2024); 15):1357580.\u003c/li\u003e\n \u003cli\u003eKoton S, Pike JR, Johansen M, Knopman DS, Lakshminarayan K, et al. 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Aldosterone and Insulin Resistance: Vicious Combination in Patients on Maintenance Hemodialysis. \u003cem\u003eTherapeutic apheresis and dialysis : official peer-reviewed journal of the International Society for Apheresis, the Japanese Society for Apheresis, the Japanese Society for Dialysis Therapy\u003c/em\u003e. (2018); 2(22):142-151.\u003c/li\u003e\n \u003cli\u003eStitziel NO, Kanter JE , Bornfeldt KE. Emerging Targets for Cardiovascular Disease Prevention in Diabetes. \u003cem\u003eTrends in molecular medicine\u003c/em\u003e. (2020); 8(26):744-757.\u003c/li\u003e\n \u003cli\u003ePala AA , Urcun YS. Effect of calculated plasma osmolality and atherogenic index of plasma on carotid artery blood flow velocities. \u003cem\u003eVascular\u003c/em\u003e. (2021); 4(29):527-534.\u003c/li\u003e\n \u003cli\u003eZhang Y, Chen S, Tian X, Xu Q, Xia X, et al. Elevated atherogenic index of plasma associated with stroke risk in general Chinese. \u003cem\u003eEndocrine\u003c/em\u003e. (2024), 10.1007/s12020-023-03677-0\u003c/li\u003e\n \u003cli\u003eJiang L, Li L, Xu Z, Tang Y, Zhai Y, et al. Non-linear associations of atherogenic index of plasma with prediabetes and type 2 diabetes mellitus among Chinese adults aged 45 years and above: a cross-sectional study from CHARLS. \u003cem\u003eFrontiers in endocrinology\u003c/em\u003e. (2024); 15):1360874.\u003c/li\u003e\n \u003cli\u003eMa X, Sun Y, Cheng Y, Shen H, Gao F, et al. Prognostic impact of the atherogenic index of plasma in type 2 diabetes mellitus patients with acute coronary syndrome undergoing percutaneous coronary intervention. \u003cem\u003eLipids in health and disease\u003c/em\u003e. (2020); 1(19):240.\u003c/li\u003e\n \u003cli\u003ePollard TJ, Johnson AEW, Raffa JD, Celi LA, Mark RG, et al. The eICU Collaborative Research Database, a freely available multi-center database for critical care research. \u003cem\u003eScientific data\u003c/em\u003e. (2018); 5):180178.\u003c/li\u003e\n \u003cli\u003eWu L, Pu H, Zhang M, Hu H , Wan Q. Non-linear relationship between the body roundness index and incident type 2 diabetes in Japan: a secondary retrospective analysis. \u003cem\u003eJournal of translational medicine\u003c/em\u003e. (2022); 1(20):110.\u003c/li\u003e\n \u003cli\u003eYan H, Zhou Q, Wang Y, Tu Y, Zhao Y, et al. Associations between cardiometabolic indices and the risk of diabetic kidney disease in patients with type 2 diabetes. \u003cem\u003eCardiovascular diabetology\u003c/em\u003e. (2024); 1(23):142.\u003c/li\u003e\n \u003cli\u003eLiu H, Liu K, Pei L, Li S, Zhao J, et al. Atherogenic Index of Plasma Predicts Outcomes in Acute Ischemic Stroke. \u003cem\u003eFrontiers in neurology\u003c/em\u003e. (2021); 12):741754.\u003c/li\u003e\n \u003cli\u003eZheng H, Wu K, Wu W, Chen G, Chen Z, et al. Relationship between the cumulative exposure to atherogenic index of plasma and ischemic stroke: a retrospective cohort study. \u003cem\u003eCardiovascular diabetology\u003c/em\u003e. (2023); 1(22):313.\u003c/li\u003e\n \u003cli\u003eFu L, Zhou Y, Sun J, Zhu Z, Xing Z, et al. Atherogenic index of plasma is associated with major adverse cardiovascular events in patients with type 2 diabetes mellitus. \u003cem\u003eCardiovascular diabetology\u003c/em\u003e. (2021); 1(20):201.\u003c/li\u003e\n \u003cli\u003eBeckman JA, Paneni F, Cosentino F , Creager MA. Diabetes and vascular disease: pathophysiology, clinical consequences, and medical therapy: part II. \u003cem\u003eEuropean heart journal\u003c/em\u003e. (2013); 31(34):2444-52.\u003c/li\u003e\n \u003cli\u003eBooth GL, Kapral MK, Fung K , Tu JV. Relation between age and cardiovascular disease in men and women with diabetes compared with non-diabetic people: a population-based retrospective cohort study. \u003cem\u003eLancet (London, England)\u003c/em\u003e. (2006); 9529(368):29-36.\u003c/li\u003e\n \u003cli\u003eLeong DP, Joseph PG, McKee M, Anand SS, Teo KK, et al. Reducing the Global Burden of Cardiovascular Disease, Part 2: Prevention and Treatment of Cardiovascular Disease. \u003cem\u003eCirc Res\u003c/em\u003e. (2017); 6(121):695-710.\u003c/li\u003e\n \u003cli\u003eElam MB, Ginsberg HN, Lovato LC, Corson M, Largay J, et al. Association of Fenofibrate Therapy With Long-term Cardiovascular Risk in Statin-Treated Patients With Type 2 Diabetes. \u003cem\u003eJAMA cardiology\u003c/em\u003e. (2017); 4(2):370-380.\u003c/li\u003e\n \u003cli\u003eHigashioka M, Sakata S, Honda T, Hata J, Yoshida D, et al. Small Dense Low-Density Lipoprotein Cholesterol and the Risk of Coronary Heart Disease in a Japanese Community. \u003cem\u003eJournal of atherosclerosis and thrombosis\u003c/em\u003e. (2020); 7(27):669-682.\u003c/li\u003e\n \u003cli\u003eHigashioka M, Sakata S, Honda T, Hata J, Shibata M, et al. The Association of Small Dense Low-Density Lipoprotein Cholesterol and Coronary Heart Disease in Subjects at High Cardiovascular Risk. \u003cem\u003eJournal of atherosclerosis and thrombosis\u003c/em\u003e. (2021); 1(28):79-89.\u003c/li\u003e\n \u003cli\u003eDav\u0026igrave; G , Patrono C. Platelet activation and atherothrombosis. \u003cem\u003eN Engl J Med\u003c/em\u003e. (2007); 24(357):2482-94.\u003c/li\u003e\n \u003cli\u003eSi Y, Fan W, Han C, Liu J , Sun L. Atherogenic Index of Plasma, Triglyceride-Glucose Index and Monocyte-to-Lymphocyte Ratio for Predicting Subclinical Coronary Artery Disease. \u003cem\u003eThe American journal of the medical sciences\u003c/em\u003e. (2021); 3(362):285-290.\u003c/li\u003e\n \u003cli\u003eLioy B, Webb RJ , Amirabdollahian F. The Association between the Atherogenic Index of Plasma and Cardiometabolic Risk Factors: A Review. \u003cem\u003eHealthcare (Basel, Switzerland)\u003c/em\u003e. (2023); 7(11):\u003c/li\u003e\n \u003cli\u003eWalter K. What Is Acute Ischemic Stroke? \u003cem\u003eJama\u003c/em\u003e. (2022); 9(327):885.\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"atherogenic index of plasma, ischemic stroke, ICU patients, multicenter study, diabetes","lastPublishedDoi":"10.21203/rs.3.rs-5638991/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5638991/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Atherogenic Index of Plasma (AIP) value is relationship with the risk of atherosclerosis, a known risk factor for cardiovascular events. However, studies on the correlation between AIP value and ischemic stroke (IS) in the diabetic population in Intensive Care Unit (ICU) are rare. Our study aimed to investigate the relationship between AIP values and IS among diabetic patients in American ICUs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA multicenter retrospective cohort study comprising 3695 patients from the eICU-CRD v2.0 database between 2014 and 2015 in the USA was conducted. We utilized logistic regression model to investigate the correlation between between AIP values and IS among diabetic patients in American ICUs. To detect possible non-linear associations, we combined logistic regression with generalized additive model (GAM). Additionally, we conducted a thorough array of sensitivity and subgroup analyses to verify the robustness of our results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe prevalence of IS was 19.46%.\u003cstrong\u003e \u003c/strong\u003eThe median AIP was 0.52 (interquartile range, 0.29-0.76). Participants with stroke exhibited a significant elevation in AIP levels. In particular, each one-unit elevation in AIP levels was associated with a 40% increased risk of IS (OR=1.4, 95% CI 1.1-2.8, P\u0026lt;0.001). In addition, a non-linear relationship exists between the AIP value and the incidence of IS, with an inflection point at 0.8. The effect sizes (OR) on the left and right sides of the inflection point were 0.9 (95%CI: 0.5-1.8; P= 0.741) and 1.7 (95%CI: 1.2-2.5; P＜0.001 ), respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research uncovers a positive, non-linear correlation between the AIP value and IS incidence among diabetic patients in American ICUs. Notably, a significant association between the AIP value and IS emerges when the AIP value is less than 0.8. From a therapeutic perspective, reducing AIP levels below the inflection point seems reasonable. However, the findings require validation through prospective studies.\u003c/p\u003e","manuscriptTitle":"Non-linear relationship between the atherogenic index of plasma and ischemic stroke in the diabetic population in ICU: A multicenter retrospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-25 15:46:40","doi":"10.21203/rs.3.rs-5638991/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"618ab2f4-47bb-4b70-be83-43190187c68c","owner":[],"postedDate":"December 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":41828177,"name":"Health sciences/Endocrinology"},{"id":41828178,"name":"Health sciences/Diseases/Cardiovascular diseases"},{"id":41828179,"name":"Health sciences/Diseases/Endocrine system and metabolic diseases"}],"tags":[],"updatedAt":"2025-08-28T06:54:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-25 15:46:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5638991","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5638991","identity":"rs-5638991","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}