Abstract
Background
The glucose-to-lymphocyte ratio has been proposed as a potential marker reflecting metabolic stress and immune-inflammatory status. This retrospective cohort study investigated the association between glucose-to-lymphocyte ratio and in-hospital mortality in patients with acute ischemic stroke using the MIMIC-IV database from 2008 to 2019.
Methods
The primary outcome was in-hospital mortality. The glucose-to-lymphocyte ratio was analyzed as both a continuous variable and a categorical variable according to quartiles. Cox proportional hazards models were used to estimate hazard ratios and 95% confidence intervals, with adjustment for potential confounders. Restricted cubic spline analysis was performed to assess potential nonlinearity. Subgroup and sensitivity analyses were conducted to evaluate the robustness of the findings.
Results
Among 7,659 screened patients, 2,565 met the inclusion criteria. The overall in-hospital mortality rate was 18.4% (473/2,565). Patients in the highest glucose-to-lymphocyte ratio quartile (Q4, ≥ 9.4) had a significantly higher risk of in-hospital mortality than those in the lowest quartile (Q1, < 3.4) in the fully adjusted model (hazard ratio = 1.54, 95% confidence interval: 1.12–2.14, P = 0.009). When analyzed as a continuous variable, each 10-unit increase in the glucose-to-lymphocyte ratio was independently associated with a 14% higher risk of in-hospital mortality (hazard ratio = 1.14, 95% confidence interval: 1.05–1.25, P = 0.003). Restricted cubic spline modeling indicated a linear relationship (P for nonlinearity = 0.584). Subgroup and sensitivity analyses confirmed the robustness of these associations. A higher glucose-to-lymphocyte ratio was also associated with increased 90-day mortality (hazard ratio = 1.13, 95% confidence interval: 1.04–1.22, P = 0.005).
Conclusions
An elevated glucose-to-lymphocyte ratio was independently and linearly associated with increased in-hospital mortality in patients with acute ischemic stroke, suggesting that it may serve as a practical prognostic marker. Further prospective multicenter studies are warranted to validate these findings.
Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1186/s12883-026-05232-6.
Keywords: Acute ischemic stroke, Glucose-to-lymphocyte ratio, Mortality, Cohort study, MIMIC database
Introduction
Stroke is the world’s second-largest cause of death and disability [1]. On average, someone dies of a stroke every 4 min [2]. In 2010, the estimated number of ischemic stroke (IS) and hemorrhagic stroke (HS) worldwide was 11.6 million and 5.3 million, respectively. In 2016, the number of new stroke cases increased to 13.7 million, and 5.5 million people died of stroke worldwide, of which 2.7 million were caused by IS and 2.8 million by HS [1]. Therefore, early identification of patients with the highest risk of adverse consequences is of great significance for the prognosis and targeting of appropriate treatment.
The glucose-to-lymphocyte ratio (GLR) is calculated from serum glucose concentration and total lymphocyte count in peripheral blood and is an indicator of glucose metabolism and systemic inflammatory status [3]. Several previous studies have shown that GLR can be used as a prognostic marker in patients with pancreatic cancer [4], non-small cell lung cancer [3], acute respiratory distress syndrome [5], and hospitalized patients with acute pancreatitis [6]. Inflammation plays a crucial role in the initiation, progression, and outcomes of ischemic stroke [7, 8]. It is an essential factor in atherosclerosis, thrombosis, and cerebral small vessel disease, which contribute to large arteries, cardiogenic thrombosis, and critical lacunar and cryptogenic ischemic stroke risk [9, 10]. Although limited, recent studies have explored the prognostic role of the glucose-to-lymphocyte ratio (GLR) in stroke-related outcomes, including stroke-associated pneumonia [11] and all-cause mortality in non-traumatic cerebral hemorrhage [12]. However, its association with in-hospital mortality, specifically in AIS patients within large-scale real-world critical care settings, remains unexamined. Thus, in the current study, we conducted a retrospective cohort study using an extensive public database to explore the relationship between GLR within 24 h of admission and in-hospital mortality in AIS patients.
Methods
Database source
All the data of this study were extracted from the Medical Information Mart for Intensive Care (MIMIC) IV database (version 1.0) [13] (https://mimic-iv.mit.edu/), comprising more than 60,000 patients admitted to the ICU of the Beth Israel Deaconess Medical Center in the United States from 2008 to 2019. Two authors, Z.L.X. and M.Y., gained permission to access the database by completing an online training course at the National Institutes of Health (Certification numbers: 27658233, 46868266) and were responsible for the original data collection and the data required for this study extraction. We strictly abide by the provisions of the data usage agreement and ensure the protection of patient privacy when processing data. The study was approved by the Beth Israel Deaconess Medical Center and the institutional review board of the Massachusetts Institute of Technology (Cambridge, Massachusetts). In order to protect the privacy of patients, all data were stripped of their identity; as a result, the ethics committee of Beth Israel Deaconess Medical Center waived informed consent.
Study population and stratification method
Patients older than 18 and diagnosed with AIS were selected for this study. Patients with AIS were identified using International Classification of Diseases (ICD) codes (ICD-9: 433.x1, 434.x1, 346.6x; ICD-10: I63.x, G43.6x). Patients were excluded if they met the following criteria: (1) the patients had no data of glucose and lymphocyte count within the first 24 h of admission and had > 15% missing data; (2) patients who received acute reperfusion therapy, such as intravenous tissue plasminogen activator or endovascular mechanical thrombectomy; (3) the patients were diagnosed with hematological diseases such as leukemia or lymphoma; (4) patients with a hospital length of stay less than 48 h. The following variables as confounding factors were extracted based on published literature [14–16] and clinical experience: age, sex, Body Mass Index (BMI), ICU Type, heart rate, mean arterial pressure (MAP), respiratory rate, Spo2, hypertension, diabetes, atrial fibrillation (AF), Charlson comorbidity index [17], invasive ventilation, non-invasive ventilation, tracheotomy, vasoactive drugs, infection, sepsis, organ dysfunction, hemoglobin, activated partial thromboplastin time (APTT), serum levels of Ca+, Cl+, Na+, K+, serum urea nitrogen, serum creatinine, HBA1c, white blood cell, lymphocyte, neutrophils, glucose, sequential organ failure assessment (SOFA) score, Glasgow coma scale (GCS) score, Oxford Acute Severity of Illness (OASIS) score, Simplified Acute Physiology (SAPS) II score, Acute Physiology Score (APS) III, Logistic Organ Dysfunction System (LODS) score, and Systemic Inflammatory Response Syndrome (SIRS) score. GLR was calculated as serum blood glucose (mg/dL, converted to mmol/L by dividing by 18.02) divided by total lymphocyte count (×10⁹/L), and the data were based on the serum blood glucose and lymphocyte count measured randomly for the first time after admission. We used data from the first ICU admission for patients with multiple ICU admissions. All laboratory parameters were extracted from results obtained within 24 h of ICU admission (i.e., baseline values) and extreme values (i.e., maxima and minima) during ICU stay, as well as average values.
Outcome
The primary outcome was in-hospital mortality, defined as the state of existence at discharge. The follow-up time was from admission to discharge or death.
Statistical analysis
Continuous variables were initially assessed for their distribution characteristics. Normally distributed data were represented as mean ± standard deviation, while non-normally distributed data were summarized using medians and interquartile ranges (IQRs). Between-group comparisons were conducted using either independent samples t-tests (for normally distributed variables) or the Wilcoxon rank-sum test (for skewed distributions). Categorical data were presented as percentages and analyzed using the chi-square (χ²) test.
To explore the independent association between the glucose-to-lymphocyte ratio (GLR) and in-hospital mortality, we applied multivariable Cox proportional hazards models with stepwise adjustment: Crude model: unadjusted; Model 1 adjusted for age and sex only; Model 2 additionally included adjustments for BMI, heart rate, mean arterial pressure (MAP), respiratory rate, history of hypertension, atrial fibrillation (AF), Charlson comorbidity index, tracheostomy, invasive mechanical ventilation, activated partial thromboplastin time (APTT), white blood cell count (WBC), neutrophil count, hemoglobin levels, Glasgow Coma Scale (GCS) score, and systemic inflammatory response syndrome (SIRS) score; Model 3 further adjusted for diabetes status, use of vasoactive medications, Sequential Organ Failure Assessment (SOFA) score, intensive care unit (ICU) type, serum urea nitrogen, glycosylated hemoglobin (HbA1c), serum creatinine, serum chloride and sodium, infection status, and composite scores including OASIS, SAPS II, APS III, and LODS.
Covariates were selected based on prior clinical evidence, statistical significance in univariable analysis (p < 0.1), and > 10% change in regression coefficient after inclusion or exclusion, consistent with published practices [18, 19].
To assess potential nonlinear relationships, restricted cubic spline models were fitted and compared with linear models using the Akaike Information Criterion (AIC). Kaplan–Meier survival curves and log-rank tests were used to visualize and compare survival outcomes across GLR strata.
Subgroup analyses were conducted to explore the potential effect modification of the GLR-mortality association in acute ischemic stroke (AIS) patients. These analyses stratified patients by age (< 65 vs. ≥65 years), sex, presence of hypertension, atrial fibrillation, use of invasive ventilation, administration of vasoactive agents, and presence of infection. Interactions between subgroups were formally tested within the Cox model framework.
All statistical procedures were performed using R software version 3.6.3 (The R Foundation for Statistical Computing, http://www.r-project.org) and Free Statistics software version 1.71. Two-sided p-values < 0.05 were considered statistically significant.
Patient and public involvement
Patients and the public were not involved in this study’s design, conduct, or reporting.
Results
Baseline characteristics of study participants
Among a total of 7659 patients screened, 2565 met the eligibility criteria for inclusion in the final analysis. We excluded 1572 patients due to duplicate records, 4 patients younger than 18 years old, and 3171 patients without ICU admission data. An additional 91 patients were excluded for having a hospital stay of less than 48 h, along with 49 patients who underwent mechanical thrombectomy and 207 patients who received intravenous thrombolysis. The patient selection process is illustrated in Fig. 1.
Fig. 1.
The flow chart of the study
The mean age of the included participants was 68.9 ± 16.0 years, and 1285 (50.1%) were male. The average BMI was 28.0 ± 6.7 kg/m². Participants were categorized into quartiles based on glucose-to-lymphocyte ratio (GLR): Q1 (< 3.4), Q2 (3.5–5.4), Q3 (5.5–9.3), and Q4 (≥ 9.4). The median GLR was 5.5 (IQR: 3.5–9.4). The average length of hospital stay increased across GLR quartiles: Q1 (11.2 ± 11.4 days), Q2 (11.5 ± 10.6), Q3 (14.0 ± 13.3), and Q4 (14.9 ± 12.3).
Overall, 473 out of 2565 patients (18.4%) died during hospitalization. Mortality rates by GLR quartiles were: Q1 (11.3%), Q2 (14.4%), Q3 (20.6%), and Q4 (27.4%). Patients in the higher GLR quartiles were more likely to be female and showed lower prevalence of hypertension and lower white blood cell and lymphocyte counts. Conversely, they had higher rates of diabetes, atrial fibrillation, and invasive mechanical ventilation use. These patients also exhibited higher neutrophil counts, urea nitrogen, creatinine, HbA1c, Charlson comorbidity index, SOFA scores, and were more likely to receive vasoactive agents or present with infections, including sepsis and organ failure (Table 1).
Table 1.
Baseline characteristics of participants
| Covariates | Total (n = 2565) |
Q1 (< 3.4) (n = 636) |
Q2 (3.5 to < 5.4) (n = 646) |
Q3 (5.5 to < 9.3) (n = 641) |
Q4 (≥ 9.4) (n = 642) |
|---|---|---|---|---|---|
| Age (years) | 68.9 ± 16.0 | 66.0 ± 17.8 | 69.6 ± 15.8 | 68.8 ± 15.3 | 71.1 ± 14.5 |
| Sex, N (%) | |||||
| Male | 1285 (50.1) | 299 (47) | 306 (47.4) | 341 (53.2) | 339 (52.8) |
| Female | 1280 (49.9) | 337 (53) | 340 (52.6) | 300 (46.8) | 303 (47.2) |
| BMI (kg/m2) | 28.0 ± 6.7 | 28.3 ± 7.0 | 27.8 ± 6.4 | 28.4 ± 6.4 | 27.6 ± 6.8 |
| ICU Type, n (%) | |||||
| CCU | 417 (16.3) | 92 (14.5) | 99 (15.3) | 105 (16.4) | 121 (18.8) |
| MICU | 427 (16.6) | 73 (11.5) | 87 (13.5) | 118 (18.4) | 149 (23.2) |
| NSICU | 749 (29.2) | 216 (34) | 217 (33.6) | 174 (27.1) | 142 (22.1) |
| SICU | 972 (37.9) | 255 (40.1) | 243 (37.6) | 244 (38.1) | 230 (35.8) |
| Heart rate (bpm) | 96.8 ± 21.5 | 95.7 ± 22.3 | 94.2 ± 20.6 | 97.4 ± 21.1 | 100.1 ± 21.8 |
| MAP (mmHg) | 71.4 ± 17.7 | 72.1 ± 17.0 | 73.1 ± 16.8 | 72.0 ± 18.6 | 68.2 ± 17.9 |
| Respiratory rate (bpm) | 19.4 ± 3.8 | 19.1 ± 3.7 | 19.1 ± 3.6 | 19.7 ± 4.0 | 19.8 ± 4.0 |
| Spo2 (%) | 93.7 ± 4.7 | 93.7 ± 4.5 | 94.0 ± 4.4 | 93.6 ± 4.6 | 93.4 ± 5.2 |
| Hypertension, n (%) | 1367 (53.3) | 351 (55.2) | 368 (57) | 346 (54) | 302 (47) |
| Diabetes, n (%) | 863 (33.6) | 147 (23.1) | 171 (26.5) | 258 (40.2) | 287 (44.7) |
| AF, n (%) | 988 (38.5) | 185 (29.1) | 276 (42.7) | 249 (38.8) | 278 (43.3) |
| Charlson comorbidity index | 7.1 ± 2.8 | 6.4 ± 2.8 | 6.9 ± 2.6 | 7.3 ± 2.9 | 7.8 ± 2.8 |
| Invasive ventilation, n (%) | 939 (36.6) | 198 (31.1) | 203 (31.4) | 237 (37) | 301 (46.9) |
| Non-invasive ventilation, n (%) | 47 ( 1.8) | 8 (1.3) | 8 (1.2) | 13 (2) | 18 (2.8) |
| Tracheotomy, n (%) | 46 ( 1.8) | 5 (0.8) | 14 (2.2) | 11 (1.7) | 16 (2.5) |
| Vasoactive drugs, n (%) | 469 (18.3) | 84 (13.2) | 98 (15.2) | 117 (18.3) | 170 (26.5) |
| Infection, n (%) | 1168 (45.5) | 241 (37.9) | 250 (38.7) | 314 (49) | 363 (56.5) |
| Sepsis, n (%) | 852 (33.2) | 157 (24.7) | 159 (24.6) | 237 (37) | 299 (46.6) |
| Organ dysfunction, n (%) | 1222 (47.6) | 254 (39.9) | 241 (37.3) | 327 (51) | 400 (62.3) |
| Hemoglobin (g/L) | 9.3 ± 2.5 | 9.7 ± 2.5 | 9.7 ± 2.5 | 9.2 ± 2.4 | 8.6 ± 2.2 |
| APTT (s) | 25.7 ± 4.0 | 25.9 ± 3.9 | 25.8 ± 4.1 | 25.7 ± 4.1 | 25.5 ± 4.0 |
| Serum calcium (mg/dl) | 8.7 ± 0.5 | 8.8 ± 0.5 | 8.8 ± 0.5 | 8.7 ± 0.5 | 8.6 ± 0.5 |
| Serum chlorine (mg/dl) | 103.7 ± 4.2 | 103.8 ± 3.7 | 104.0 ± 4.0 | 103.7 ± 4.4 | 103.3 ± 4.5 |
| Serum sodium (mg/dl) | 139.9 ± 3.4 | 139.9 ± 3.1 | 140.2 ± 3.2 | 140.0 ± 3.5 | 139.8 ± 3.6 |
| Serum potassium (mEq/L) | 4.1 ± 0.3 | 4.1 ± 0.3 | 4.1 ± 0.3 | 4.1 ± 0.3 | 4.1 ± 0.3 |
| Serum urea nitrogen (mg/dl) | 19.0 (14.0, 29.0) | 17.0 (13.0, 23.0) | 19.0 (14.0, 26.0) | 20.0 (15.0, 29.0) | 24.0 (17.0, 36.0) |
| Serum creatinine (mg/dl) | 0.9 (0.7, 1.3) | 0.9 (0.7, 1.1) | 0.9 (0.7, 1.1) | 1.0 (0.7, 1.3) | 1.1 (0.8, 1.5) |
| HBA1c (%) | 6.2 ± 1.2 | 5.9 ± 1.0 | 6.1 ± 1.0 | 6.4 ± 1.4 | 6.5 ± 1.4 |
| WBC (K/uL) | 9.5 (7.2, 12.7) | 10.7 (8.2, 14.1) | 9.4 (7.5, 12.1) | 9.0 (6.7, 12.2) | 8.7 (6.3, 12.5) |
| Neutrophils(%) | 81.5 ± 9.8 | 75.6 ± 12.0 | 79.6 ± 8.2 | 83.3 ± 7.2 | 87.6 ± 6.5 |
| Lymphocyte (%) | 14.7 (8.6, 20.6) | 24.5 (17.1, 31.5) | 16.5 (13.5, 20.1) | 12.0 (8.5, 17.0) | 6.3 (4.2, 9.9) |
| Glucose (mg/dl) | 122.0 (102.0, 158.0) | 106.0 (93.0, 122.0) | 117.0 (100.2, 139.0) | 130.0 (109.0, 170.0) | 153.0 (120.0, 212.8) |
| GLR | 5.5 (3.5, 9.4) | 2.6 (2.0, 3.1) | 4.4 (3.9, 4.9) | 7.0 (6.2, 7.9) | 14.2 (11.3, 21.1) |
| SOFA score | 4.0 (2.0, 6.0) | 3.0 (2.0, 5.0) | 4.0 (2.0, 6.0) | 4.0 (2.0, 7.0) | 5.0 (3.0, 8.0) |
| GCS score | 13.8 ± 2.3 | 13.8 ± 2.3 | 13.9 ± 2.2 | 13.8 ± 2.3 | 13.7 ± 2.5 |
| OASIS score | 32.9 ± 9.5 | 30.9 ± 9.1 | 32.3 ± 9.1 | 33.0 ± 9.6 | 35.4 ± 9.5 |
| SAPS II score | 33.5 ± 12.6 | 31.1 ± 12.8 | 31.5 ± 11.0 | 33.8 ± 12.3 | 37.6 ± 13.3 |
| APS III | 41.0 (29.0, 60.0) | 36.0 (25.0, 51.0) | 36.0 (27.0, 54.0) | 41.0 (29.0, 60.0) | 50.0 (36.0, 71.0) |
| LODS score | 5.0 (2.0, 7.0) | 4.0 (1.0, 6.0) | 4.0 (2.0, 7.0) | 5.0 (2.0, 7.0) | 6.0 (3.0, 8.8) |
| SIRS score | 2.0 (1.0, 3.0) | 2.0 (1.0, 3.0) | 2.0 (1.0, 3.0) | 2.0 (1.0, 3.0) | 2.0 (1.0, 3.0) |
| Los hospital (day) | 12.9 ± 12.0 | 11.2 ± 11.4 | 11.5 ± 10.6 | 14.0 ± 13.3 | 14.9 ± 12.3 |
| Los ICU (day) | 5.8 ± 7.3 | 4.9 ± 6.0 | 5.3 ± 6.0 | 6.1 ± 7.9 | 6.9 ± 8.7 |
| In-hospital mortality, n (%) | 473 (18.4) | 72 (11.3) | 93 (14.4) | 132 (20.6) | 176 (27.4) |
| 90-day mortality, n (%) | 512 (20.0) | 77 (12.1) | 104 (16.1) | 141 (22) | 190 (29.6) |
Abbreviation: BMI Body Mass Index, CCU Coronary Care Unit, MICU Medical Intensive Care Unit, NSICU Neuro Surgical Intensive Care Unit, SCIU Surgical Intensive Care Unit, bpm beat per minute, MAP Mean Arterial Pressure, AF Atrial Fibrillation, APTT Activated Partial Thromboplastin Time, WBC White blood cell, GLR Glucose-to-lymphocyte Ratio, SOFA Sequential Organ Failure Assessment, GCS Glasgow Coma Scale, OASIS Oxford Acute Severity of Illness Score, SAPS II Simplified Acute Physiology Score II, APS III Acute Physiology Score III, LODS Logistic Organ Dysfunction System, SIRS Systemic Inflammatory Response Syndrome
Association between GLR and in-hospital mortality
A restricted cubic spline model indicated a linear association between GLR and in-hospital mortality after adjustment for confounders (P for non-linearity = 0.584, Fig. 2). The AIC for the spline model was not lower than that for the linear model, further supporting that a linear specification adequately captured the association.
Fig. 2.
A linear relationship between GLR and in-hospital mortality for patients with AIS. The red area represents hospital mortality. Solid and dashed lines represent the predicted value and 95% confidence intervals. They were adjusted for age, sex, BMI, heart rate, MAP, resp rate, hypertension, AF, Charlson comorbidity index, tracheotomy, invasive ventilation, APTT, WBC, neutrophils, hemoglobin, GCS score, SIRS score, diabetes, vasoactive drugs, SOFA score, ICU type, serum urea nitrogen, HBA1c, serum creatinine, serum chlorine, serum sodium, infection, OASIS score, SAPS II score, APS III, and LODS score. Only 90% of the data is shown
When treated as a continuous variable, each 10-unit increase in GLR was associated with a 14% higher risk of in-hospital mortality in the unadjusted model (HR = 1.14, 95% CI: 1.07–1.22, P < 0.001). After adjusting for age and sex (Model 1), the hazard remained similar (HR = 1.13, 95% CI: 1.06–1.21, P < 0.001). In the fully adjusted model (Model 3), this association persisted (HR = 1.14, 95% CI: 1.05–1.25, P = 0.003) (Table 2).
Table 2.
Association between GLR and In-hospital Mortality in a multiple regression model
| Variable | Crude Model | Model 1 | Model 2 | Model 3 | ||||
|---|---|---|---|---|---|---|---|---|
| HR (95% CI) | P value | HR (95% CI) | P value | HR (95% CI) | P value | HR (95% CI) | P value | |
| GLRa | 1.14 (1.07 ~ 1.22) | < 0.001 | 1.13 (1.06 ~ 1.21) | < 0.001 | 1.12 (1.03 ~ 1.22) | 0.007 | 1.14 (1.05 ~ 1.25) | 0.003 |
| GLR4 | ||||||||
| Q1(GLR < 3.4) | 1(Ref) | 1(Ref) | 1(Ref) | 1(Ref) | ||||
| Q2(3.5 ≤ GLR < 5.4) | 1.25 (0.92 ~ 1.70) | 0.157 | 1.18 (0.86 ~ 1.60) | 0.302 | 1.42 (1.03 ~ 1.95) | 0.033 | 1.29 (0.93 ~ 1.79) | 0.122 |
| Q3(5.5 ≤ GLR < 9.3) | 1.48 (1.11 ~ 1.97) | 0.008 | 1.41 (1.05 ~ 1.87) | 0.02 | 1.54 (1.13 ~ 2.10) | 0.006 | 1.46(1.06 ~ 2.00) | 0.021 |
| Q4(GLR ≥ 9.4) | 1.84 (1.40 ~ 2.43) | < 0.001 | 1.63 (1.24 ~ 2.15) | < 0.001 | 1.61 (1.18 ~ 2.21) | 0.003 | 1.54 (1.12 ~ 2.14) | 0.009 |
| P for trend. test | < 0.001 | < 0.001 | 0.005 | 0.01 | ||||
aGLR was entered as a continuous analysis variable per 10-unit increase. Abbreviation: GLR glucose-to-lymphocyte ratio
Model 1 = Adjust for (age + sex)
Model 2 = Model 1+ (BMI + heart rate + MAP + resp rate + hypertension + AF + Charlson comorbidity index + tracheotomy + invasive ventilation + APTT + WBC + neutrophils+hemoglobin + GCS score + SIRS score)
Model 3 = Model 2+ (diabetes + vasoactive drugs + SOFA score + ICU type + Serum urea nitrogen + HBA1c + serum creatinine + serum chlorine + serum sodium + infection + OASIS score + SAPS II score + APS III + LODS score)
When GLR was analyzed categorically by quartiles, patients in the highest quartile (Q4) had significantly higher in-hospital mortality compared to those in the lowest quartile (Q1) in the fully adjusted model (HR = 1.54, 95% CI: 1.12–2.14, P = 0.009). This dose–response trend was further supported by Kaplan–Meier survival analysis (log-rank test P < 0.0001, Fig. 3), where Q4 participants demonstrated the highest mortality rate.
Fig. 3.
Kaplan-Meier Survival Curves for in-hospital AIS patients. The colors of the quartile correspond to the colors as depicted in Fig. 3. The highest GLR quartile had the lowest in-hospital survival (log-rank test P < 0.0001). Only patients with a hospital length of stay ≤ 30 days are displayed. Discharged patients were treated as censored observations; thus, the displayed survival probability reflects time-dependent in-hospital survival and is not directly comparable to the crude mortality rates in Table 1
Stratified analyses and interaction tests
We conducted stratified analyses and interaction testing to assess potential effect modifiers of the GLR-mortality relationship (Fig. 4). The positive association between GLR and in-hospital mortality remained significant in subgroups including patients aged < 65 years (HR = 1.29, 95% CI: 1.11–1.49), males (HR = 1.14, 95% CI: 1.00–1.29), those without hypertension (HR = 1.21, 95% CI: 1.08–1.37), without atrial fibrillation (HR = 1.25, 95% CI: 1.12–1.40), those receiving invasive mechanical ventilation (HR = 1.17, 95% CI: 1.05–1.31), and vasoactive drug users (HR = 1.18, 95% CI: 1.05–1.34).
Fig. 4.
Stratified Analyses by Potential Modifiers of the Association Between GLR and Risk of in-hospital mortality
Although interaction terms for age and atrial fibrillation yielded P < 0.05, no robust effect modification was observed, and the findings were generally consistent across subgroups. Given multiple comparisons and the uniform direction of associations, these interactions may not reflect clinically meaningful heterogeneity.
Sensitive analysis
To further validate our findings, we examined the association between GLR and 90-day mortality. The overall 90-day mortality rate was 20.0% (512/2565). In multivariable regression models adjusted for screened covariates, each 10-unit increase in GLR was associated with a 13% higher risk of 90-day mortality (HR = 1.13, 95% CI: 1.04–1.22, P = 0.005). Kaplan–Meier curves were also generated to visualize 90-day survival across GLR quartiles (Supplementary Figure A), and the results were consistent with the primary in-hospital mortality analyses.
Stratified analyses yielded similar results across subgroups. Additionally, restricted cubic spline analysis demonstrated a linear relationship between GLR and 90-day mortality (P for non-linearity = 0.344), further supporting the robustness of the association. (online supplemental material Table A and Figure B).
Discussion
This relatively large-scale cohort study demonstrated a linear relationship between GLR and in-hospital mortality for patients with AIS. In the fully adjusted model, for every 10 units of GLR increase, hospital mortality increased by 14%. Stratified analyses showed that the relationship between GLR and in-hospital mortality remained robust.
Stroke is still one of the significant diseases leading to the annual increase in global mortality and disability rates, which poses a significant public health burden on society [20, 21]. How to reduce the mortality of stroke has become the focus of social research. GLR is an indicator of glucose metabolism and systemic inflammation related to the occurrence and development of many diseases. Because of its easy access and low price, more and more attention has been paid to its clinical use. Our study found a dose-response relationship between GLR and hospital mortality. In the fully adjusted model, we observed a 14% increase in hospital mortality for every 10 additional GLR units. This relationship reminds us that GLR may play a key role in the severity of the patient’s condition. From a biological point of view, GLR may reflect the body’s inflammatory state. With the increase in GLR dose, there may be more intense inflammatory and immune responses in patients, which may lead to higher mortality.
Previous studies show that GLR is closely related to the occurrence and prognosis of various diseases [22–26]. For example, Li et al. [25] found that patients in the high-GLR group had a relatively higher incidence of acute kidney injury when compared with the low-GLR group (79.3% versus 54.0%, P < 0.001). The Spearman correlation coefficients showed that GLR was significantly positively correlated with acute kidney injury, indicating a close relationship between preoperative GLR and postoperative cardiac surgery in ICU patients with acute kidney injury. Yang et al. [3] found that the risk of death from non-small cell lung cancer was positively related to GLR levels. After adjusting the covariates, Liu et al. [15] found that higher GLR levels were associated with an increased risk of in-hospital death [HR = 1.70,95%CI: (1.24–2.34)] in patients with acute myocardial infarction. This relationship was also seen in men ≥ 65 years old, patients who did not receive renal replacement therapy, coronary artery bypass grafting, or beta blockers, using or not using pressor drugs, and regardless of SOFA score. Recently, Yang et al. [12] also found that with the increase of GLR, the hospital mortality and ICU mortality of American patients with non-traumatic intracerebral hemorrhage also increased progressively, showing a significant linear relationship. Cerebrovascular disease is a significant cause of human death worldwide, with very high mortality and disability rates, among which cerebral infarction is the most common and fatal cerebrovascular accident in the process of cerebral ischemia-reperfusion. The inflammatory reaction is the leading cause of neuronal apoptosis, which affects neurological function and daily life [27, 28]. In recent years, Many studies have shown that in the pathological process of AIS, the levels of white blood cells and blood glucose are closely related to the severity of the disease and the prognosis of patients [29–32]. Neutrophils and lymphocytes are two common subtypes of leukocytes, which are essential mediators in their pathological process, and both play an important role in nerve injury after cerebral infarction [29]. Although GLR has been associated with stroke-related outcomes such as stroke-associated pneumonia [11] and mortality in non-traumatic cerebral hemorrhage [12], its independent association with in-hospital mortality specifically in AIS patients has not been previously examined in a large-scale ICU cohort. Through a large cohort study, we showed that there is a linear relationship between GLR and hospital mortality in patients with AIS. For every 10 units of GLR increase, hospital mortality increases by 14%, and the 90-day mortality increases by 13%, indicating that GLR is closely related to the prognosis of AIS.
The exact mechanism underlying the association between elevated GLR levels and in-hospital mortality for patients with AIS is unclear. The relationship between glucose levels and immune cells is a pivotal aspect. Elevated glucose levels, often associated with conditions like diabetes, have been recognized to exert profound effects on immune cell function. Hyperglycemia, for instance, can trigger oxidative stress and inflammation, leading to alterations in immune cell responses [15, 33]. Specifically, high glucose levels have been implicated in impairing neutrophil and macrophage functions, affecting their ability to combat pathogens and resolve inflammation [14]. Another key aspect is the generation of advanced glycation end products (AGEs) due to prolonged exposure to high glucose levels [34]. These AGEs can interact with immune cells, altering their functions through receptor-mediated signaling [35]. This molecular crosstalk contributes to the dysregulation of immune responses, potentially exacerbating inflammatory processes.
Moreover, the impact of glucose extends beyond direct effects on immune cells to influence the broader immune system. Hyperglycemia disrupts the balance of pro-inflammatory and anti-inflammatory signals, creating an environment conducive to chronic inflammation. This dysregulation impairs the immune system’s ability to mount an effective response and sets the stage for a pro-inflammatory milieu that can contribute to the development and progression of various diseases, including stroke [12, 33].
In the context of stroke, immune cells play a dual role in the intricate balance between injury and repair within the brain. The molecular mechanisms involve the activation of toll-like receptors (TLRs) on immune cells, triggering downstream signaling pathways [36]. High glucose levels may exacerbate neuroinflammation by amplifying the activation of microglia and astrocytes, key players in the brain’s immune response [37]. This sustained inflammatory environment can lead to secondary tissue damage and hinder post-stroke recovery.
We know that inflammation plays a vital role in the occurrence and development of ischemic stroke [38, 39], and systemic inflammation can also increase the susceptibility to stroke [40]. High GLR represents high glucose content and low lymphocyte content. As we all know, diabetes has been identified as an essential risk factor for AIS. AIS’s incidence and adverse outcomes in patients with diabetes are significantly higher [41, 42]. Inflammation may lead to diabetes by regulating insulin resistance, which in turn aggravates the symptoms of hyperglycemia and further promotes the long-term complications of diabetes [43]. It may also virtually increase the mortality of patients with AIS. Some studies have reported that lymphocytes play an essential role in the healing and repair of inflammation [44–46].
On the contrary, lymphocytes are considered the source of pro-inflammatory cytokines and cytotoxic substances and significantly negatively affect the ischemic brain [47]. Clinical evidence suggests lower lymphocyte count is associated with poor early neurological improvement and long-term functional outcomes [48]. The association between glucose, immune cells, and stroke involves a complex interplay of molecular mechanisms, encompassing AGEs, intracellular signaling pathways, immune cell functionality, etc. These molecular intricacies provide a foundation for understanding the pathophysiological connections and potential therapeutic targets within this intricate triad, which may be the potential mechanism of high GLR leading to high in-hospital mortality in patients with AIS.
It is worth noting that the relationship between GLR and AIS mortality may be attributed to confounding factors and effect modifiers. However, our findings remained robust after adjusting for a comprehensive set of potential confounders. The sex distribution in our cohort was nearly equal (50.1% males and 49.9% females), consistent with the reported sex distribution of AIS patients in ICU settings. Therefore, we performed subgroup and sensitivity analyses to further evaluate the stability of our findings across different patient characteristics.
More importantly, we conducted stratified analysis by including age, sex, hypertension, atrial fibrillation, invasive ventilation, vasoactive drugs, and infection, and found that GLR was associated with higher in-hospital mortality among participants < 65 years, male, and those without hypertension and AF, as well as the users of invasive ventilation and vasoactive drugs. Although nominally significant interactions were observed for age and atrial fibrillation, no robust effect modification was identified, and the direction of association remained consistent across all subgroups. However, due to the multiplicity of testing, our hypothesis still needs to be further confirmed in more studies.
Our research has the following strengths. First, we conducted a large and diverse population study using real-world data. Second, strict statistical adjustment was used in this study to reduce the susceptibility of potential residual confounders, and the results were stable. Third, we treated the target independent variables as continuous and categorical variables, reducing the contingency in data analysis and enhancing the results’ robustness. Finally, this study is among the first to examine the independent association between GLR and in-hospital mortality in AIS patients within a large-scale real-world ICU cohort, and a linear relationship was found. Because GLR is easy to obtain and cheap in clinical practice, it is straightforward to promote its use in clinical practice, which can play an excellent clinical application role for clinicians to judge the prognosis of AIS patients early.
There are some limitations to our study. First, due to the retrospective and observational nature of the analysis, residual confounding cannot be fully excluded despite adjustment for a wide range of potential covariates. Second, the diagnosis of acute ischemic stroke (AIS) was based on structured data extracted from an electronic database, which may not capture all clinical nuances. Patients with severe conditions who could not be transferred from other institutions might have been excluded, potentially leading to underrepresentation of high-risk individuals. Similarly, patients with milder AIS may have been admitted to general wards rather than the ICU, and thus were not included in this study, which could introduce selection bias and limit the generalizability of our findings. Third, missing data for certain variables may have introduced selection bias or measurement error, which could affect the validity of the results. Fourth, the NIHSS, a standard stroke severity scale, was not available in MIMIC-IV; GCS was used as a surrogate, which may not fully capture stroke severity. Fifth, patients receiving reperfusion therapy (intravenous thrombolysis or mechanical thrombectomy) were excluded to avoid treatment-related confounding, but this may have introduced selection bias. Sixth, the study population was limited to patients in the United States, and we were unable to perform detailed ethnicity-specific analyses, which restricts the generalizability of our findings. Seventh, functional outcomes and quality-of-life measures were not assessed, which are increasingly recognized as important endpoints in stroke research. Finally, the retrospective design precludes causal inference; the observed association between GLR and mortality reflects correlation rather than causation. Therefore, well-designed prospective studies are warranted to further elucidate the prognostic value of GLR in AIS.
Conclusion
We observed a linear relationship between GLR and in-hospital mortality for patients with AIS. Patients with higher GLR have higher in-hospital mortality. Further extensive multicenter prospective studies are needed to confirm this relationship.
Supplementary Information
Acknowledgements
We thank Peng Zhao, Shaoyan Cai, Haili Pan, and Wenfeng Cao for their valuable input during discussions of this study.
Abbreviations
- AIS
Acute Ischemic Stroke
- AF
Atrial Fibrillation
- APTT
Activated Partial Thromboplastin Time
- APS III
Acute Physiology Score III
- BMI
Body Mass Index
- CCU
Coronary Care Unit
- CI
Confidence Interval
- GLR
Glucose-to-Lymphocyte Ratio
- GCS
Glasgow Coma Scale
- HR
Hazard Ratio
- HS
Hemorrhagic Stroke
- ICU
Intensive Care Unit
- IS
Ischemic Stroke
- IQR
Interquartile Range
- LODS
Logistic Organ Dysfunction System
- MAP
Mean Arterial Pressure
- MICU
Medical Intensive Care Unit
- MIMIC-IV
Medical Information Mart for Intensive Care IV
- NSICU
Neuro Surgical Intensive Care Unit
- OASIS
Oxford Acute Severity of Illness Score
- SAPS II
Simplified Acute Physiology Score II
- SD
Standard Deviation
- SICU
Surgical Intensive Care Unit
- SOFA
Sequential Organ Failure Assessment
- SIRS
Systemic Inflammatory Response Syndrome
- WBC
White Blood Cell
- HbA1c
Hemoglobin A1c
- AGEs
Advanced Glycation End Products
- TLRs
Toll-Like Receptors
Authors contributions
MY conceived and designed the study. ZLX and MY acquired the data and contributed to data interpretation. ZLX and MY performed the statistical analyses and drafted the manuscript. Both ZLX and MY had full access to all raw data and take responsibility for the integrity and accuracy of the data analysis. HYZ provided critical revisions to the manuscript for important intellectual content. All authors read and approved the final manuscript.
Funding
The present study was supported by Jiangxi Provincial Health Technology Project (NO. 202110003, NO. 202510714), the Science and Technology Research Project of Jiangxi Provincial Department of Education (NO. GJJ218910), and the Science and Technology Plan Project of Jiangxi Provincial Administration of Traditional Chinese Medicine (NO. 2022B741). The authors thank the participants of this study for their significant contributions.
Data availability
All data generated or analysed during this study are included in this article [and its supplementary information files].
Declarations
Ethics approval and consent to participate
The studies involving humans were approved by the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center. The studies were conducted in accordance with the local legislation, institutional requirements, and the Declaration of Helsinki. Written informed consent for participation was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and institutional requirements.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
All data generated or analysed during this study are included in this article [and its supplementary information files].