Abstract
Background and purpose
High-sensitivity C-reactive protein-triglyceride-glucose index (CTI) is an innovative biomarker of insulin resistance and inflammation. The objective of this study was to explore the association of high-sensitivity CTI with functional outcome in elderly patients with acute ischemic stroke (AIS) undergoing intravenous thrombolysis (IVT).
Methods
Elderly AIS patients treated with IVT were enrolled in three centers. Unfavorable functional outcome was defined as Modified Rankin Scale score ranging from 3–6. Logistic regression models were used to calculate the odds ratio (OR) and 95% confidence interval (95% CI) for the association between high-sensitivity CTI and 3-month functional outcome. Restricted cubic splines (RCS) were performed to explore the shape of this association. Receiver operating characteristic curve was applied to evaluate the discriminatory capacity of high-sensitivity CTI. Furthermore, subgroup and sensitivity analyses were performed to examine the consistency of the observed association.
Results
A total of 708 elderly patients were enrolled, among whom 231 (32.6%) developed unfavorable functional outcome at 3 month follow up. In Model 3, higher continuous high sensitivity CTI levels were independently associated with 3 month unfavorable functional outcome among elderly AIS patients receiving IVT (OR = 1.64; 95% CI, 1.28–2.11). Taking the first quartile of high sensitivity CTI as the reference group, patients in the fourth quartile exhibited the highest risk of unfavorable functional outcome in Model 3 (OR = 2.62, 95% CI, 1.48–4.64). RCS revealed a significant overall association between high sensitivity CTI and unfavorable functional outcome, with no statistically significant nonlinear component (P for overall < 0.001; P for nonlinearity = 0.743). Subgroup and sensitivity analyses demonstrated consistent directions of associations.
Conclusion
High-sensitivity CTI was independently associated with functional outcome at 3 months in elderly AIS patients treated with IVT.
Keywords: acute ischemic stroke, elderly, functional outcome, high-sensitivity C-reactive protein-triglyceride-glucose index, intravenous thrombolysis
Introduction
In China, it is estimated that 14.2% of the population was aged 65 and older in 2021, and this group is anticipated to rise to around 395 million by 2050 (1). Acute ischemic stroke (AIS), which is recognized as a critical health challenge, results in morbidity as well as mortality and imposes a considerable strain on the elderly (2–4). At present, intravenous thrombolysis (IVT) is regarded as an effective treatment for the patients with AIS (5–7). Nevertheless, there still remain numerous elderly AIS patients that may experience unfavorable outcomes even after receiving the treatment of IVT (8, 9). Therefore, it is vital to investigate the biomarkers associated with the clinical outcomes in the elderly patients with AIS undergoing IVT.
Insulin resistance (IR), which contributes to the formation of thrombosis and the progression of atherosclerosis, may play an important role in the ischemic stroke (10, 11). Triglyceride-glucose index (TyG) is reported to be an innovative indicator of IR (12–14). There were several studies that suggested the relationship between TyG and stroke (15–18). What is more, inflammation can serve as a key factor in the pathogenesis of stroke (19–21). Hence, there is a gradual increase in the clinical study focusing on high-sensitivity C-reactive protein-triglyceride glucose index (CTI), which combine TyG, a biomarker of IR, and C-reactive protein (CRP), a biomarker of inflammation (22–24). The results of the study conducted by Hu and his colleagues showed a notable positive linear relationship between the levels of CTI and the occurrence of stroke (24).
In this research, we intended to explore the correlation between the levels of high-sensitivity CTI and functional outcome in the elderly AIS patients treated with IVT.
Materials and methods
Study design and participants
Consecutive elderly patients with AIS who received IVT within 4.5 h of symptom onset were prospectively enrolled for data collection and retrospectively analyzed from three participating centers: Nanjing First Hospital, Nantong Third People’s Hospital, and the Affiliated Hospital of Nantong University. All patients were treated in dedicated stroke units. Eligible participants were included in the final analysis according to the following criteria.
Inclusion criteria: 1. Admission within 4.5 h after onset; 2. Treated with IVT; 3. 65 years or older.
Exclusion criteria: 1. Severe inflammatory diseases or infectious diseases within 2 weeks preceding stroke onset; 2. Incomplete data of high-sensitivity CRP or fasting triglyceride or fasting blood glucose; 3. loss to follow-up over the following 3 months after admission.
Informed consent was obtained from participants or their legal representatives. This study was approved by the ethics committees of all participating hospitals.
Data acquisition
On the day of admission, all the participants underwent standard assessments of demographic characteristics (age and gender), vascular risk factors (history of hypertension, history of diabetes mellitus, history of atrial fibrillation, history of coronary artery disease, current smoking and current drinking alcohol), medication use (antiplatelet and anticoagulation), clinical assessment (stroke severity, onset to treatment time [OTT], proximal arterial occlusion [PAO] and stroke subtype). Computed tomography, magnetic resonance, electrocardiogram, echocardiography, carotid ultrasonography, and transcranial Doppler were performed for assessing stroke subtype and PAO. Stroke severity was assessed using National Institutes of Health Stroke Scale (NIHSS) score. Stroke subtype was classified according to Trial of Org 10,172 in Acute Stroke Treatment (TOAST) criteria (25).
Measurement of high-sensitivity CTI
The high-sensitivity CTI index was calculated using the following formula: high-sensitivity CTI = 0.412 × Ln (high-sensitivity CRP [mg/L]) + TyG (22, 26–28). The TyG was calculated as Ln (triglyceride [mg/dL] × fasting blood glucose [mg/dL] / 2) (15–18). All samples were obtained within 24 h after stroke onset.
Definition of outcome
Modified Rankin Scale (mRS), ranging from 0 to 6, is a standard scale to assess the independent status, where higher scores denote poorer independence. To assess functional outcomes in the stroke patients, mRS is widely utilized. At the 3-month follow-up, standardized assessments were performed by certified neurologists via either structured telephone interviews or in-person clinic visits. The primary study outcome was unfavorable functional outcome, which was defined as mRS score ranging from 3–6 (29–31). Meanwhile, favorable functional outcome was defined as mRS score ranging from 0–2 (32–34). What is more, for the purpose of sensitivity analyses, non-excellent functional outcome was termed as mRS 2–6, and excellent functional outcome was termed as mRS 0–1 (35, 36).
Statistical analysis
Statistical analyses were performed using R version 4.5.0 software.1 All participants were categorized into four groups according to the quartiles of high-sensitivity CTI calculated from the present study sample, with cut-off values of 8.743, 9.238, and 9.880 corresponding to Q1-Q4, respectively. Categorical variables were expressed as n (%). Group differences across high-sensitivity CTI quartiles (Q1-Q4) were assessed using ANOVA for normally distributed continuous variables, Kruskal-Wallis tests for non-normally distributed continuous variables, and χ2 tests or Fisher’s exact tests for categorical variables, as appropriate. We used violin plots to show the distribution of high-sensitivity CTI between the favorable outcome group and unfavorable outcome group. Ordinal logistic regression was used to assess the trend in mRS distribution across CTI quartiles. CTI quartiles were coded as an ordinal numeric variable (1–4) to test the linear trend across quartiles. The proportional-odds assumption was confirmed by the Brant-test for this trend-testing model.
Logistic regression models were performed to estimate associations between the levels of high-sensitivity CTI and unfavorable functional outcome in elderly AIS patients undergoing IVT. Model 1 was the crude model. Model 2 was adjusted for age, gender, current smoking and current alcohol drinking. Model 3 was adjusted for age, gender, current smoking, current alcohol drinking, history of hypertension, history of diabetes mellitus, history of atrial fibrillation, history of coronary artery disease, antiplatelet agents, anticoagulants, NIHSS group, OTT, PAO and stroke subtype.
We further evaluated the trends and strength of the association between the levels of high-sensitivity CTI and unfavorable functional outcome in elderly AIS patients undergoing IVT using restricted cubic splines (RCS) with 3 knots (at the 10th, 50th, and 90th percentiles), adjusted for the covariates in Model 3. Prespecified subgroup analyses were conducted to test the consistency of the association between high-sensitivity CTI and unfavorable functional outcome. Stratified covariates included age, gender, current smoking, current drinking, history of hypertension, history of diabetes mellitus, history of coronary artery disease, and history of atrial fibrillation. The fully-adjusted Model 3 was refitted within each stratum. Between-stratum heterogeneity was evaluated via Z-test of coefficient differences to obtain p-values for heterogeneity.
Receiver operating characteristic (ROC) curve analysis was used to test the overall discriminative ability of high-sensitivity CTI for unfavorable functional outcome in elderly AIS patients undergoing IVT. The Youden index was calculated as Sensitivity + Specificity – 1. To test the robustness of our findings, we conducted sensitivity analyses by excluding patients with PAO and assessed the relationships only among patients without PAO. Furthermore, we explored the relationship between the levels of high-sensitivity CTI and non-excellent functional outcome in elderly AIS patients treated with IVT. A two-tailed value of p < 0.05 was considered statistically significant.
Results
Baseline characteristics
From August 2020 to February 2025, we screened 1,821 AIS patients treated with IVT in this study, initially. A total of 1,113 patients were excluded for the following reasons: 32 patients were excluded for severe inflammatory or infectious diseases; 506 patients were excluded for incomplete data; 278 were excluded for loss to follow-up over the following 3 months after admission. In addition, 297 patients younger than 65 years of age were excluded. There were 708 participants (418 men and 290 women) with a mean age of 75.52 years included in the present study, of whom 231 (32.6%) developed unfavorable functional outcome at 3 months. Table 1 showed the baseline characteristics of the study participants according to the quartiles of high-sensitivity CTI levels. Below are the notable distinctions among these four groups: history of diabetes mellitus (p < 0.001), history of hypertension (p = 0.037), history of atrial fibrillation (p = 0.045), stroke subtype (p < 0.001) and functional outcome (p < 0.001).
Table 1.
Characteristics of participants based on the quartile of high-sensitivity CTI.
| Characteristics | high-sensitivity CTI | P | |||
|---|---|---|---|---|---|
| Q1 (n = 177) | Q2 (n = 177) | Q3 (n = 176) | Q4 (n = 178) | ||
| Age, (%) | 0.062 | ||||
| ≥75 | 89 (50.28) | 101 (57.06) | 89 (50.57) | 76 (42.70) | |
| 65–74 | 88 (49.72) | 76 (42.94) | 87 (49.43) | 102 (57.30) | |
| Gender, (%) | 0.509 | ||||
| Female | 72 (40.68) | 76 (42.94) | 77 (43.75) | 65 (36.52) | |
| Male | 105 (59.32) | 101 (57.06) | 99 (56.25) | 113 (63.48) | |
| Vascular risk factors, (%) | |||||
| Current Drinking Alcohol | 46 (25.99) | 42 (23.73) | 53 (30.11) | 43 (24.16) | 0.512 |
| Current Smoking | 53 (29.94) | 54 (30.51) | 53 (30.11) | 52 (29.21) | 0.996 |
| History of diabetes mellitus | 30 (16.95) | 29 (16.38) | 55 (31.25) | 81 (45.51) | <0.001 |
| History of hypertension | 120 (67.80) | 123 (69.49) | 138 (78.41) | 139 (78.09) | 0.037 |
| History of atrial fibrillation | 48 (27.12) | 40 (22.60) | 35 (19.89) | 27 (15.17) | 0.045 |
| History of coronary heart disease | 26 (14.69) | 29 (16.38) | 24 (13.64) | 25 (14.04) | 0.898 |
| Clinical assessment, (%) | |||||
| PAO | 56 (31.64) | 62 (35.03) | 63 (35.80) | 69 (38.76) | 0.574 |
| NIHSS group | 0.135 | ||||
| Mild (0–4) | 85 (48.02) | 70 (39.55) | 73 (41.48) | 65 (36.52) | |
| Moderate (5–10) | 45 (25.42) | 53 (29.94) | 51 (28.98) | 44 (24.72) | |
| Moderately Severe (11–20) | 40 (22.60) | 48 (27.12) | 38 (21.59) | 57 (32.02) | |
| Severe (≥21) | 7 (3.95) | 6 (3.39) | 14 (7.95) | 12 (6.74) | |
| Stroke subtype (TOAST) | <0.001 | ||||
| LAA | 56 (31.64) | 74 (41.81) | 76 (43.18) | 97 (54.49) | |
| CE | 74 (41.81) | 57 (32.20) | 43 (24.43) | 37 (20.79) | |
| SAO | 41 (23.16) | 39 (22.03) | 41 (23.30) | 39 (21.91) | |
| Other/Unknown | 6 (3.39) | 7 (3.95) | 16 (9.09) | 5 (2.81) | |
| OTT | 0.380 | ||||
| <3 h | 123 (69.49) | 133 (75.14) | 119 (67.61) | 121 (67.98) | |
| 3–4.5 h | 54 (30.51) | 44 (24.86) | 57 (32.39) | 57 (32.02) | |
| Medication use, (%) | |||||
| Antiplatelet therapy | 0.543 | ||||
| Never use | 23 (12.99) | 17 (9.60) | 14 (7.95) | 20 (11.24) | |
| New Initiation Post-Stroke | 121 (68.36) | 130 (73.45) | 138 (78.41) | 129 (72.47) | |
| Use prior to stroke | 33 (18.64) | 30 (16.95) | 24 (13.64) | 29 (16.29) | |
| Anticoagulation therapy | 0.261 | ||||
| Never use | 119 (67.23) | 122 (68.93) | 124 (70.45) | 134 (75.28) | |
| New initiation post-stroke | 46 (25.99) | 51 (28.81) | 45 (25.57) | 38 (21.35) | |
| Use prior to stroke | 12 (6.78) | 4 (2.26) | 7 (3.98) | 6 (3.37) | |
| Functional outcome, (%) | <0.001 | ||||
| Favorable | 134 (75.71) | 133 (75.14) | 112 (63.64) | 98 (55.06) | |
| Unfavorable | 43 (24.29) | 44 (24.86) | 64 (36.36) | 80 (44.94) | |
High-sensitivity CTI, high-sensitivity C-reactive protein-triglyceride-glucose index; NIHSS, National Institutes of Health Stroke Scale; OTT, onset to treatment time; PAO, proximal arterial occlusion; TOAST, Trial of Org 10,172 in Acute Stroke Treatment; LAA, large-artery atherosclerosis; CE, cardioembolism; SAO, small-artery occlusion.
Comparisons across high-sensitivity CTI quartiles were performed using ANOVA, Kruskal-Wallis test, χ2 test, or Fisher’s exact test as appropriate.
Association between high-sensitivity CTI and outcome
Figure 1 illustrated the violin plots of high-sensitivity CTI levels between the favorable functional outcome group and the unfavorable functional outcome group. An unadjusted ordinal logistic regression was performed was performed to test the trend across high-sensitivity CTI quartiles for the full distribution of mRS scores. A significant shift toward poorer functional outcomes was observed with rising CTI quartiles among elderly AIS patients receiving intravenous thrombolysis (P for trend < 0.001; Figure 2).
Figure 1.
The violin plot in distribution of high-sensitivity CTI between the favorable functional outcome group and the unfavorable functional outcome group.
Figure 2.
Distribution of 3-month modified Rankin Scale score according to high-sensitivity CTI quartiles among elderly acute ischemic stroke patients undergoing IVT by unadjusted ordinal logistic regression.
Figure 3 showed the results of logistic regression. In the model 1, the levels of high-sensitivity CTI were associated with unfavorable functional outcome at 3 months in elderly AIS patients undergoing IVT (odds ratio [OR], 1.61; 95% confidence interval [CI], 1.32–1.96, p < 0.001), when we serve the levels of high-sensitivity CTI as continuous variables. What is more, the third quartile (OR, 1.78; 95% CI, 1.12–2.82, p = 0.014) and fourth quartile (OR, 2.54; 95% CI, 1.62–4.00, p < 0.001) of high-sensitivity CTI (first quartile used as the reference value) were also linked to unfavorable functional outcome. Additionally, similar results were found in model 2 and model 3. In model 2, the levels of high-sensitivity CTI, as continuous variables, were related to unfavorable functional outcome at 3 months (OR, 1.69; 95% CI, 1.38–2.07, p < 0.001). The third quartile (OR, 1.82; 95% CI, 1.13–2.91, p =0.013) and fourth quartile (OR, 2.85; 95% CI, 1.79–4.55, p < 0.001) of high-sensitivity CTI (first quartile used as the reference value) were linked to unfavorable functional outcome. In Model 3, the levels of high-sensitivity CTI, as continuous variables, were related to unfavorable functional outcome at 3 months (OR, 1.64; 95% CI, 1.28–2.11, p < 0.001). The third quartile (OR, 1.93; 95% CI, 1.10–3.40, p = 0.023) and fourth quartile (OR, 2.62; 95% CI, 1.48–4.64, p < 0.001) of high-sensitivity CTI (first quartile used as the reference value) were linked to unfavorable functional outcome.
Figure 3.
OR and 95% CI of unfavorable functional outcome according to high-sensitivity CTI among elderly acute ischemic stroke patients undergoing IVT. Model 1: Crude model; Model 2: Adjusted for age and gender, current smoking and current drinking alcohol; Model 3: Adjusted for age and gender, current smoking, current drinking alcohol, history of hypertension, history of diabetes mellitus, history of atrial fibrillation, history of coronary artery disease, antiplatelet, anticoagulation, NIHSS group, OTT, PAO and stroke subtype.
RCS analysis
According to the results of RCS analysis (Figure 4A), high-sensitivity CTI was associated with unfavorable 3-month functional outcome after IVT (P for overall < 0.001), and no statistically significant nonlinear association was detected (P for nonlinearity = 0.743).
Figure 4.
Relationship of high-sensitivity CTI with functional outcome in elderly acute ischemic stroke patients undergoing IVT. (A) Relationship of high-sensitivity CTI with unfavorable functional outcome (mRS 3–6). (B) Relationship of high-sensitivity CTI with unfavorable functional outcome (mRS 3–6) excluding PAO patients. (C) Relationship of high-sensitivity CTI with non-excellent functional outcome (mRS 2–6).
Subgroup analyses
Subgroup analyses demonstrated that higher high-sensitivity CTI was consistently associated with elevated risk of unfavorable functional outcome across all examined strata (Figure 5). No significant interactions were found between high-sensitivity CTI and these subgroup factors (all P for interaction > 0.05).
Figure 5.
Subgroup analysis of the association between high-sensitivity CTI and unfavorable functional outcome.
ROC curve analysis
To further evaluate the discriminatory capacity of high-sensitivity CTI for unfavorable 3-month functional outcomes after IVT, we performed ROC curve analysis (Figure 6A). The AUC of high-sensitivity CTI was 0.613 (95% CI 0.568–0.657), indicating modest discriminatory capacity for post-thrombolysis unfavorable functional outcome. The optimal cutoff value of high-sensitivity CTI derived from the maximum Youden’s index was 9.299, yielding a sensitivity of 0.610 and specificity of 0.591 at this threshold.
Figure 6.
ROC for high-sensitivity CTI for discrimination of functional outcome. (A) ROC for high-sensitivity CTI for discrimination of unfavorable functional outcome (mRS 3–6). (B) ROC for high-sensitivity CTI for discrimination of unfavorable functional outcome (mRS 3–6) excluding PAO patients. (C) ROC for high-sensitivity CTI for discrimination of non-excellent functional outcome (mRS 2–6).
Sensitivity analyses
We excluded the patients with PAO and performed the sensitivity analyses. As shown in Figure 7, after the adjustment for model 3, the fourth quartile (OR, 3.15; 95% CI, 1.47–6.75, p = 0.003) of high-sensitivity CTI (first quartile used as the reference value) were also related with unfavorable functional outcome in elderly AIS patients undergoing IVT. In addition, levels of high-sensitivity CTI, as continuous variables, were also linked to the unfavorable functional outcome in this model (OR, 1.92; 95% CI, 1.36–2.71, p < 0.001). Moreover, we observed an overall association between high-sensitivity CTI and unfavorable functional outcome (P for overall < 0.001), while no statistically significant nonlinear association was detected (P for nonlinearity = 0.262; Figure 4B). AUC of high-sensitivity CTI to discriminate post-thrombolysis unfavorable functional outcome was 0.635 (a sensitivity of 0.632 and a specificity of 0.610; Figure 6B). We also served non-excellent functional outcome (mRS 2–6) as the study outcome. In the Model 3, the third quartile (OR, 3.04; 95% CI, 1.76–5.26, p < 0.001) and fourth quartile (OR, 3.40; 95% CI, 1.94–5.96, p < 0.001) of high-sensitivity CTI (first quartile used as the reference value) were associated with non-excellent functional outcome (Figure 8). Levels of high-sensitivity CTI, as continuous variables, were also related with the non-excellent functional outcome in this model (OR, 1.75; 95% CI, 1.37–2.23, p < 0.001). What is more, we observed an overall association between high-sensitivity CTI and the non-excellent functional outcome (P for overall < 0.001), while no statistically significant nonlinear association was detected (P for nonlinearity = 0.453; Figure 4C). AUC of high-sensitivity CTI to discriminate post-thrombolysis non-excellent functional outcome was 0.619 (a sensitivity of 0.587 and a specificity of 0.632; Figure 6C).
Figure 7.
OR and 95% CI of unfavorable functional outcome according to high-sensitivity CTI among elderly acute ischemic stroke patients undergoing IVT, excluding the patients with PAO. Model 1: Crude model; Model 2: Adjusted for age and gender, current smoking and current drinking alcohol; Model 3: Adjusted for age and gender, current smoking, current drinking alcohol, history of hypertension, history of diabetes mellitus, history of atrial fibrillation, history of coronary artery disease, antiplatelet, anticoagulation, NIHSS group, OTT and stroke subtype.
Figure 8.
OR and 95% CI of non-excellent functional outcome (mRS 2–6) according to high-sensitivity CTI among elderly acute ischemic stroke patients undergoing IVT. Model 1: Crude model; Model 2: Adjusted for age and gender, current smoking and current drinking alcohol; Model 3: Adjusted for age and gender, current smoking, current drinking alcohol, history of hypertension, history of diabetes mellitus, history of atrial fibrillation, history of coronary artery disease, antiplatelet, anticoagulation, NIHSS group, OTT, PAO and stroke subtype.
Discussion
Our study focused on the link between high-sensitivity CTI levels and the functional outcomes in elderly AIS patients who received IVT. The results of our study showed that the elevated levels of high-sensitivity CTI might be related with the increased probability of the unfavorable functional outcomes in elderly AIS patients treated with IVT in diverse regression models. Thus, these findings imply that the levels of high-sensitivity CTI may have a particular association with the post-thrombolysis prognosis of elderly AIS patients.
It is reported that TyG, calculated from triglycerides and blood glucose, is one of reliable biomarkers of IR (37, 38). IR is associated with metabolic diseases, for instance, hyperglycemia and dyslipidemia. These diseases mentioned above are proved to be the risk factors of ischemic stroke, the frequent illness in the elderly. The results of an 11-year follow-up study implied that high levels of both baseline and long-term updated cumulative average TyG index might be able to predict ischemic stroke, independently (39). The research utilizing data from the China National Stroke Registry II, indicated that TyG could be related to the recurrence of ischemic stroke and all-cause death in AIS patients with type-2 diabetes mellitus (40). Moreover, TyG was identified as an independent risk factor in critically ill patients with first-ever stroke (18). In addition, many studies have confirmed the role of inflammatory response in the pathophysiological mechanism of stroke (19–21, 41, 42). High-sensitivity CRP is considered as one of convenient and simple inflammatory biomarkers. One previous study showed that high-sensitivity CRP might be a risk factor for ischemic stroke and retinal artery occlusion (43). The analysis of UK Biobank data revealed that elevated and remaining high levels of CRP were related to both any stroke as well as ischemic stroke (44). Wang et al. found that high-sensitivity CRP could serve as the biomarker of stroke recurrence (45). CRP also is report to be linked to carotid restenosis in elderly AIS patients undergoing interventional surgery (46). Hence, CTI, which integrates the advantages of CRP and TyG, may be able to play a critical role in the mechanism of ischemic stroke. One recent large-scale study, analyzing the data from China Health and Retirement Longitudinal Study (CHARLS), has identified that elevated levels of CTI may be linked to an increased risk of stroke in people with normal glucose regulation or prediabetes mellitus (24), which perhaps highlights the potential role of CTI in stroke. Another study based on CHARLS showed the relationship between CTI levels and the risk of stroke in the patients with hypertension (47).
The violin plot suggested that there was a significant difference in the levels of high-sensitivity CTI between the favorable functional outcome group and the unfavorable functional outcome group. Owing to the intricateness of the prognosis of elderly AIS patients, we developed several regression models and included high-sensitivity CTI as either a categorical variable or a continuous variable into these regression models, all of which depicted that the increased levels of high-sensitivity CTI might be linked to the worse prognosis of elderly AIS patients who received IVT. Subgroup analyses showed that the association of high-sensitivity CTI with post-thrombolysis functional outcome was qualitatively consistent across different clinical strata, with no significant subgroup interactions detected. Furthermore, we preformed the following sensitivity analyses: excluding the elderly AIS patients with PAO, or regarding mRS score > 1 as the outcome. After these sensitivity analyses, we observed analogous results, which denoted that further research is needed in the future to explore the role of high-sensitivity CTI in the prognosis of elderly AIS patients undergoing IVT.
There are several potential limitations in this study. First, given that all participants in our study were Chinese elderly patients undergoing IVT, the relationship between the levels of high-sensitivity CTI and functional outcome in elderly AIS patients treated with IVT may not be able to be generalized to non-Chinese populations. Second, some risk factors that may be linked to post-thrombolysis functional outcome, such as stromal cell-derived factor (SDF)-1 (48) and brain-derived tau (49), were unavailable in this study. These biomarkers might serve as potential independent predictors for functional outcome in elderly AIS patients treated with IVT. Third, the levels of high-sensitivity CTI might be able to vary during hospitalization. Our future research needs dynamic examination of high-sensitivity CTI. Fourth, considerable patient exclusions may introduce potential selection bias, and categorization of NIHSS and OTT could further cause information loss and residual confounding, as the adjustment for post-baseline in-hospital antiplatelet and anticoagulant therapies which may bring over-adjustment bias. Fifth, high-sensitivity CTI only showed modest discriminatory performance for functional outcome. In addition, although all centers adhered to standardized routine clinical laboratory quality-control procedures for biomarker testing, measurements were undertaken on distinct platforms with different reagents, which may introduce inter-center measurement heterogeneity. Despite these limitations mentioned above, the present analysis adds evidence regarding the association between high-sensitivity CTI and 3-month functional outcome specifically among elderly AIS patients treated with IVT, a high-risk subgroup that has received limited dedicated investigation (50).
To sum up, our research indicated that high-sensitivity CTI, a readily accessible biomarker, was associated with functional outcomes in older AIS patients receiving IVT. To confirm these results about this conclusion, further research will be needed.
Acknowledgments
We express our gratitude to all the researchers and patients who participated in this study.
Funding Statement
The author(s) declared that financial support was received for this work and/or its publication. This study was supported by the Young Scientific and Technological Talents Promotion Project of Jiangsu Association for Science and Technology (JSTJ-2024-373) and Research Project of Nantong Health Commission (MS2023011).
Edited by: Guangdong Wang, First Affiliated Hospital of Xi’an Jiaotong University, China
Reviewed by: Huasheng Lv, Shandong Provincial Qianfoshan Hospital, China
Chenyang Li, Fujian University of Traditional Chinese Medicine, China
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics statement
The studies involving humans were approved by Ethics Committee of Nanjing First Hospital, Nantong Third People’s Hospital, and the Affiliated Hospital of Nantong University. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
Author contributions
PG: Writing – review & editing, Conceptualization, Writing – original draft, Funding acquisition, Software, Formal analysis, Visualization. GC: Writing – review & editing, Writing – original draft, Formal analysis, Conceptualization, Software, Validation, Visualization. SC: Visualization, Data curation, Validation, Investigation, Writing – review & editing. YF: Validation, Investigation, Writing – review & editing. YJ: Data curation, Writing – review & editing, Validation, Investigation. YZ: Visualization, Investigation, Validation, Data curation, Writing – review & editing. JL: Investigation, Writing – review & editing, Validation. JS: Writing – review & editing, Investigation, Data curation, Validation. XZ: Methodology, Supervision, Investigation, Writing – review & editing. RZ: Writing – review & editing, Investigation, Software, Data curation, Project administration, Methodology. YW: Funding acquisition, Writing – review & editing, Supervision.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that Generative AI was not used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
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
- 1.Chen X, Giles J, Yao Y, Yip W, Meng Q, Berkman L, et al. The path to healthy ageing in China: a Peking University-lancet commission. Lancet. (2022) 400:1967–2006. doi: 10.1016/S0140-6736(22)01546-X, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Luo X, Chen S, Luo W, Li Q, Zhu Y, Li J. Comparison of the clinical outcomes between reperfusion and non-reperfusion therapy in elderly patients with acute ischemic stroke. Clin Interv Aging. (2024) 19:1247–58. doi: 10.2147/CIA.S464010, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Huang M, Wang W, Li WL, Chen YQ, Chen XT, Liu Y, et al. Construction and evaluation of a nomogram model for predicting the risk of hospital-acquired pneumonia in elderly patients with acute ischemic stroke. BMC Geriatr. (2025) 25:340. doi: 10.1186/s12877-025-05936-3, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Zhang Y, Chi L, Shu H, Zhou Q, Zhang S, Huang X, et al. Safety of alteplase intravenous thrombolysis and influencing factors of clinical outcome in elderly patients with acute ischemic stroke. BMC Neurol. (2024) 24:464. doi: 10.1186/s12883-024-03973-w, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Ma H, Campbell BCV, Parsons MW, Churilov L, Levi CR, Hsu C, et al. Thrombolysis guided by perfusion imaging up to 9 hours after onset of stroke. N Engl J Med. (2019) 380:1795–803. doi: 10.1056/NEJMoa1813046, [DOI] [PubMed] [Google Scholar]
- 6.Thomalla G, Simonsen CZ, Boutitie F, Andersen G, Berthezene Y, Cheng B, et al. MRI-guided thrombolysis for stroke with unknown time of onset. N Engl J Med. (2018) 379:611–22. doi: 10.1056/NEJMoa1804355, [DOI] [PubMed] [Google Scholar]
- 7.Torbey MT, Jauch E, Liebeskind DSStroke Advisory Board of the National Stroke AssociationAcute. Thrombolysis 3 to 4.5 hours after acute ischemic stroke. N Engl J Med. (2008) 359:2839–41. doi: 10.1056/NEJMc082179 [DOI] [PubMed] [Google Scholar]
- 8.Fan Y, Shi G, Wang S, Lu Y, Kong X, Chen L. Prognostic outcome of intravenous thrombolysis in elderly patients aged ≥ 60 years with acute ischemic stroke by ASTRAL and THRIVE scales. J Thromb Thrombolysis. (2025) 58:120–5. doi: 10.1007/s11239-024-03039-1, [DOI] [PubMed] [Google Scholar]
- 9.Nighoghossian N, Abbas F, Cho TH, Geraldo AF, Cottaz V, Janecek E, et al. Impact of leukoaraiosis on parenchymal hemorrhage in elderly patients treated with thrombolysis. Neuroradiology. (2016) 58:961–7. doi: 10.1007/s00234-016-1725-7, [DOI] [PubMed] [Google Scholar]
- 10.Ding PF, Zhang HS, Wang J, Gao YY, Mao JN, Hang CH, et al. Insulin resistance in ischemic stroke: mechanisms and therapeutic approaches. Front Endocrinol. (2022) 13:1092431. doi: 10.3389/fendo.2022.1092431, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Wieberdink RG, Koudstaal PJ, Hofman A, Witteman JC, Breteler MM, Ikram MA. Insulin resistance and the risk of stroke and stroke subtypes in the nondiabetic elderly. Am J Epidemiol. (2012) 176:699–707. doi: 10.1093/aje/kws149 [DOI] [PubMed] [Google Scholar]
- 12.Zhao J, Li N, Li S, Dou J. The predictive significance of the triglyceride-glucose index in forecasting adverse cardiovascular events among type 2 diabetes mellitus patients with co-existing hyperuricemia: a retrospective cohort study. Cardiovasc Diabetol. (2025) 24:218. doi: 10.1186/s12933-025-02783-9, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Ding R, Cheng E, Wei M, Pan L, Ye L, Han Y, et al. Association between triglyceride-glucose index and mortality in critically ill patients with atrial fibrillation: a retrospective cohort study. Cardiovasc Diabetol. (2025) 24:138. doi: 10.1186/s12933-025-02697-6, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Lin L, Lin Y, Ling X, Zhang Z, Guo X, Ding Z. The prognostic value of the triglyceride-glucose index in predicting recurrence of acute pancreatitis: a retrospective cohort study. Nutr Metab. (2025) 22:55. doi: 10.1186/s12986-025-00956-7, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Lu L, Chen Y, Liu B, Li X, Wang J, Nie Z, et al. Association between cumulative changes of the triglyceride glucose index and incidence of stroke in a population with cardiovascular-kidney-metabolic syndrome stage 0-3: a nationwide prospective cohort study. Cardiovasc Diabetol. (2025) 24:202. doi: 10.1186/s12933-025-02754-0, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Qian J, Chi Q, Qian C, Fan X, Ding W, Wang T, et al. Atherogenic index of plasma and triglyceride-glucose index mediate the association between stroke and all-cause mortality: insights from the lipid paradox. Lipids Health Dis. (2025) 24:173. doi: 10.1186/s12944-025-02586-7, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Zhu R, He Q, Wang R, Tai Y, Peng C, Wu C. Triglyceride-glucose index is a significant predictor of hospital mortality in non-diabetic critically ill patients with ischemic stroke: a retrospective cohort study of the MIMIC-IV database. Acta Diabetol. (2025) 62:1659–69. doi: 10.1007/s00592-025-02502-6, [DOI] [PubMed] [Google Scholar]
- 18.Chen Y, Yang Z, Liu Y, Li Y, Zhong Z, McDowell G, et al. Exploring the prognostic impact of triglyceride-glucose index in critically ill patients with first-ever stroke: insights from traditional methods and machine learning-based mortality prediction. Cardiovasc Diabetol. (2024) 23:443. doi: 10.1186/s12933-024-02538-y, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Zhang X, Gong P, Zhao Y, Wan T, Yuan K, Xiong Y, et al. Endothelial caveolin-1 regulates cerebral thrombo-inflammation in acute ischemia/reperfusion injury. EBioMedicine. (2022) 84:104275. doi: 10.1016/j.ebiom.2022.104275, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Wang Q, Wang D, Gao Y, Jiang J, Li M, Li S, et al. Impaired membrane lipids in ischemic stroke: a key player in inflammation and thrombosis. J Neuroinflammation. (2025) 22:144. doi: 10.1186/s12974-025-03464-w, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Gong P, Liu Y, Gong Y, Chen G, Zhang X, Wang S, et al. The association of neutrophil to lymphocyte ratio, platelet to lymphocyte ratio, and lymphocyte to monocyte ratio with post-thrombolysis early neurological outcomes in patients with acute ischemic stroke. J Neuroinflammation. (2021) 18:51. doi: 10.1186/s12974-021-02090-6, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Huang C, You H, Zhang Y, Li Z, Li M, Feng X, et al. Association between C-reactive protein-triglyceride glucose index and depressive symptoms in American adults: results from the NHANES 2005 to 2010. BMC Psychiatry. (2024) 24:890. doi: 10.1186/s12888-024-06336-4, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Zhou Y, Lin H, Weng X, Dai H, Xu J. Correlation between hs-CRP-triglyceride glucose index and NAFLD and liver fibrosis. BMC Gastroenterol. (2025) 25:252. doi: 10.1186/s12876-025-03870-7, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Huo G, Tang Y, Liu Z, Cao J, Yao Z, Zhou D. Association between C-reactive protein-triglyceride glucose index and stroke risk in different glycemic status: insights from the China health and retirement longitudinal study (CHARLS). Cardiovasc Diabetol. (2025) 24:142. doi: 10.1186/s12933-025-02686-9, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Adams HP, Jr, Bendixen BH, Kappelle LJ, Biller J, Love BB, Gordon DL, et al. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of org 10172 in acute stroke treatment. Stroke. (1993) 24:35–41. doi: 10.1161/01.str.24.1.35, [DOI] [PubMed] [Google Scholar]
- 26.Ren Y, Xu R, Zhang J, Jin Y, Zhang D, Wang Y, et al. Association between the C-reactive protein-triglyceride-glucose index and endometriosis: a cross-sectional study using data from the national health and nutrition examination survey, 1996-2006. BMC Womens Health. (2025) 25:13. doi: 10.1186/s12905-024-03541-x, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Xu M, Zhang L, Xu D, Shi W, Zhang W. Usefulness of C-reactive protein-triglyceride glucose index in detecting prevalent coronary heart disease: findings from the National Health and nutrition examination survey 1999-2018. Front Cardiovasc Med. (2024) 11:1485538. doi: 10.3389/fcvm.2024.1485538, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Zhang B, Gu Y, Chen Y, Xia W, Shao N, Zhuang Q, et al. Association between C-reactive protein-triglyceride glucose index and testosterone levels among adult men: analyses of NHANES 2015-2016 data. Sex Med. (2025) 13:qfaf012. doi: 10.1093/sexmed/qfaf012, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Qu Y, Jin H, Abuduxukuer R, Qi S, Si XK, Zhang P, et al. The association between serum S100β levels and prognosis in acute stroke patients after intravenous thrombolysis: a multicenter prospective cohort study. BMC Med. (2024) 22:304. doi: 10.1186/s12916-024-03517-6, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Zhang X, Cao L, Wu S, Wang D, Wang H, Zhang D, et al. Early intensive blood pressure management after endovascular treatment in ischaemic stroke (IDENTIFY): a multicentre, open-label, blinded-endpoint, randomised controlled trial. Lancet Regional Health Western Pacific. (2025) 59:101589. doi: 10.1016/j.lanwpc.2025.101589, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Cheng X, Cai W, Li C, Shen X, Li J, Huang L, et al. Imaging-based brain frailty predicts unfavorable outcomes in acute ischemic stroke. J Am Heart Assoc. (2025) 14:e039790. doi: 10.1161/JAHA.124.039790, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Nybondas M, Martinez-Majander N, Ringleb P, Ungerer M, Gumbinger C, Trüssel S, et al. Intravenous thrombolysis in young adults with ischemic stroke: a cohort study from the international TRISP collaboration. Eur Stroke J. (2025) 10:721–9. doi: 10.1177/23969873241304305 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Marko M, Miksova D, Haidegger M, Schneider J, Ebner J, Lang MB, et al. Trends in sex differences of functional outcome after intravenous thrombolysis in patients with acute ischemic stroke. Int J Stroke. (2024) 19:1147–54. doi: 10.1177/17474930241273696, [DOI] [PubMed] [Google Scholar]
- 34.Marko M, Miksova D, Ebner J, Lang M, Serles W, Sommer P, et al. Temporal trends of functional outcome in patients with acute ischemic stroke treated with intravenous thrombolysis. Stroke. (2022) 53:3329–37. doi: 10.1161/STROKEAHA.121.038400 [DOI] [PubMed] [Google Scholar]
- 35.Tinchon A, Mikšová D, Lang W, Krebs S, Freydl E, Baumgartner C, et al. Timing and outcome prediction of intravenous thrombolysis in posterior circulation stroke: insights from the Austrian stroke unit registry. Eur Stroke J. (2025):23969873251341770. doi: 10.1177/23969873251341770 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Palazzo P, Padlina G, Dobrocky T, Strambo D, Seners P, Mechtouff L, et al. Relevance of National Institutes of Health stroke scale subitems for best revascularization therapy in minor stroke patients with large vessel occlusion: an observational multicentric study. Eur J Neurol. (2023) 30:3741–50. doi: 10.1111/ene.16009, [DOI] [PubMed] [Google Scholar]
- 37.Minh HV, Tien HA, Sinh CT, Thang DC, Chen CH, Tay JC, et al. Assessment of preferred methods to measure insulin resistance in Asian patients with hypertension. J Clin Hypertens. (2021) 23:529–37. doi: 10.1111/jch.14155, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Vasques AC, Novaes FS, de Oliveira MdS, Souza JR, Yamanaka A, Pareja JC, et al. TyG index performs better than HOMA in a Brazilian population: a hyperglycemic clamp validated study. Diabetes Res Clin Pract. (2011) 93:e98–e100. doi: 10.1016/j.diabres.2011.05.030 [DOI] [PubMed] [Google Scholar]
- 39.Wang A, Wang G, Liu Q, Zuo Y, Chen S, Tao B, et al. Triglyceride-glucose index and the risk of stroke and its subtypes in the general population: an 11-year follow-up. Cardiovasc Diabetol. (2021) 20:46. doi: 10.1186/s12933-021-01238-1, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Liu D, Yang K, Gu H, Li Z, Wang Y, Wang Y. Predictive effect of triglyceride-glucose index on clinical events in patients with acute ischemic stroke and type 2 diabetes mellitus. Cardiovasc Diabetol. (2022) 21:280. doi: 10.1186/s12933-022-01704-4, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Liu Q, Shi K, Wang Y, Shi FD. Neurovascular inflammation and complications of thrombolysis therapy in stroke. Stroke. (2023) 54:2688–97. doi: 10.1161/STROKEAHA.123.044123, [DOI] [PubMed] [Google Scholar]
- 42.Monsour M, Borlongan CV. The central role of peripheral inflammation in ischemic stroke. J Cerebral Blood Flow Metabol. (2023) 43:622–41. doi: 10.1177/0271678X221149509, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Wang Y, Zhang X, Liang A, Niu Y, Chen D, Du Z, et al. High-sensitivity C-reactive protein and risk of retinal artery occlusion and ischaemic stroke: a cross-cohort study. Br J Ophthalmol. (2025) 109:1081–7. doi: 10.1136/bjo-2023-325044, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Ling Y, Yuan S, Cheng H, Tan S, Huang X, Tang Y, et al. Exploring the link between C-reactive protein change and stroke risk: insights from a prospective cohort study and genetic evidence. J Am Heart Assoc. (2025) 14:e038086. doi: 10.1161/JAHA.124.038086, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Wang L, Yuan C, Hatsukami T, Zhao X, Gao P, Sui B, et al. Incremental value of serum inflammatory biomarkers to carotid atherosclerotic plaque burden for prediction of ipsilateral ischemic stroke recurrence. Acta Radiologica. (2025) 66:1047–56. doi: 10.1177/02841851251340607, [DOI] [PubMed] [Google Scholar]
- 46.Wu X, Wang X, Lin H, Zhang Y, Jiang Y, Jiang B. Development and application of a machine learning-based predictive model for carotid restenosis after interventional surgery in elderly ischemic stroke patients. Neurologist. (2025) 30:365–72. doi: 10.1097/NRL.0000000000000627, [DOI] [PubMed] [Google Scholar]
- 47.Tang S, Wang H, Li K, Chen Y, Zheng Q, Meng J, et al. C-reactive protein-triglyceride glucose index predicts stroke incidence in a hypertensive population: a national cohort study. Diabetol Metab Syndr. (2024) 16:277. doi: 10.1186/s13098-024-01529-z, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.You S, Chen H, Miao M, Du J, Che B, Xu T, et al. Prognostic significance of plasma SDF-1 in acute ischemic stroke patients with diabetes mellitus: the CATIS trial. Cardiovasc Diabetol. (2023) 22:274. doi: 10.1186/s12933-023-01996-0, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Stanne TM, Gonzalez-Ortiz F, Brännmark C, Jood K, Karikari T, Blennow K, et al. Association of Plasma Brain-Derived tau with Functional Outcome after Ischemic Stroke. Neurology. (2024) 102:e209129. doi: 10.1212/WNL.0000000000209129, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Liu X, Zheng X, Chen Z, Chen H, Xie M, Zhang J. Associations of inflammation-related nutritional and metabolic status indices CAR and CTI with 90-day unfavorable functional outcomes in patients with acute ischemic stroke. Front Nutr. (2026) 13:1790922. doi: 10.3389/fnut.2026.1790922, [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.