Unveiling the dual threat: combined elevated triglyceride-glucose index and intracranial arterial stenosis burden for enhanced stroke risk stratification

Background: The relationship between an elevated triglyceride-glucose index (TyG) combined with intracranial arterial stenosis (ICAS) and stroke recurrence remains incompletely understood. This study investigated how varying ICAS burdens influence the prognosis of ischemic stroke patients with different TyG levels.

Methods: This prospective cohort study analyzed a consecutively enrolled population of 712 acute ischemic stroke patients, with intracranial atherosclerotic burden quantitatively evaluated via computed tomography angiography (CTA) or three-dimensional time-of-flight magnetic resonance angiography (MRA), and stenosis severity graded according to WASID criteria. Participants were categorized into four mutually exclusive subgroups based on TyG index quartiles and ICAS status (presence/absence). To investigate exposure-response gradients, further stratification incorporated both ICAS severity (no/mild [<50%], moderate [50–69%], or severe [70–99% or occlusion] stenosis) and vascular involvement (single vs. multiple affected arterial territories). The primary endpoint was defined as ischemic stroke recurrence within a standardized 90-day follow-up period. Cox proportional hazards models examined adjusted associations between TyG/ICAS status and outcomes, complemented by Kaplan-Meier curves with log-rank tests for time-to-event visualization. Mechanistic pathways were delineated through causal mediation analysis employing Quasi-Bayesian approximation (100 Monte Carlo simulations) to quantify mediating effects between metabolic dysfunction and cerebrovascular outcomes.

Conclusion: The synergistic combination of elevated TyG index and ICAS burden demonstrated superior prognostic value for 3-month stroke recurrence risk compared to either parameter independently, suggesting that concurrent assessment of insulin resistance (via TyG index) and vascular imaging biomarkers (via ICAS burden) may refine early risk stratification. This finding underscores the importance of integrating systemic metabolic dysfunction with structural cerebrovascular pathology to identify patients at heightened vulnerability during the critical post-stroke recovery window.

Introduction

Insulin resistance (IR) is an independent risk factor for atherosclerosis, coronary heart disease and stroke, affecting a significant proportion of the general population (1–3). A study of 72 non-diabetic patients with ischemic stroke found that 50% had significant IR (4). Meta-analysis data showed a 2.55-fold increased risk of stroke in insulin-resistant populations compared with the general population after adjustment for conventional confounders (5). Similarly, population-based cohort studies have identified IR as a marker of increased stroke risk in non-diabetic individuals (6).

At present, IR is conspicuously absent from post-stroke risk stratification protocols. The relationship between type 2 diabetes mellitus (T2DM) and atherosclerotic plaque formation goes beyond simple hyperglycaemia. IR is a distinct risk factor for atherosclerosis, exerting several proatherogenic effects even in the absence of T2DM (7–9). This metabolic dysfunction may precede T2DM diagnosis by years or decades, with atherosclerotic processes potentially initiating long before diabetes becomes clinically apparent (10). While the hyperinsulinemic normoglycemic clamp test (HOMA-IR) remains the gold standard for measuring insulin resistance, its complexity and resource-intensive nature limit its utility in routine clinical practice and large-scale epidemiological research (11). The TyG index has emerged as a practical, cost-effective, and intuitive measure of insulin resistance, with substantial epidemiological evidence supporting its association with insulin resistance (12–14).

ICAS occurs frequently in Chinese populations, with previous studies demonstrating that increased ICAS burden correlates strongly with stroke recurrence, more severe neurological deficits, and poor clinical outcomes (15–19). Current clinical practice lacks definitive pharmacological or endovascular interventions to eliminate stroke recurrence risk in ICAS patients. Consequently, identifying those at highest risk for recurrence among the ICAS population remains clinically imperative.

Systemic metabolic disturbances, as reflected by the TyG index, and local haemodynamic disturbances, as reflected by ICAS, may have a dual synergistic effect: the former exacerbates blood–brain barrier disruption through a proinflammatory state, and the latter promotes expansion of the ischemic hemidarkness zone through hypoperfusion, which together accelerate the process of neuronal apoptosis. The ability of the TyG index in combination with ICAS to serve as a highly efficient and cost-effective risk stratification tool to accurately screen patients with a poor prognosis in stroke needs to be further evaluated in prospective cohorts. Patients with poor prognosis need further evaluation in prospective cohorts. Based on current clinical needs, the aim of this study was to investigate the synergistic effect of the TyG index combined with ICAS on short-term recurrence of ischemic stroke.

Methods

Ethics statement

This observational cohort study was approved by the Ethics Committee of Shanghai Tenth People’s Hospital and conducted in accordance with the Declaration of Helsinki guidelines.

Study population

The study cohort comprised patients diagnosed with acute ischemic stroke who underwent CTA or MRA evaluation at Shanghai Tenth People’s Hospital between August 2023 and August 2024. Ischemic stroke diagnosis was established through assessment of clinical manifestations and neuroimaging findings from brain CT or MRI scans. The final analysis included 712 patients. The X-tile software was employed to determine the optimal TyG threshold for distinguishing elevated stroke recurrence risk, which formed the basis for stratifying patients into lower and higher TyG groups.

Exclusion criteria encompassed: transient ischemic attack (TIA); treatment with recombinant tissue plasminogen activator (rt-PA) or endovascular intervention; presence of infections, neoplasms, hematological disorders, autoimmune conditions, chronic liver disease, or renal disease; and modified Rankin score (mRS) greater than 2.

Image acquisition

Cerebral vascular stenosis severity and distribution were evaluated using CTA or MRA imaging. Vascular assessment followed the Warfarin and Aspirin for Symptomatic Intracranial Arterial Stenosis (WASID) methodology (20). ICAS was defined as atherosclerotic stenosis or occlusion exceeding 50% in any major intracranial artery. The evaluated vessels included: middle cerebral artery (M1–M2 segments), anterior cerebral artery (A1–A2 segments), posterior cerebral artery (P1–P2 segments), vertebral artery (V4 segment), internal carotid artery (C6–C7 segments), and basilar artery.

ICAS severity was categorized as: no/mild stenosis (<50%), moderate stenosis (50–69%), or severe stenosis (70–99% or occlusion). ICAS burden was classified according to the number of affected vessels: no stenosis, single vessel involvement, or multiple vessel involvement.

Clinical data collection

Patient information was obtained from computerized medical records. Baseline characteristics included age, gender, body mass index (BMI), current smoking and alcohol consumption status, and admission systolic and diastolic blood pressures (SBP and DBP). Medical history documentation encompassed hypertension, diabetes, atrial fibrillation, previous stroke, and coronary atherosclerotic heart disease.

Laboratory assessments included total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and fasting blood glucose (FBG). Blood samples were collected after a minimum 8-h fasting period and analyzed within 24 h of admission. Stroke severity was assessed by qualified neurologists using the National Institutes of Health Stroke Scale (NIHSS). The TyG index was calculated as Ln[TG(mg/dL) × FBG(mg/dL)/2] (21).

Outcome assessment

Stroke recurrence was defined as an acute new focal neurological deficit lasting more than 24 h with an increase in NIHSS score of 4 or more; or on the basis of imaging evidence (new infarct or enlargement of the original infarct area) on MRI or CT scan (22).

Statistical analysis

Continuous variables were expressed as means with standard deviations and analyzed using t-tests, while categorical variables were presented as counts with percentages and evaluated using chi-square tests.

Cox regression models were employed to analyze the relationships between TyG index, ICAS status, and ischemic stroke recurrence. Two analytical models were constructed: Model 1 presented unadjusted analyses, while Model 2 incorporated adjustments for demographic characteristics (gender, age, SBP, DBP, current smoking status, current alcohol consumption), medical history (hypertension, diabetes, stroke, atrial fibrillation, coronary heart disease), hospitalization treatment (antiplatelet therapy, statin therapy), and laboratory parameters (HDL and LDL levels). Multicollinearity testing confirmed the absence of significant correlation among adjusted variables, with variance inflation factors and tolerance values presented in Supplementary Material.

The cumulative risk of stroke recurrence over the 90-day follow-up period was visualized using Kaplan–Meier curves. To assess the incremental predictive value of combining TyG index and ICAS status beyond conventional risk factors, we calculated the C-statistic, integrated discrimination improvement (IDI), and net reclassification index (NRI). Additionally, causal mediation analysis evaluated the magnitude of potential mediating effects between variables. All statistical tests were two-tailed, with p-values less than 0.05 considered statistically significant.

Results

Baseline characteristics

The baseline characteristics of the enrolled patients are shown in Table 1. Of the 712 patients enrolled, 353 (49.57%) were male. Comparative analysis showed that patients in the higher TyG index group were significantly younger (66 vs. 69 years, p < 0.001) and had higher BMI, SBP and DBP. Laboratory analyses showed increased levels of TG, glucose, TyG index, TC, HDL and LDL in this group (all p < 0.01). The lower TyG group had a lower prevalence of hypertension and diabetes. However, this group had a higher incidence of atrial fibrillation (all p < 0.01). Cardiogenic embolism was more common than atherosclerotic stroke in the lower TyG group, which also had lower proportions of severe stenosis and multiple vessel involvement.

Table 1

Table 1. Baseline characteristics based on TyG index and ICAS status.

On stratification by ICAS status, 56.8% of the ICAS group had severe stenosis and 51.4% multiple vessel involvement. ICAS patients were slightly older (68 ± 10 vs. 66 ± 10 years, p = 0.020). Other variables such as BMI, SBP, DBP, current smoking status, alcohol consumption and admission NIHSS scores showed no significant differences between the groups. However, ICAS patients had higher levels of TG, glucose, TyG index, HDL and LDL and an increased prevalence of hypertension, diabetes and previous stroke (all p < 0.01).

TyG index and stroke recurrence in different subgroups

Table 2 compares the associations between the TyG index and the risk of stroke recurrence in three subgroups: the total population (n = 712), the ICAS subgroup (n = 407), and the non-ICAS subgroup (n = 305). After multivariable adjustment, the TyG index as a continuous variable showed no significant association in the overall population (OR = 1.18, 95% CI: 0.72–1.95, p = 0.505); however, patients in the highest TyG quartile (Q4) had a 2.51-fold increased risk of recurrence compared to Q1 (OR = 2.51, 95% CI: 1.02–6.17, p = 0.045). In the ICAS subgroup, neither the continuous TyG index (OR = 1.28, 95% CI: 0.72–2.28, p = 0.400) nor the quartile-based analysis (Q4 vs. Q1: OR = 1.79, 95% CI: 0.60–5.29, p = 0.293) showed statistically significant associations with stroke recurrence risk. In the non-ICAS population, no significant differences in the risk of stroke recurrence were observed between subgroups.

Table 2

Table 2. TyG index and stroke recurrence in different subgroups.

Risk of stroke at 3 months by the status of TyG index and ICAS

During follow-up, 27 patients were lost to follow-up and 53 experienced recurrent ischemic stroke. Using patients with lower TyG levels and without ICAS as the reference group, those with elevated TyG index or ICAS demonstrated increased risk of 90-day stroke recurrence (Table 3). In the unadjusted model, patients with both higher TyG index and increased ICAS burden showed a 3.61-fold elevated recurrence risk (95% CI, 1.49–8.78; p = 0.005). This association persisted in the fully adjusted model, with a 3.21-fold increased risk (95% CI, 1.23–8.42; p = 0.018) in this group.

Table 3

Table 3. Effect of TyG and ICAS burden on stroke recurrence.

Analysis revealed a dose-dependent relationship between ICAS severity and new stroke risk (p for trend <0.001). Compared to the reference group, patients with severe ICAS demonstrated increased stroke risk regardless of TyG status, though the risk was higher in the elevated TyG group (adjusted hazard ratio 3.81, 95% CI 1.40–10.37) than in the lower TyG group (adjusted hazard ratio 3.32, 95% CI 1.15–9.58). Similar patterns emerged when patients were stratified by number of affected ICAS segments, as illustrated in the Kaplan–Meier curves (Figure 1).

Figure 1

Figure 1. Cumulative risk of stroke recurrence over 90 days. (A) Kaplan-Meier survival curves of the TyG index in combination with ICAS subgroups. (B) Kaplan-Meier survival curves of the TyG index in combination with ICAS segments. (C) Kaplan-Meier survival curves of the TyG index in combination with ICAS stenosis subgroups.

The value of TyG + ICAS improved the risk stratification of stroke recurrence

The addition of TyG index and ICAS status significantly improved the predictive capability of the conventional risk model, with the C-statistic increasing from 0.689 to 0.714 (p = 0.045). Furthermore, this combined approach demonstrated enhanced discrimination and reclassification ability (IDI: 1.60%, p = 0.024; Continuous-NRI: 17.60%, p = 0.036) (Table 4).

Table 4

Table 4. Improved risk stratification with combined TyG and ICAS assessment.

Causal mediation analysis

The total effect of ICAS on stroke recurrence showed statistical significance (coefficient 0.04827, 95% CI 0.00545–0.08880, p = 0.020). However, the indirect effect mediated through TyG demonstrated no statistical significance (coefficient −0.00126, 95% CI −0.00612 to 0.00271, p = 0.560). The direct effect of ICAS on stroke recurrence maintained significance (coefficient 0.04952, 95% CI 0.00590–0.09034, p = 0.020). The proportion mediated by TyG was estimated at −2.2% (95% CI −23.5, 7.4), indicating minimal mediation effect (Table 5).

Table 5

Table 5. Mediation analysis for the associations between ICAS and stroke recurrence.

Discussion

This study showed that patients with both an elevated TyG index and an increased ICAS burden had a significantly higher risk of recurrent ischemic stroke at 90 days. Our results showed a gradient relationship when stratified by stenosis severity and number of ICAS segments. Specifically, patients with an elevated TyG index and multiple ICAS stenoses had a 4.24-fold increased risk of recurrent stroke. Similarly, patients with severe stenosis had a 3.81-fold increased risk of short-term recurrence. This synergistic effect proved more predictive of new stroke risk than either elevated TyG levels or ICAS burden alone.

Several studies have validated the TyG index as a reliable indicator of insulin resistance (12, 23). The PURE study found that the TyG index independently predicted future cardiovascular mortality, myocardial infarction, stroke and T2DM, suggesting a fundamental role for IR in the pathogenesis of cardiovascular and metabolic diseases (14). Given the established diagnostic value of the TyG index for IR and these documented associations, we hypothesized a strong relationship between the TyG index and cerebrovascular disease.

There is increasing evidence that an elevated TyG index significantly influences vascular events and prognostic deterioration in ischemic stroke patients. In a multicentre study of 3,216 stroke patients in 22 Chinese hospitals, Miao M et al. found that increased TyG index correlated with higher rates of unfavorable functional outcome at discharge and in-hospital mortality, suggesting that TyG index significantly influences pathophysiological progression during the acute phase of ischemic stroke (24). The TyG index has also been shown to be associated with major adverse cardiac and cerebrovascular events, even in patients who have undergone endovascular intervention (25, 26).

Previous research has linked IR to platelet activation (27, 28). Basili S demonstrated the role of IR as a key determinant of platelet activation in obese women (29). Guo Y et al. showed that patients with acute ischemic stroke receiving dual antiplatelet therapy had increased platelet reactivity and a higher prevalence of aspirin-induced platelet reactivity when TyG index was elevated (30), which partially explains why patients receiving intensive antiplatelet therapy but with IR have a higher risk of stroke recurrence. These findings suggest that personalized therapeutic strategies to control the TyG index may offer viable approaches for secondary stroke prevention.

An elevated TyG index has been shown to facilitate both the initiation and progression of atherosclerosis, with established associations between TyG and carotid atherosclerosis (31) and plaque instability (32). Similar correlations have been demonstrated between elevated TyG levels and the severity of coronary artery stenosis (33, 34). As ICAS is an independent risk factor for stroke recurrence and has a particularly high prevalence in Asian populations (35, 36), the simultaneous presence of ICAS and an elevated TyG index may have a synergistic effect on the prediction of stroke recurrence.

This synergistic effect likely stems from multiple mechanisms. In insulin-resistant states, the anti-inflammatory and antiatherogenic benefits normally mediated by endothelial nitric oxide generation through the phosphatidylinositol-3-kinase pathway become attenuated (37–39). Simultaneously, the mitogen-activated protein kinase pathway in endothelial and vascular smooth muscle cells, along with its various proatherogenic effects, may be enhanced (40–43). This pathophysiological imbalance potentially mediates stenosis progression during the acute phase. Additionally, lesional macrophage apoptosis combined with impaired efferocytosis may result in increased plaque necrosis, consequently elevating thrombosis risk in pre-existing narrow arteries (44).

Furthermore, Hoscheidt SM et al. showed that increased IR levels correlated with decreased cerebral perfusion and arterial blood flow (45), and a Korean study showed that in acute ischemic stroke patients with middle cerebral artery or internal carotid artery occlusion, increased TyG index independently predicted poor outcome, even after reperfusion therapy (46). We propose that a higher TyG index in patients with large vessel occlusion may indicate reduced perfusion levels, contributing to suboptimal neurological recovery.

Our study also shows that people with a higher ICAS burden have the highest risk of stroke recurrence. Elevated ICAS levels indicate extensive vasculopathy and multiple vascular risk factors, which contribute to a significantly higher risk of recurrent infarction in the acute phase. Therefore, patients with higher ICAS burden combined with elevated TyG warrant more intensive follow-up and aggressive secondary prevention due to their increased risk of recurrence.

This investigation revealed significant direct effects of ICAS on stroke recurrence but did not support substantial mediation through TyG. The direct pathway suggests alternative mechanisms through which ICAS influences recurrent stroke, independent of metabolic factors captured by TyG. The negative proportion mediated further indicates that TyG plays a minimal role in this causal pathway, warranting investigation of other potential mediators.

Study limitations

This study has several important limitations. First, digital subtraction angiography (DSA) is not routinely used as an intracranial vascular screening tool at our center. Although MRA/CTA shows high agreement with DSA, the most accurate vascular assessment is not yet available. However, non-invasive and readily available screening tools may offer broader clinical applicability than DSA for routine patient assessment.

Second, defining this heterogeneous disease solely based on lumen size potentially overlooks other high-risk markers, including non-stenotic but high-risk carotid plaque (47), vessel calcification (48) inflammatory status (49), and cerebral small vessel disease burden scores (50). Future implementation of higher spatial resolution techniques to detect plaque composition, vessel wall changes and the dynamic evolution of the collateral circulation would improve the differentiation of atherosclerotic progression. Given potential synergistic effects, the integration of multiple biomarkers could optimize the identification of truly high-risk patients. Third, stratification by intracranial vascular burden necessarily limited our subgroup sample sizes, potentially reducing statistical power. Larger analyses are needed to validate the generalisability of our findings. Fourth, we did not serially monitor changes in the TyG index over time, which may have limited our ability to detect longitudinal associations with stroke recurrence. Finally, we acknowledge that our findings may not generalize to reperfusion-treated or highly disabled populations. Future studies could stratify analyses by treatment status or disability severity.

Conclusion

The combination of an elevated TyG index and ICAS burden effectively stratifies the short-term risk of stroke recurrence, with these markers showing superior predictive value when used together rather than individually. This combined approach may help to identify patients at highest risk of recurrence, potentially guiding more targeted interventions and improving patient outcomes. However, further research is needed to fully elucidate the underlying mechanisms of this association and to validate these findings in different populations.

Data availability statement

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

The studies involving humans were approved by the Ethics Committee of Shanghai Tenth People’s Hospital (Approval number: SHSY-IEC-4.1/21-308/01). The studies were conducted in accordance with the local legislation and institutional requirements. 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.

Author contributions

BL: Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Writing – original draft, Writing – review & editing. GM: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. XL: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This research was supported by grants from the National Natural Science Foundation of China (No. 82101537 and 82071206), Shanghai Science and Technology Commission (No. 22Y31900204), Shanghai Health Commission (No. 20234Y001), Science and Technology Commission of Chongming District (No. CKY2023-40), and Shanghai Municipal Key Clinical Specialty (shslczdzk06102).

Acknowledgments

The authors extend their gratitude to the research team for their valuable contributions to data collection and manuscript revision. We are indebted to all study participants for their involvement.

Conflict of interest

The 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.

Generative AI statement

The authors declare that no Gen AI was used in the creation of this manuscript.

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.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fneur.2025.1561329/full#supplementary-material

References

1. Hill, MA, Yang, Y, Zhang, L, Sun, Z, Jia, G, Parrish, AR, et al. Insulin resistance, cardiovascular stiffening and cardiovascular disease. Metabolism. (2021) 119:154766. doi: 10.1016/j.metabol.2021.154766

PubMed Abstract | Crossref Full Text | Google Scholar

2. Kernan, WN, Inzucchi, SE, Viscoli, CM, Brass, LM, Bravata, DM, and Horwitz, RI. Insulin resistance and risk for stroke. Neurology. (2002) 59:809–15. doi: 10.1212/WNL.59.6.809

PubMed Abstract | Crossref Full Text | Google Scholar

3. Jelenik, T, Flögel, U, Álvarez-Hernández, E, Scheiber, D, Zweck, E, Ding, Z, et al. Insulin resistance and vulnerability to cardiac ischemia. Diabetes. (2018) 67:2695–702. doi: 10.2337/db18-0449

PubMed Abstract | Crossref Full Text | Google Scholar

4. Kernan, WN, Inzucchi, SE, Viscoli, CM, Brass, LM, Bravata, DM, Shulman, GI, et al. Impaired insulin sensitivity among nondiabetic patients with a recent TIA or ischemic stroke. Neurology. (2003) 60:1447–51. doi: 10.1212/01.wnl.0000063318.66140.a3

PubMed Abstract | Crossref Full Text | Google Scholar

5. Wu, A, Li, Y, Liu, R, Qi, D, Yu, G, Yan, X, et al. Predictive value of insulin resistance as determined by homeostasis model assessment in acute ischemic stroke patients: a systematic review and meta-analysis. Horm Metab Res. (2021) 53:746–51. doi: 10.1055/a-1648-7767

PubMed Abstract | Crossref Full Text | Google Scholar

6. Rundek, T, Gardener, H, Xu, Q, Goldberg, RB, Wright, CB, Boden-Albala, B, et al. Insulin resistance and risk of ischemic stroke among nondiabetic individuals from the northern Manhattan study. Arch Neurol. (2010) 67:1195–200. doi: 10.1001/archneurol.2010.235

PubMed Abstract | Crossref Full Text | Google Scholar

7. Monnier, L, Hanefeld, M, Schnell, O, Colette, C, and Owens, D. Insulin and atherosclerosis: how are they related? Diabetes Metab. (2013) 39:111–7. doi: 10.1016/j.diabet.2013.02.001

PubMed Abstract | Crossref Full Text | Google Scholar

8. Bornfeldt, KE, and Tabas, I. Insulin resistance, hyperglycemia, and atherosclerosis. Cell Metab. (2011) 14:575–85. doi: 10.1016/j.cmet.2011.07.015

PubMed Abstract | Crossref Full Text | Google Scholar

9. Rask-Madsen, C, and Kahn, CR. Tissue-specific insulin signaling, metabolic syndrome, and cardiovascular disease. Arterioscler Thromb Vasc Biol. (2012) 32:2052–9. doi: 10.1161/ATVBAHA.111.241919

PubMed Abstract | Crossref Full Text | Google Scholar

10. Iglesies-Grau, J, Garcia-Alvarez, A, Oliva, B, Mendieta, G, García-Lunar, I, Fuster, JJ, et al. Early insulin resistance in normoglycemic low-risk individuals is associated with subclinical atherosclerosis. Cardiovasc Diabetol. (2023) 22:350. doi: 10.1186/s12933-023-02090-1

PubMed Abstract | Crossref Full Text | Google Scholar

11. Muniyappa, R, Lee, S, Chen, H, and Quon, MJ. Current approaches for assessing insulin sensitivity and resistance in vivo: advantages, limitations, and appropriate usage. Am J Physiol Endocrinol Metab. (2008) 294:E15–26. doi: 10.1152/ajpendo.00645.2007

PubMed Abstract | Crossref Full Text | Google Scholar

12. Tahapary, DL, Pratisthita, LB, Fitri, NA, Marcella, C, Wafa, S, Kurniawan, F, et al. Challenges in the diagnosis of insulin resistance: focusing on the role of HOMA-IR and Tryglyceride/glucose index. Diabetes Metab Syndr. (2022) 16:102581. doi: 10.1016/j.dsx.2022.102581

PubMed Abstract | Crossref Full Text | Google Scholar

13. Tao, LC, Xu, JN, Wang, TT, Hua, F, and Li, JJ. Triglyceride-glucose index as a marker in cardiovascular diseases: landscape and limitations. Cardiovasc Diabetol. (2022) 21:68. doi: 10.1186/s12933-022-01511-x

PubMed Abstract | Crossref Full Text | Google Scholar

14. Lopez-Jaramillo, P, Gomez-Arbelaez, D, Martinez-Bello, D, Abat, MEM, Alhabib, KF, Avezum, Á, et al. Association of the triglyceride glucose index as a measure of insulin resistance with mortality and cardiovascular disease in populations from five continents (PURE study): a prospective cohort study. Lancet Healthy Longev. (2023) 4:e23–33. doi: 10.1016/S2666-7568(22)00247-1

PubMed Abstract | Crossref Full Text | Google Scholar

15. Tian, L, Yue, X, Xi, G, Wang, Y, Li, Z, Zhou, Y, et al. Multiple intracranial arterial stenosis influences the long-term prognosis of symptomatic middle cerebral artery occlusion. BMC Neurol. (2015) 15:68. doi: 10.1186/s12883-015-0326-0

PubMed Abstract | Crossref Full Text | Google Scholar

16. Kim, BS, Chung, PW, Park, KY, Won, HH, Bang, OY, Chung, CS, et al. Burden of intracranial atherosclerosis is associated with long-term vascular outcome in patients with ischemic stroke. Stroke. (2017) 48:2819–26. doi: 10.1161/STROKEAHA.117.017806

PubMed Abstract | Crossref Full Text | Google Scholar

17. Wang, Y, Zhao, X, Liu, L, Soo, YO, Pu, Y, Pan, Y, et al. Prevalence and outcomes of symptomatic intracranial large artery stenoses and occlusions in China: the Chinese intracranial atherosclerosis (CICAS) study. Stroke. (2014) 45:663–9. doi: 10.1161/STROKEAHA.113.003508

PubMed Abstract | Crossref Full Text | Google Scholar

18. Lainelehto, K, Pienimäki, JP, Savilahti, S, Huhtala, H, Numminen, H, and Putaala, J. Cervicocerebral atherosclerosis burden increases long-term mortality in patients with ischemic stroke or transient ischemic attack. J Am Heart Assoc. (2024) 13:e032938. doi: 10.1161/JAHA.123.032938

PubMed Abstract | Crossref Full Text | Google Scholar

19. Sun, P, Liu, L, Pan, Y, Wang, X, Mi, D, Pu, Y, et al. Intracranial atherosclerosis burden and stroke recurrence for symptomatic intracranial artery stenosis (sICAS). Aging Dis. (2018) 9:1096–102. doi: 10.14336/AD.2018.0301

PubMed Abstract | Crossref Full Text | Google Scholar

20. Chimowitz, MI, Lynn, MJ, Howlett-Smith, H, Stern, BJ, and Hertzberg, VS. Comparison of warfarin and aspirin for symptomatic intracranial arterial stenosis. N Engl J Med. (2005) 352:1305–16.

Google Scholar

21. Simental-Mendía, LE, Rodríguez-Morán, M, and Guerrero-Romero, F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab Syndr Relat Disord. (2008) 6:299–304. doi: 10.1089/met.2008.0034

PubMed Abstract | Crossref Full Text | Google Scholar

22. Feng, X, Chan, KL, Lan, L, Abrigo, J, Liu, J, Fang, H, et al. Stroke mechanisms in symptomatic intracranial atherosclerotic disease: classification and clinical implications. Stroke. (2019) 50:2692–9. doi: 10.1161/STROKEAHA.119.025732

Crossref Full Text | Google Scholar

23. Vasques, AC, Novaes, FS, de Oliveira Mda, S, 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

Crossref Full Text | Google Scholar

24. Miao, M, Bi, Y, Hao, L, Bao, A, Sun, Y, Du, H, et al. Triglyceride-glucose index and short-term functional outcome and in-hospital mortality in patients with ischemic stroke. Nutr Metab Cardiovasc Dis. (2023) 33:399–407. doi: 10.1016/j.numecd.2022.11.004

PubMed Abstract | Crossref Full Text | Google Scholar

25. Yang, Y, Ma, M, Zhang, J, Jin, S, Zhang, D, and Lin, X. Triglyceride-glucose index in the prediction of clinical outcomes after successful recanalization for coronary chronic total occlusions. Cardiovasc Diabetol. (2023) 22:304. doi: 10.1186/s12933-023-02037-6

PubMed Abstract | Crossref Full Text | Google Scholar

26. Chen, Q, Xiong, S, Zhang, Z, Yu, X, Chen, Y, Ye, T, et al. Triglyceride-glucose index is associated with recurrent revascularization in patients with type 2 diabetes mellitus after percutaneous coronary intervention. Cardiovasc Diabetol. (2023) 22:284. doi: 10.1186/s12933-023-02011-2

PubMed Abstract | Crossref Full Text | Google Scholar

27. Randriamboavonjy, V, and Fleming, I. Insulin, insulin resistance, and platelet signaling in diabetes. Diabetes Care. (2009) 32:528–30. doi: 10.2337/dc08-1942

PubMed Abstract | Crossref Full Text | Google Scholar

28. Grant, PJ. Diabetes mellitus as a prothrombotic condition. J Intern Med. (2007) 262:157–72. doi: 10.1111/j.1365-2796.2007.01824.x

PubMed Abstract | Crossref Full Text | Google Scholar

29. Basili, S, Pacini, G, Guagnano, MT, Manigrasso, MR, Santilli, F, Pettinella, C, et al. Insulin resistance as a determinant of platelet activation in obese women. J Am Coll Cardiol. (2006) 48:2531–8. doi: 10.1016/j.jacc.2006.08.040

PubMed Abstract | Crossref Full Text | Google Scholar

30. Guo, Y, Zhao, J, Zhang, Y, Wu, L, Yu, Z, He, D, et al. Triglyceride glucose index influences platelet reactivity in acute ischemic stroke patients. BMC Neurol. (2021) 21:409. doi: 10.1186/s12883-021-02443-x

PubMed Abstract | Crossref Full Text | Google Scholar

31. Miao, M, Zhou, G, Bao, A, Sun, Y, Du, H, Song, L, et al. Triglyceride-glucose index and common carotid artery intima-media thickness in patients with ischemic stroke. Cardiovasc Diabetol. (2022) 21:43. doi: 10.1186/s12933-022-01472-1

PubMed Abstract | Crossref Full Text | Google Scholar

32. Wang, A, Tian, X, Zuo, Y, Zhang, X, Wu, S, and Zhao, X. Association between the triglyceride-glucose index and carotid plaque stability in nondiabetic adults. Nutr Metab Cardiovasc Dis. (2021) 31:2921–8. doi: 10.1016/j.numecd.2021.06.019

PubMed Abstract | Crossref Full Text | Google Scholar

33. Liang, S, Wang, C, Zhang, J, Liu, Z, Bai, Y, Chen, Z, et al. Triglyceride-glucose index and coronary artery disease: a systematic review and meta-analysis of risk, severity, and prognosis. Cardiovasc Diabetol. (2023) 22:170. doi: 10.1186/s12933-023-01906-4

PubMed Abstract | Crossref Full Text | Google Scholar

34. Wang, J, Huang, X, Fu, C, Sheng, Q, and Liu, P. Association between triglyceride glucose index, coronary artery calcification and multivessel coronary disease in Chinese patients with acute coronary syndrome. Cardiovasc Diabetol. (2022) 21:187. doi: 10.1186/s12933-022-01615-4

PubMed Abstract | Crossref Full Text | Google Scholar

35. Liu, L, Wong, KS, Leng, X, Pu, Y, Wang, Y, Jing, J, et al. Dual antiplatelet therapy in stroke and ICAS: subgroup analysis of CHANCE. Neurology. (2015) 85:1154–62. doi: 10.1212/WNL.0000000000001972

PubMed Abstract | Crossref Full Text | Google Scholar

36. Gutierrez, J, Turan, TN, Hoh, BL, and Chimowitz, MI. Intracranial atherosclerotic stenosis: risk factors, diagnosis, and treatment. Lancet Neurol. (2022) 21:355–68. doi: 10.1016/S1474-4422(21)00376-8

PubMed Abstract | Crossref Full Text | Google Scholar

37. Zeng, G, Nystrom, FH, Ravichandran, LV, Cong, LN, Kirby, M, Mostowski, H, et al. Roles for insulin receptor, PI3-kinase, and Akt in insulin-signaling pathways related to production of nitric oxide in human vascular endothelial cells. Circulation. (2000) 101:1539–45. doi: 10.1161/01.cir.101.13.1539

PubMed Abstract | Crossref Full Text | Google Scholar

38. Kuboki, K, Jiang, ZY, Takahara, N, Ha, SW, Igarashi, M, Yamauchi, T, et al. Regulation of endothelial constitutive nitric oxide synthase gene expression in endothelial cells and in vivo: a specific vascular action of insulin. Circulation. (2000) 101:676–81. doi: 10.1161/01.cir.101.6.676

PubMed Abstract | Crossref Full Text | Google Scholar

39. Fernández-Hernando, C, Ackah, E, Yu, J, Suárez, Y, Murata, T, Iwakiri, Y, et al. Loss of Akt1 leads to severe atherosclerosis and occlusive coronary artery disease. Cell Metab. (2007) 6:446–57. doi: 10.1016/j.cmet.2007.10.007

PubMed Abstract | Crossref Full Text | Google Scholar

40. Liu, W, Liu, Y, and Lowe, WL Jr. The role of phosphatidylinositol 3-kinase and the mitogen-activated protein kinases in insulin-like growth factor-I-mediated effects in vascular endothelial cells. Endocrinology. (2001) 142:1710–9. doi: 10.1210/endo.142.5.8136

PubMed Abstract | Crossref Full Text | Google Scholar

41. Montagnani, M, Golovchenko, I, Kim, I, Koh, GY, Goalstone, ML, Mundhekar, AN, et al. Inhibition of phosphatidylinositol 3-kinase enhances mitogenic actions of insulin in endothelial cells. J Biol Chem. (2002) 277:1794–9. doi: 10.1074/jbc.M103728200

PubMed Abstract | Crossref Full Text | Google Scholar

42. Cusi, K, Maezono, K, Osman, A, Pendergrass, M, Patti, ME, Pratipanawatr, T, et al. Insulin resistance differentially affects the PI 3-kinase- and MAP kinase-mediated signaling in human muscle. J Clin Invest. (2000) 105:311–20. doi: 10.1172/JCI7535

PubMed Abstract | Crossref Full Text | Google Scholar

43. Jiang, ZY, Zhou, QL, Chatterjee, A, Feener, EP, Myers, MG Jr, White, MF, et al. Endothelin-1 modulates insulin signaling through phosphatidylinositol 3-kinase pathway in vascular smooth muscle cells. Diabetes. (1999) 48:1120–30. doi: 10.2337/diabetes.48.5.1120

PubMed Abstract | Crossref Full Text | Google Scholar

44. Tabas, I, Tall, A, and Accili, D. The impact of macrophage insulin resistance on advanced atherosclerotic plaque progression. Circ Res. (2010) 106:58–67. doi: 10.1161/CIRCRESAHA.109.208488

PubMed Abstract | Crossref Full Text | Google Scholar

45. Hoscheidt, SM, Kellawan, JM, Berman, SE, Rivera-Rivera, LA, Krause, RA, Oh, JM, et al. Insulin resistance is associated with lower arterial blood flow and reduced cortical perfusion in cognitively asymptomatic middle-aged adults. J Cereb Blood Flow Metab. (2017) 37:2249–61. doi: 10.1177/0271678X16663214

PubMed Abstract | Crossref Full Text | Google Scholar

46. Lee, M, Kim, CH, Kim, Y, Jang, MU, Mo, HJ, Lee, SH, et al. High triglyceride glucose index is associated with poor outcomes in ischemic stroke patients after reperfusion therapy. Cerebrovasc Dis. (2021) 50:691–9. doi: 10.1159/000516950

PubMed Abstract | Crossref Full Text | Google Scholar

47. Kamel, H, Navi, BB, Merkler, AE, Baradaran, H, Díaz, I, Parikh, NS, et al. Reclassification of ischemic stroke etiological subtypes on the basis of high-risk nonstenosing carotid plaque. Stroke. (2020) 51:504–10. doi: 10.1161/STROKEAHA.119.027970

PubMed Abstract | Crossref Full Text | Google Scholar

48. Gepner, AD, Young, R, Delaney, JA, Budoff, MJ, Polak, JF, Blaha, MJ, et al. Comparison of carotid plaque score and coronary artery calcium score for predicting cardiovascular disease events: the multi-ethnic study of atherosclerosis. J Am Heart Assoc. (2017) 6:e005179. doi: 10.1161/JAHA.116.005179

PubMed Abstract | Crossref Full Text | Google Scholar

49. McCabe, JJ, Camps-Renom, P, Giannotti, N, McNulty, JP, Coveney, S, Murphy, S, et al. Carotid plaque inflammation imaged by PET and prediction of recurrent stroke at 5 years. Neurology. (2021) 97:e2282–91. doi: 10.1212/WNL.0000000000012909

PubMed Abstract | Crossref Full Text | Google Scholar

50. Chen, H, Pan, Y, Zong, L, Jing, J, Meng, X, Xu, Y, et al. Cerebral small vessel disease or intracranial large vessel atherosclerosis may carry different risk for future strokes. Stroke Vasc Neurol. (2020) 5:128–37. doi: 10.1136/svn-2019-000305

PubMed Abstract | Crossref Full Text | Google Scholar

Glossary

BP - Blood pressure

BMI - Body mass index

CI - Confidence interval

CTA - Computed tomography angiography

MRA - Magnetic resonance angiography

DSA - Digital subtraction angiography

ECAS - Extracranial arterial stenosis

FPG - Fasting plasma glucose

HDL - High-density lipoprotein

ICAS - Intracranial arterial stenosis

IS - Ischemic stroke

IDI - Integrated discrimination improvement

LDL - Low-density lipoprotein

NIHSS - National Institutes of Health Stroke Scale

NRI - Net reclassification index

HR - Hazard ratio

TIA - Transient ischemic attack

TyG - Triglyceride-glucose index

TG - Triglyceride

WBC - White blood cell

WASID - Warfarin and Aspirin for Symptomatic Intracranial Arterial Stenosis