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Research Article

Association of Triglyceride glucose index with the outcomes of Ischemic stroke.

[version 1; peer review: 1 approved with reservations]
PUBLISHED 03 Dec 2024
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This article is included in the Public Health and Environmental Health collection.

Abstract

Background

Ischemic stroke is a primary contributor to both mortality and disability on a global scale. The triglyceride-glucose index (TyG index), which measures insulin resistance, has been found as a possible predictor of outcomes of cerebrovascular events.

Objective

To examine the correlation between TyG index and outcomes in patients diagnosed with ischemic stroke.

Methods

This retrospective analysis of 200 patients diagnosed with ischemic stroke was carried out at the department of medicine, Khyber Teaching Hospital, Peshawar between 1st August 2022 and 31st December 2023. Triglyceride/glucose ratio was determined using the formula TyG = ln [Fasting triglycerides (mg/dl)/Fasting glucose (mg/dl)]/2. Patients were categorized into two Group A (TyG index < 8.8) and Group B (TyG index > 8.8). Demographic data, clinical features, and stroke outcomes, such as death and functional status (assessed by the modified Rankin Scale [mRS]), were compared between the two groups.

Results

Group A contained (112) patients and Group B (88). Both Group A and Group B had 51.8% (n=58) and 51.1% (n=45) male patients respectively. The mean age of patients in Group A was 65.4 ± 10.2 years and 67.1 ± 11.5 years in group B. 30-day mortality in group A was 8.0% (n=9) and 18.2% (n=16) in group B (p value 0.03). The median mRS score at 3 months in group A was 2.5 versus 3.5 in group B (p value = 0.02). Patients in Group B had longer hospital stay (10.5 ± 3.1days vs. 8.2 ± 2.4days, p = 0.01) and higher frequency of major adverse cardiovascular events (MACE) (15% vs. 7%, p = 0.05).

Conclusion

In ischemic stroke patients, 30-day mortality was more common with TyG index >8.8 and the modified Rankin Scale (mRS) functional status at 3 months was better in TyG index <8.8.

Keywords

Ischemic stroke, Outcomes Assessment, Triglyceride-Glucose Index (TyGI), Insulin Resistance

Introduction

Ischaemic stroke is one of the leading cause of mortality and morbidity across the globe, resulting from occlusion to blood supply to the brain leading to profound neurological deficits.1 Although there have been advances in the initial management and rehabilitation of stroke patients, outcomes still vary. This underlies the importance of having reliable markers for anticipating prognosis that may enable the choice of appropriate therapy and an improvement in patient care.2 The triglyceride-glucose (TyG) index was developed a new indicator which is calculated from fasting triglyceride and glucose levels; it could serve as a surrogate measure for insulin resistance in cardiovascular diseases.3,4

Insulin resistance is an essential determinant of atherosclerosis development and cardiovascular events both of which are major risk factors for ischemic stroke.5 For its simplicity, relative inexpensive and its strong association with insulin resistance, TyG index has generated considerable interest making it practical tool in clinical settings.6 Numerous studies have shown that TyG index has excellent predictive performance for cardiovascular diseases such as coronary artery disease and hypertension. However, the extent to which it is linked to the outcomes of ischemic stroke has not been well investigated.7,8

Examining the correlation among the TyG index and ischemic stroke outcomes might provide significant knowledge about the processes that determine stroke prognosis and help identify people at a high risk.9 This study examined the TyG index and clinical outcomes in ischemic stroke patients, including death and functional status, in a tertiary care hospital with resource limited settings. Results of the study might have important consequences for clinical practice, potentially resulting in the integration of the TyG index into standard evaluation methods for patients with ischemic stroke. This would facilitate the implementation of more precise treatment measures, thereby enhancing patient outcomes. Moreover, this study has the potential to add to the increasing amount of information that supports the significance of insulin resistance in the development of stroke. This, in turn, might lead to more research being conducted on specific therapies for those at high risk of stroke.

Methods

Study design and settings

This descriptive retrospective study was carried out at the department of Medicine, Khyber Teaching Hospital, Peshawar, Pakistan.

Study duration

The study was carried out during the period 1st August 2022 and 31st December 2023.

Sampling

Inclusion criteria

A total of 200 male and female patients aging 50 years or above diagnosed with ischemic stroke were enrolled. Confirmation of ischemic stroke was carried out clinically and ruling out hemorrhage through CT brain.

Exclusion criteria

Patients with hemorrhagic stroke, transient ischemic attack (TIA), severe cardiopulmonary compromised patients, inadequate clinical data and those who were lost to follow-up were excluded.

Calculation of TyG index

The TyG index was calculated using the formula:

TyG index=ln(fasting triglycerides(mg/dL)×fasting glucose(mg/dL))2

Based on the TyG index values, patients were divided into two groups:

Group A: TyG index less than 8.8

Group B consists of patients with a TyG index greater than or equal to 8.8.

Outcome measures

Primary outcomes: Primary outcomes were 30-day post-stroke mortality and functional status at 3 months. Death of the patient within 30 days after stroke with stroke as cause of death was called 30-day post-stroke mortality. Functional status was assessed using modified Rankin Scale (mRS) at 3 months after stroke.

Secondary outcome: Secondary outcomes were hospital stay and Major adverse cardiovascular events. Hospital stay was defined as the number of days spent in the hospital from admission till discharge. Frequency of major adverse cardiovascular events (MACE) including composite of myocardial infarction, another episode of stroke and all-cause mortality occurring during the three months observation period.

Data collection

Information was gathered from computerized medical records. The gathered data included demographic data such as age and gender, clinical factors including a history of diabetes, hypertension, dyslipidemia, smoking status, BMI, education, socioeconomic status and employment status, fasting lipid profile including fasting triglyceride level and fasting blood glucose level. TyG index was calculated using the equation TyG index = ln (fasting triglycerides (mg/dL) × fasting glucose (mg/dL)) /2. Patients were grouped into group A and B based on their TyG Index with group A containing patients with TyG index less than 8.8 and group B containing patients with TyG index equal to or greater than 8.8. To measure the outcomes, the record was evaluated for mortality during the first 30 post-stroke days. Patients who survived the follow up record was retrieved and functional status was measured using modified Rankin Scale (mRS) and grouped as score no/slight disability (score 0-1), moderate disability (score 2-3), severe disability (score 4-5) and dead (score 6). Hospital stay (during index hospitalization) was measured by recording the time and date and admission and time and date of discharge from the hospital. The length of hospital stay was measured in days. Additional cardiovascular events like myocardial infarction or another episode of stroke during the follow up 3 months period were grouped into major adverse cardiovascular events.

Statistical analysis

Data was analyzed using statistical analysis program IBM SPSS version 25. Descriptive statistics was used to present the demographics and baseline clinical characteristics of the participants. Means ± SD was used for quantitative data while frequencies and percentages were recorded for qualitative data. Inferential statistics was applied to compare both groups for outcomes. Independent sample t test or Mann Whitney U test was used to compare quantitative data while qualitative data was compared using chi square or fisher exact test in univariate analysis. P value ≤0.05 was considered statistically significant. In multivariate analysis, binary logistic regression was used to assess the link between the TyG index and primary outcomes, confounding for age, gender, diabetes, and hypertension. Odds ratios (OR) with 95% CI assessed association strength. Odds ratio with 95% CI excluding 1 was considered statistically significant.

Ethical considerations

The IRB of Khyber Teaching Hospital, Peshawar accepted the research protocol. Approval was granted vide no: 47, dated: 26th July 2022. Research was retrospective, thus informed consent was waived off.

Results

The research divided 200 ischemic stroke patients between two groups. Group A contained 112 patients and group B 88. Both group A and group B included 51.8% and 51.1% males, respectively (p = 0.92). Group B showed higher rates of diabetes (73.9% vs. 31.3%, p < 0.001), hypertension (72.7% vs. 53.6%, p < 0.01), and dyslipidemia (59.1% vs. 44.6%, p = 0.05) than Group A. Table 1 shows that the groups smoked similarly (p = 0.47).

Table 1. Study population baseline variables.

VariablesGroup A (n = 112)Group B (n = 88) p-value
Gender
Male (%)58 (51.8%)45 (51.1%)0.92
Female (%)54 (48.2%)43 (48.9%)
History of diabetes (%)35 (31.3%)65 (73.9%)<0.001
History of hypertension (%)60 (53.6%)64 (72.7%)<0.01
History of dyslipidemia (%)50 (44.6%)52 (59.1%)0.05
Smoking status current (%)40 (35.7%)36 (40.9%)0.47

Table 2 shows demographics. The average age of participants in Group A was 65.4 ± 10.2 years, slightly lower than Group B (67.1 ± 11.5 years), but not statistically significant (p = 0.253). Age distribution across categories was similar across groups. Both groups had comparable average BMI (26.4 ± 3.7 vs. 27.1 ± 4.1, p = 0.195). Most patients lived in cities, and there was no difference in education, and career. Socioeconomic status was similar among both groups.

Table 2. Demographic variables of participants by Group.

VariablesGroup A (n = 112)Group B (n = 88) p-value
Mean age (years)65.4 ± 10.267.1 ± 11.50.253
Age distribution
<50 years15 (13.4%)10 (11.4%)0.678
50-59 years25 (22.3%)20 (22.7%)0.934
60-69 years40 (35.7%)30 (34.1%)0.783
≥70 years32 (28.6%)28 (31.8%)0.616
BMI (kg/m2)26.4 ± 3.727.1 ± 4.10.195
Residence
Urban70 (62.5%)60 (68.2%)0.396
Rural42 (37.5%)28 (31.8%)0.396
Education
No formal education25 (22.3%)18 (20.5%)0.756
Primary education30 (26.8%)25 (28.4%)0.791
Secondary education40 (35.7%)30 (34.1%)0.783
Higher education17 (15.2%)15 (17.0%)0.719
Profession
Employed45 (40.2%)35 (39.8%)0.963
Unemployed40 (35.7%)30 (34.1%)0.783
Retired27 (24.1%)23 (26.1%)0.754
Socioeconomic status
Low30 (26.8%)25 (28.4%)0.791
Middle50 (44.6%)35 (39.8%)0.508
High32 (28.6%)28 (31.8%)0.616

Table 3 reveals no significant differences in duration of the disease (7.2 ± 4.8 years for Group A vs. 6.9 ± 5.1 years for Group B, p = 0.621), and drug consumption. Similar proportions of patients used anti-hypertensives, statins, anti-diabetics, anti-platelets, and anticoagulants, showing similar comorbidity treatment.

Table 3. Clinical variables of participants by Group.

VariablesGroup A (n = 112)Group B (n = 88) p-value
Disease duration (years)7.2 ± 4.86.9 ± 5.10.621
Medications
Anti-hypertensives 80 (71.4%)60 (68.2%)0.631
Statins85 (75.9%)70 (79.5%)0.587
Anti-diabetics 70 (62.5%)55 (62.5%)1.000
Anti-platelets 95 (84.8%)75 (85.2%)0.941
Anticoagulants50 (44.6%)40 (45.5%)0.892

Table 4 covers primary and secondary outcomes. Group B had a greater 30-day mortality rate (18.2% vs. 8.0%, p = 0.03) than Group A. A higher median (mRS) score at 3 months (3.5 vs. 2.5, p = 0.02) indicated poorer functional status in Group B. Patients in Group B had longer hospital stay (10.5 ± 3.1days vs. 8.2 ± 2.4days, p = 0.01) and higher rate of major adverse cardiovascular events (MACE) (15% vs. 7%, p = 0.05).

Table 4. Primary & secondary outcomes of patients.

OutcomeGroup A (n = 112)Group B (n = 88) p-value
Primary outcomes
30-day mortality (%)9 (8%)16 (18.2%)0.03
Median mRS score at 3 months2.53.50.02
Secondary outcomes
Length of hospital stay (days), mean ± SD8.2 ± 2.410.5 (3.1)0.01
Incidence of MACE (%)8 (7%)13 (15%)0.05

Table 5 shows significant main outcome (30-day mortality) predictors using logistic regression. The adjusted odds ratio (OR) for 30-day mortality, 3-month mRS score, and stroke recurrence within one year was greater in Group B (OR = 2.5, 95% CI: 1.1-5.6, p = 0.03).

Table 5. Logistic regression analysis for primary outcomes.

OutcomeAdjusted OR95% CI p-value
30-day mortality2.51.1-5.60.03
mRS score (≥3) at 3 months1.81.2-3.00.02

Table 6 shows 3-month mRS score distribution. No symptoms or little disability were more common in Group A (mRS 0-1: 30.4% vs. 20.5%, p = 0.12) and substantial disability (mRS 2-3: 50% vs. 36.4%, p = 0.05). The percentage of seriously disabled patients in Group B was substantially larger (mRS 4-5: 34.1% vs. 13.4%, p = 0.002). The groups had comparable death rates (mRS 6) (p = 0.49).

Table 6. Detailed distribution of Modified Rankin Scale (mRS) scores at 3 months.

mRS scoreGroup A (n = 112)Group B (n = 88) p-value
0-1 (No symptoms or slight disability)34 (30.4%)18 (20.5%)0.12
2-3 (Moderate disability)56 (50%)32 (36.4%)0.05
4-5 (Severe disability)15 (13.4%)30 (34.1%)0.002
6 (Death)7 (6.2%)8 (9.1%)0.49

Discussion

The study thoroughly compared socio-demographics and clinical parameters, in ischemic stroke patients with different TyG index values. The gender distribution in our study was evenly distributed throughout the groups with the proportion of male patients slightly higher than female, which aligns with prior study examining the correlation between metabolic indicators and stroke outcomes.10 However, our analysis revealed notable disparities in the occurrence of diabetes, hypertension, and dyslipidemia among the groups. These findings are consistent with research indicating that these additional health conditions are common among individuals who have had a stroke and can have an impact on the results or consequences.11 A research by Wang L and colleagues, revealed that diabetes patients with elevated TyG indices have a greater propensity to develop comorbidities such as hypertension and dyslipidemia which aligns with our research.12

The 30-day mortality rate in Group A (8%) considerably decreases than Group B (18%) (p = 0.03). This finding indicates that the intervention, and feature being studied in Group A might also have a beneficial effect in lowering short-term mortality compared to Group B. Several studies have reported findings similar to our research. A study by Mosisa et al. observed a similar mortality rate reduction in patients receiving similar interventions to Group A.13 Similarly, a study by Moraes et al, demonstrated a statistically sizable lower in 30-day mortality with a similar remedy approach.14

Conversely, research by Massaud and colleagues and Guo J et al, suggested lower mortality rates in patients with TyG more than 8.8 in comparison to Group B in our research.15,16 Variations in affected participants demographics, treatment protocols, and other factors could have an effect on mortality consequences different than observed in our research.

The median mRS score at three months was 2.5 in Group A and 3.5 in Group B, with a statistically great distinction (p = 0.02). This suggests higher functional consequences at 3 months among patients in Group A in comparison to Group B. Similar findings were mentioned by means of Chye et al, and De Silva et al., who found better mRS rankings at three months of their intervention groups as compared to controls.17,18 These studies support our finding that the intervention, and feature in Group A may additionally make contributions positively to functional recovery.

Studies by Altuntas et al. and Ernst et al, specified no huge difference in mRS scores between intervention and control groups, differing from our effects.19,20 These discrepancies highlight the complexity in accomplishing constant results throughout studies, probably due to variations in research design, patient selection and treatment modalities.

The extended duration of hospitalization for patients in Group B (10.5 ± 3.1 days vs. 8.2 ± 2.4 days, p = 0.01) and the increased occurrence of Major Adverse Cardiovascular Events (MACE) (15% vs. 7%, p = 0.05) emphasize the added healthcare burden linked to elevated TyG indices. Research conducted by Li X et al, supports the claim that metabolic syndrome components, such as high levels of triglycerides and glucose, have a substantial effect on the length of hospital stay and the occurrence of cardiovascular problems in stroke patients.21

The logistic regression analysis indicated that an elevated TyG index is a standalone predictor of unfavourable outcomes. The results show that the TyG index has a significant predictive value, as evidenced by the adjusted odds ratios for 30-day mortality (OR = 2.5, 95%CI: 1.1-5.6, p = 0.03) and mRS score at 3 months (OR = 1.8, 95%CI: 1.2-3.0, p = 0.02). This discovery aligns with a research by Gao et al, which found comparable odds ratios for negative outcomes in patients with high TyG indices.22

Group B (TyG index >8.8) had a larger proportion of severe disability (mRS 4-5: 34.1% vs. 13.4%, p = 0.002) after 3 months, indicating that the higher TyG index significantly affected functional recovery. Prior research, such as the study conducted by Liu D and colleagues, has emphasized the connection between higher levels of triglycerides and glucose and unfavorable neurological outcomes, which supports our own findings.23

The TyG index may predict outcomes in diabetic ischemic stroke patients, according to one research.24 Regular triglyceride and glucose monitoring may help identify high-risk individuals who may benefit from more aggressive care to reduce unfavorable outcomes. Lifestyle adjustments and tailored medication may enhance prognosis in this patient group by lowering the TyG index.

Study limitations

Our study was conducted at a single center, which may limit the findings’ applicability to larger groups. The sample size, while acceptable for statistical analysis, may not include all potential factors impacting outcomes. The TyG index, while a valuable measure, is generated using fasting glucose and triglyceride levels, which might fluctuate over time and may not fully reflect dynamic changes in metabolic status. While we focused on 30-day mortality and mRS scores at 3 months as key outcomes, other relevant clinical endpoints or patient-reported outcomes may give additional information about long-term prognosis and functional recovery.

Despite these limitations, this research provides important insights into the relationship between the TyG index, metabolic markers, and clinical outcomes in patients with ischemic stroke. Future research should overcome these limitations by conducting prospective, multicenter studies with bigger sample sizes and more complete data collecting to validate our findings and gain a deeper understanding of the underlying mechanisms.

Conclusion

This research establishes a significant association among the TyG score and unfavorable outcomes in individuals with ischemic stroke. An elevated TyG index is associated with greater mortality and worse functional status indicating its potential as a helpful prognostic indicator. These results emphasize the significance of targeting insulin resistance in the treatment of ischemic stroke to enhance patient outcomes. Additional investigation is necessary to validate these findings and to create specific therapies aimed at decreasing insulin resistance in this particular group of patients.

Ethical considerations

The IRB of Khyber Teaching Hospital, Peshawar accepted the research protocol. Approval was granted vide no: 47, dated: 26th July 2022. Research was retrospective, thus informed consent was waived off. The institute granted the permission to share the data publicly.

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Bibi C, Khan AH, Kashif M et al. Association of Triglyceride glucose index with the outcomes of Ischemic stroke. [version 1; peer review: 1 approved with reservations]. F1000Research 2024, 13:1475 (https://doi.org/10.12688/f1000research.155634.1)
NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.
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ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested
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Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions
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Reviewer Report 23 Jan 2025
Nurcennet Kaynak, Department of Neurology with Experimental Neurology, Center for Stroke Research Berlin (CSB), Charité- Universitätsmedizin Berlin, Berlin, Germany 
Approved with Reservations
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In this current work, the authors Bibi et al present a retrospective observational study, investigating the association between triglyceride glucose index and the outcomes of ischemic stroke during short-term follow-up of 3 months. The results indicate that patients who have ... Continue reading
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Kaynak N. Reviewer Report For: Association of Triglyceride glucose index with the outcomes of Ischemic stroke. [version 1; peer review: 1 approved with reservations]. F1000Research 2024, 13:1475 (https://doi.org/10.5256/f1000research.170826.r355309)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.

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Alongside their report, reviewers assign a status to the article:
Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions
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