Triglyceride-Glucose Index and the Incidence of Stroke: A Meta-Analysis of Cohort Studies

Triglyceride-Glucose Index and the Incidence of Stroke: A Meta-Analysis of Cohort Studies | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Triglyceride-Glucose Index and the Incidence of Stroke: A Meta-Analysis of Cohort Studies Canlin Liao, Haixiong Xu, Tao Jin, Ke Xu, Zhennan Xu, Lingzhen Zhu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1972856/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Insulin resistance has been confirmed to be involved in atherosclerosis pathogenesis. As a new indicator, the triglyceride-glucose (TyG) index has greater operability in the evaluation of insulin resistance. Previous studies have shown inconsistent results in evaluating the association between TyG index and stroke incidence in people without stroke at baseline. Therefore, this study was to systematically assess the association by conducting a meta-analysis. Methods Cohort studies on TyG index and stroke were obtained by searching the PubMed, the Cochrane Library (CENTRAL) and EMBASE databases. The multivariate-adjusted correlation of end points was studied, including TyG index and stroke (including ischemic stroke and hemorrhagic stroke) or ischemic stroke. Review Manager 5.3 and Stata 16 were adopted for meta-analysis. Results Eight cohort studies with 5,719,098 participants were included in this meta-analysis. The results showed that participants with the highest TyG index category at baseline, compared to those with the lowest TyG index category, were independently associated with a higher risk of stroke [Hazard ratio (HR): 1.32, 95% confidence interval (CI): 1.22–1.43, I 2 = 32%, P < 0.00001]. Subgroups analysis remained that study designs, ethnicity and characteristics of participants had no subgroup effects (for subgroup analysis, all P༞0.05), except outcome report(stroke or ischemic stroke) which suggested that it may had a stronger effect on the association(χ 2 = 4.78, P = 0.03). Conclusions A higher TyG index may be independently associated with a higher risk of stroke in people without stroke at baseline. Keywords: Triglyceride-glucose index, Insulin resistance, stroke, Meta-analysis Triglyceride-glucose index Insulin resistance stroke Meta-analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Background Stroke is one of the most devastating diseases in the world today. Globally, it is the second leading cause of the increase in years of life lost (YLL). 1 In addition, the increasingly youthful trend of stroke deserves our great attention. 2 Ischemic stroke is the result of blood circulation disorders in the cerebral blood vessels, caused by occlusion of the large cerebral arteries which happens more commonly in the middle cerebral artery 3 or cerebral small vessel disease. 4 Previous studies have demonstrated that insulin resistance plays an important role in the pathogenesis of ischemic stroke. 5 The gold standard for the assessment of insulin resistance is the hyperinsulinemic-euglycemic clamp test (HIEC). Due to the complexity of the test process, the extensive time required and the high cost, its clinical application is very limited. 6 The homeostasis model assessment of insulin resistance (HOMA-IR) index is also not so convenient and economical in clinical application, although it is the most accessible indicator for evaluating insulin resistance in clinical practice. 7 As a novel surrogate indicator of insulin resistance, the TyG index, derived from the fasting triglyceride and glucose levels, is convenient and fast to obtain as well as economical and reliable. 8 The TyG index can be calculated as: ln [TG (mg/dL) × FBG (mg/dL)/2]. 9 , 10 Studies have confirmed that the TyG index has a significant correlation with both HIEC and HOMA-IR. 11 Therefore, the TyG index can be used as an easily accessible and operational indicator for evaluating insulin resistance. Observational studies have revealed the relationship between high TyG index and stroke in the population. However, most of them were cross-sectional studies. 12 , 13 Recently, as more and more cohort studies on stroke and TyG index have been published, we have found inconsistent results. 14 – 17 Therefore, our study aims to summarize the association between baseline TyG index and stroke incidence in people without stroke at baseline. 2. Research Methods This Meta-analysis was based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 18 ( http://www.prisma-statement.org/ ) and Cochrane's Handbook. 19 , 20 Electronic databases including PubMed, the Cochrane Library (CENTRAL) and EMBASE were searched for relevant studies and literature. 2.1 Study Selection The studies adhering to all the following criteria were included: (1) Participants were adults with no stroke at baseline; (2) Cohort studies were published as full-length articles in English; (3) TyG index was measured at baseline; (4) The outcome included the occurrence of a stroke or ischemic stroke; (5) Risk factors adjusted for potential confounders were reported. On the contrary, studies were excluded from the meta-analysis for at least one of the following criteria: (1) Participants were less than 18 years old; (2) The studies were not Cohort study; (3) There was no reporting of stroke; (4) There was no measuring of TyG index; (5) Reported data was based on univariate analysis rather than multivariate analysis. Two researchers (C.L. Liao and K. Xu) used PICOS principles to search related literature and independently evaluated the literature. Any disputes were resolved after the discussion with a third researcher (L.Z. Zhu). 2.2 Data extraction Two researchers (C.L. Liao and K. Xu) independently extracted data from the articles. The extracted content included: name of the authors, publication year, study design, country, participant characteristics, average age, proportion of males, proportion of diabetic patients, TyG index analysis, follow-up duration, result validation, etc . After data extraction, the two researchers exchanged data for verification. 2.3 Literature search PubMed, the Cochrane Library (CENTRAL) and EMBASE databases were searched with the combination of following terms: (1) “triglyceride and glucose index” OR “triglyceride-glucose index *” OR “TyG index” OR “triglyceride glucose index” OR “triacylglycerol glucose index”; (2) “stroke” OR “Cerebrovascular Accident” OR “Cerebrovascular Accidents” OR “CVA” OR “CVAs” OR “Apoplexy” OR “Brain Vascular Accident” OR “Brain Vascular Accidents”. The final literature search was performed on 16 December 2021. 2.4 Literature screening The search results obtained from PubMed, the Cochrane Library (CENTRAL) and EMBASE databases were exported to Endnote X9, whose function of “duplicate finder” help search and remove repetitive literature. The screening of literature was divided into 2 stages. First, conduct a preliminary screening based on the titles and abstracts of the literature to obtain possibly eligible literature, eligibility-unknown literature and clearly eligible literature. For the literature that might be eligible and those whose eligibility was unknown, their full-length texts would be obtained and further selected according to the inclusion and exclusion criteria, thus getting eligible studies. The selection of the title, abstract and full-length text were carried out by two researchers (Z.N. Xu and L.Z. Zhu) strictly and independently based on the inclusion and exclusion criteria. When the screening results were inconsistent, the two researchers would discuss and negotiate with each other to reach a consensus. If the negotiation failed, we would consult with the third researcher (T. Jin) and adopted his opinion. 2.5 Quality Evaluation The Newcastle-Ottawa Scale 20 was used to evaluate the quality of each study according to the selection of the study groups, comparability of the groups and ascertainment of the outcome of interest. The scale ranges from 1 to 9 and research with test results of 7 or more are classified as high quality. The assessment was performed independently by 2 researchers (L.Z. Zhu and Z.N. Xu). If there was any disagreement between the researchers, it was resolved by consensus. If the negotiation failed, we would consult with the third researcher (T. Jin) and adopted his opinion. 2.6. Data analyses Hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) were used as a general measure for the association between TyG index and stroke in people who had no stroke at the baseline examination. For the study that analyzed the TyG index as a categorical variable, the HRs of the incidence of stroke in participants with the highest TyG index level compared to those with the lowest TyG index level were extracted. Cochran's Q test and estimation of I 2 were used to assess the heterogeneity of the included cohort studies. 21 If I 2 ༜50%, it was considered that there was no significant heterogeneity. In addition, a random-effect model was used to synthesize HR data, as this model was considered as a more general method that could incorporate potential heterogeneity into the study. 19 Furthermore, sensitivity analyses, excluding one individual study at a time, were carried out to test the stability of the results. 22 Pre-defined subgroup analyses were also performed to evaluate the impact of study characteristics including outcome reports, study design, characteristics of participants and ethnicity on the association between TyG index and the risk of the incidence of stroke. The potential publication bias was assessed by visual inspection of the symmetry of the funnel plots. RevMan (Version 5.3; Cochrane Collaboration, Oxford, UK) was adopted to perform the statistical analysis. 3. Research Results 3.1 Process and results of Literature screening The search strategy retrieved a total of 128 articles through PubMed, the Cochrane Library (CENTRAL) and EMBASE databases (Fig. 1). 113 articles were obtained after excluding 15 duplications. 8 studies comprising 5,719,098 participants were included in the meta-analysis after further evaluation of the abstract and full-length text twice according to inclusion criteria. Of them, 4 were prospective cohort studies and the others were retrospective cohort studies. 3.2 study characteristics and quality evaluation 3.2.1 study characteristics The characteristics of eight cohort studies 14–17, 23–26 included name of the author, publication year, study design, country, participant characteristics, number, average age, proportion of men, proportion of diabetic patients, TyG index analysis, follow-up duration, result verification, result report and variables adjusted (Table 1 ). Overall, 8 cohort studies with 5,719,098 participants were included. The studies were carried out in China, 12 , 14 – 16 , 24, 25 South Korea 23 and Spain. 17 As for the study design, 4 of them were prospective cohort studies 16 , 17 , 24, 25 and the remaining 4 were retrospective cohort studies. 14 , 15 , 23, 25 6 studies reported the occurrence of stroke (including ischemic stroke and hemorrhagic stroke), 4 , 15 – 17 , 23–25 of which 3 studies reported the occurrence of ischemic stroke. 15 , 16 , 26 These studies were published from 2016 to 2021, where patients at baseline were followed for time ranging from post-intervention to 11.2 years. In most studies, the male to female ratios were not balanced, but were balanced overall. Research subjects of four studies were participants without stroke in the community 14 , 16 , 23, 26 while those of the other studies were outpatients or inpatients in hospitals. 15 , 17 , 24, 25 All studies have adjusted variables. The baseline TyG index was analyzed in eight cohort studies as a categorical variable. Median, quartile or quintile were used to divide research subjects into a higher TyG index group and a lower TyG index group. After variables adjusted, the relative risk and 95% CI of stroke or ischemic stroke were calculated in the higher TyG index group during the follow-up period with the lowest TyG index group as a reference. 3.2.2 Quality Evaluation Eight studies included in this meta-analysis were cohort studies. The Newcastle-Ottawa Scale 20 was used to evaluate their quality and the results showed that three studies scored 7 points and the other five studies scored 9 points. Above all, all included cohort studies have scored more than 7 points, which meant they were of high quality (Table 2 ). Table 1 Characteristics of the included cohort studies Study Year Design Country Characteristics of participants Number of participants Mean age (Years) Male (%) Proportion of DM TyG Index Analysis Follow-up duration (years) Outcome validation Outcomes reported Variables adjusted Sanchez-Inigo 17 2016 PC Spain First-time attendee outpatients to an internal medicine department without ASCVDs 5,014 54.4 61.2 5.2 Q5:Q1 8.8 ICD-10 stroke (157) Age, sex, BMI, smoking, alcohol intake, lifestyle pattern, HTN, T2DM, antiplatelet, therapy, HDL-C, and LDL-C Li 14 2019 RC China Participants aged over 60 years without stroke who participated in a routine health check-up program 6078 70.5 53.1 11.8 Q4: Q1 5.5 ICD-10 stroke (234) Age, sex, living, alone, current, smoker, alcohol, consumption, exercise, BMI, SBP, HDL-C, LDLC, and T2DM Mao 24 2019 PC China patients diagnosed with NSTE-ACS without stroke 791 62.5 67.4 32.6 M2:M1 1 Clinical evaluation Stroke (5) Age, sex, metabolic syndrome, LDL-C, HDL-C, SYNTAX score, CRP, basal insulin, sulfonylurea, metformin, α-glucosidase inhibitor, ACEI/ARB, beta-blocker, and PCI/CABG. Hong 23 2020 RC Korea Community population without stroke 5,593,134 53.0 50.5 3.7 Q4:Q1 8.2 ICD-10 Stroke (89,120) Age, sex, smoking, alcohol, consumption, regular physical activity, low socioeconomic, status, BMI, HTN, and TC Wang 25 2020 RC China consecutive patients with diabetes who underwent coronary angiography for ACS 3,428 66.3 55.9 100 T3:T1 3 Clinical evaluation non-fatal stroke (46) Age, male, smoker, previous MI, previous CABG, BMI, AMI, LVEF, left main disease, multi-vessel disease, HbA1c, hs-CRP, statin, insulin Zhao 15 2020 RC China patients with NSTE-ACS, who received elective PCI without diabetes 1576 59.7 73.7 0 M2:M1 2 Clinical evaluation non-fatal ischemic stroke (27) Age, gender, smoking history, hypertension, dyslipidemia, previous history of MI, PCI, stroke and PAD, eGFR, LVEF, LM disease, three-vessel disease, SYNTAX score, number of stents, statins at discharge and ACEI/ARB at discharge ACEI/ARB Wang 16 2021 PC China Community population without stroke 97,653 51.67 79.62 29.3 Q4:Q1 11.02 Clinical evaluation Stroke (5122) ischemic stroke (4277) Age, gender, level of education, income, smoking, alcohol abuse, physical activity, BMI, SBP, DBP, history of MI, dyslipidemia, HDL-C, LDL-C, Hs-CRP, antidiabetic drugs, lipid-lowering drugs, HTN, DM, antihypertensive drugs Zhao 26 2021 PC China People aged ≥ 40 years, who were free of stroke and cardiovascular disease 11,777 54.0 40.9 0 Q4:Q1 6 Clinical evaluation Ischemic stroke (677) Hemorrhagic stroke (1024) Age, gender; marital status, income, education level, smoking, alcohol drinking, physical activity, family history of stroke, SBP, DBP, resting heart rate, BMI, WC, TC, HDL-C and LDL-C. TyG, triglyceride–glucose index ; PC, prospective cohort ;RC, retrospective cohort; Q5:Q1, the 5th quintile vs. the 1st quintile; Q4:Q1, the 4th quartile vs. the 1st quartile; T3:T1, the 3th tertile vs. the 1st tertile; M2:M1, the 2th median vs. the 1st median; T2DM, type 2 diabetes mellitus; ICD-10, International Classification of Diseases, tenth edition; PAD, peripheral artery disease; HTN, hypertension; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; BMI, body mass index; WC, wrist circumference; eGFR, estimated glomerular filtrating rate; SBP, systolic blood pressure; DBP, diastolic blood pressure ; TC, total cholesterol; hs-CRP: high-sensitivity C-reactive protein; CABG, Coronary Artery Bypass Grafting; PCI, Percutaneous Transluminal Coronary Intervention; AMI, actue myocardial infarction; MI, myocardial infarction; LVEF, left ventricular ejection fraction; Table 2 Details of quality evaluation via the Newcastle–Ottawa Scale Study Representativeness of the exposed cohort Selection of the non-exposed cohort Ascertainment of exposure Outcome not present at baseline Control for age Control for other confounding factors Assessment of outcome Sufficient follow-up duration Adequacy of follow-up of cohorts Total Sanchez-Inigo 2016 17 1 1 1 1 1 1 1 1 1 9 Li 2019 14 1 1 1 1 1 1 1 1 1 9 Hong 2020 23 1 1 1 1 1 1 1 1 1 9 Mao 2019 24 1 1 1 1 0 1 1 0 1 7 Wang 2020 25 1 1 1 1 0 1 1 0 1 7 Wang 2021 16 1 1 1 1 1 1 1 1 1 9 Zhao 2020 15 1 1 1 1 0 1 1 0 1 7 Zhao 2021 26 1 1 1 1 1 1 1 1 1 9 3.3 Results of Meta-analysis of cohort studies With a random-effect model, the pooled results of 8 cohort studies 14–17, 23–26 showed that compared to participants with the lowest TyG index category at baseline, the participants with the highest TyG index category had a significantly increased risk of stroke during follow-up (HR: 1.32, 95% CI 1.22–1.43, P༜0.00001; Fig. 2). This finding was consistent with the outcomes reported of subgroup analysis: subgroup of stroke(including ischemic stroke and hemorrhagic stroke) (HR:1.26, 95%CI 1.24–1.29, I 2 = 0%, P༜0.00001; Fig. 3a-3e) and subgroup of ischemic stroke (HR: 1.55, 95%CI 1.29–1.85, I 2 = 20%, P༜0.00001; Fig. 3a-3e), which showed that compared to participants with the lowest TyG index category at baseline, the participants with the highest TyG index category may had a higher risk to suffer from ischemic stroke (χ 2 = 4.78, P = 0.03). Besides, subgroup analyses also showed consistent association between prospective studies (HR: 1.52, 95%CI 1.18–1.97, I 2 = 43%, P = 0.001; Fig. 3a-3e) and retrospective studies (HR: 1.26, 95%CI 1.23–1.29, I 2 = 0%, P < 0.00001; Fig. 3a-3e), between community population (HR: 1.30, 95%CI 1.20–1.42, I 2 = 53%, P < 0.00001; Fig. 3a-3e) and outpatients or inpatients population (HR: 1.76, 95%CI 1.19–2.60, I 2 = 0%, P = 0.005; Fig. 3a-3e), between Chinese (HR: 1.47, 95%CI 1.21–1.79, I 2 = 32%, P = 0.0001; Fig. 3a-3e) and non - Chinese (HR: 1.26, 95%CI 1.23–1.29, I 2 = 0%, P < 0.00001; Fig. 3a-3e). Subgroup analysis according to the weight of studies demonstrated that without two larger studies the smaller studies also had consistent association (HR: 1.58, 95%CI 1.28–1.97, I 2 = 0%, P༜0.0001;Figure 3a-3e). The sensitivity analysis by excluding one study at a time showed similar results. 3.4 Publication bias The funnel plots were drawn using stroke as an outcome indicator in order to observe the publication bias of eight cohort studies. The funnel plots were symmetric on visual inspection, suggesting a low risk of publication bias (Fig. 4). Since only 8 studies 14–17, 23–26 were included, less than 10 studies were required, Egger's regression test was unable to perform in this study. 27 4. Discussion Based on the fact that insulin resistance was involved in the pathogenesis of atherosclerosis, TyG index was selected as a new surrogate indicator of insulin resistance and a Meta-analysis was performed to assess the association between baseline TyG index and the incidence of stroke in the population without stroke at baseline. After being assessed through the Newcastle-Ottawa scale, all studies included in this study were of high quality. Only cohort studies were included and thus the potential recall bias associated with the cross-sectional design was avoided. Only cohort studies with multivariate adjustments were included, and therefore potential confounding bias was avoided to the greatest extent. In addition, sensitivity and subgroup analyses were carried out in all the included studies to ensure the robustness of the results. Moreover, no significant heterogeneity was observed among all the included cohort studies. The main research results were as follows: (1) A higher baseline TyG index might be an independent predictor of an increase in the incidence of stroke in people without stroke at baseline; (2) Subgroup analyses showed that the correlation between the baseline TyG index and the incidence of stroke was not significantly affected by study design, characteristics of participants (i.e. community population or patients) and ethnicity of participants (i.e. Chinese or non- Chinese) (3)The subgroup analyses based on the outcomes reported demonstrated that the difference between two groups was statistically significant and the HR of ischemic stroke was higher than that of stroke group. TyG index, as a result of triglycerides and fasting blood glucose, has been recognized as a simple and reliable surrogate indicator of insulin resistance. 28 In clinical applications, it is relatively economical to measure blood triglycerides and fasting blood glucose, and the TyG index can be obtained through simple calculations. A previous study proved that the TyG index has high sensitivity and specificity in detecting insulin resistance, 10 which is superior to the homeostasis model assessment of insulin resistance (HOMA-IR). 29 In addition, compared to HOMA-IR, the TyG index, not requiring measurement of insulin levels, can be conveniently and economically used for all patients and healthy people and is also suitable for large-scale screening of insulin resistance. It is currently believed that insulin resistance is a key point in the incidence of ischemic stroke, which plays an important role in the pathogenesis of ischemic stroke by promoting the late changes of atherosclerosis. In addition, insulin resistance not only enhances adhesion, activation and aggregation of platelets, but also causes hemodynamics disturbance, all of which are conducive to the occurrence of ischemic stroke. 5 Furthermore, insulin resistance can also lead to an imbalance of glucose metabolism, resulting in chronic hyperglycemia. This in turn triggers oxidative stress and inflammation, leading to cell damage. In addition, Insulin resistance can alter systemic lipid Metabolism, bringing about dyslipidemia and the development of the well-known lipid triad: (1) high levels of plasma triglycerides, (2) low levels of high-density lipoproteins, and (3) small and dense low-density lipoproteins. This triad, together with endothelial dysfunction (which can also be caused by abnormal insulin signaling), gives rise to the formation of atherosclerotic plaques. 30 Current studies support that the association between TyG index and hemorrhagic stroke was tenuous. Pathologically, hemorrhagic stroke is different from ischemic stroke. The most common causes of hemorrhagic stroke are hypertension (30–60%), cerebral amyloid angiopathy (10–30%), anticoagulation (1–20%) and vascular structural disease (3–8%), while the cause is unknown in about 5–20% of cases. 31 A previous study pointed out that there was no evidence of an association between insulin resistance and the incidence of hemorrhagic stroke. 16 Moreover, a multicenter cross-sectional study demonstrated that the TyG index was not related to the risk of hospitalization and ICU death in patients with severe hemorrhagic stroke. 32 Furthermore, the results of the subgroup analysis according to the outcome reported in our study suggest that there is a subgroup effect about the difference between the stroke subgroup (including ischemic stroke and hemorrhagic stroke) and the ischemic stroke subgroup. The HR of the ischemic stroke subgroup is higher than that of the stroke subgroup, probably resulting from the fact that hemorrhagic stroke events are included in the stroke subgroup, contributing to a decrease in the HR. This result is consistent with the above research, and indirectly reflects that the high TyG index may not have a significant correlation with the incidence of hemorrhagic stroke. When the results of meta-analysis are interpreted, some limitations should be observed. 1. Despite the systematic search, the number of studies eventually included was limited. Due to the limitation of the included research data, the TyG index was used as categorical data for the study and the TyG index used as a continuous variable was in shortage. Therefore, it is not clear whether the association between the TyG index and the incidence of stroke is linear. 2. In the subgroup analysis, only the reported results, the study design, the characteristics of participants and the ethnicity of the population were analyzed. More research is needed to determine whether other research characteristics will affect the results, such as gender, diabetes status, follow-up time, concurrent medications used, etc. 3. Among the studies we eventually included, there were six Chinese studies and only two non-Chinese studies, one of which was from Asia and the other from Europe. Data from other states such as the Americas, Australia and Africa are still scarce, so more detailed ethnic subgroup analysis should be conducted further. 4. Due to the limitations of research data, hemorrhagic stroke cannot be evaluated in a systematical way. 5. Although the cohort studies included were all adjusted for multivariate, the influence of unadjusted participating factors in the cohort studies could not be ruled out on the HR of the study and the association between TyG index and the incidence of stroke. Similarly, we do not know if the data before multivariate adjustment has an impact on the study. 6. Even though we conducted a subgroup analysis shown the significant affect after excluding two larger studies (Hong 2020 and Wang 2021), which have a combined weight of 86.3% that have a major influence on the meta-analysis. 5. Conclusion The current evidence from the included observational cohort studies suggests that a higher TyG index may be related to an increase in the incidence of stroke in people without stroke at baseline. Therefore, a higher TyG index may be an independent predictor of stroke in people without stroke at baseline. The above findings need to be verified by a large-scale prospective cohort study to further clarify the underlying pathophysiological mechanism between TyG index and stroke. Abbreviations TyG: Triglyceride–glucose; HRs: Hazard ratios; CIs: Confidence intervals HIEC: hyperinsulinemic-euglycemic clamp test HOMA-IR: homeostasis model assessment of insulin resistance Declarations Acknowledgements None. Authors’ contributions CL, KX and LZ conceived and designed research; CL, KX and LZ performed the literature search and data extraction; ZN, LZ and TJ performed the literature screening and quality evaluation; CL and KX analyzed data and wrote the initial paper; HX and MF revised the paper; MF had primary responsibility for final content. All authors reviewed and revised the manuscript, and approved the final manuscript for submission. Funding None. Availability of data and material All data generated or analysis during this study are included in this published article. 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Meta-analysis of observational studies in epidemiology: A proposal for reporting. Meta-analysis of observational studies in epidemiology (moose) group. JAMA . 2000;283:2008-2012 19. Higgins J GS. Cochrane handbook for systematic reviews of interventions version 5.1.0. The cochrane collaboration ；available online at: Www.Cochranehandbook.Org . 2011 20. Wells GA SB, O’Connell D, Peterson J, Welch V, Losos M, et al. The newcastle-ottawa scale (nos) for assessing the quality of nonrandomised studies in meta-analyses(2010).Available online at: Http:// www. Ohri. Ca/progr ams/clini cal_ epide miolo gy/oxford. Asp. . 21. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med . 2002;21:1539-1558 22. Patsopoulos NA, Evangelou E, Ioannidis JP. Sensitivity of between-study heterogeneity in meta-analysis: Proposed metrics and empirical evaluation. Int J Epidemiol . 2008;37:1148-1157 23. Hong S, Han K, Park C-Y. The triglyceride glucose index is a simple and low-cost marker associated with atherosclerotic cardiovascular disease: A population-based study. BMC medicine . 2020;18 24. Mao Q, Zhou D, Li Y, Wang Y, Xu SC, Zhao XH. The triglyceride-glucose index predicts coronary artery disease severity and cardiovascular outcomes in patients with non-st-segment elevation acute coronary syndrome. Disease markers . 2019;2019:6891537 25. Wang L, Cong HL, Zhang JX, Hu YC, Wei A, Zhang YY, et al. Triglyceride-glucose index predicts adverse cardiovascular events in patients with diabetes and acute coronary syndrome. Cardiovascular Diabetology . 2020;19 26. Zhao Y, Sun H, Zhang W, Xi Y, Shi X, Yang Y, et al. Elevated triglyceride–glucose index predicts risk of incident ischaemic stroke: The rural chinese cohort study. Diabetes and Metabolism . 2021;47 27. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ . 1997;315:629-634 28. Du T, Yuan G, Zhang M, Zhou X, Sun X, Yu X. Clinical usefulness of lipid ratios, visceral adiposity indicators, and the triglycerides and glucose index as risk markers of insulin resistance. Cardiovasc Diabetol . 2014;13:146 29. 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 30. Ormazabal V, Nair S, Elfeky O, Aguayo C, Salomon C, Zuniga FA. Association between insulin resistance and the development of cardiovascular disease. Cardiovasc Diabetol . 2018;17:122 31. Cordonnier C, Demchuk A, Ziai W, Anderson CS. Intracerebral haemorrhage: Current approaches to acute management. The Lancet . 2018;392:1257-1268 32. Zhang B, Liu L, Ruan H, Zhu Q, Yu D, Yang Y, et al. Triglyceride-glucose index linked to hospital mortality in critically ill stroke: An observational multicentre study on eicu database. Front Med (Lausanne) . 2020;7:591036 Additional Declarations No competing interests reported. Supplementary Files PRISMA2020Checklist.zip Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1972856","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":130942019,"identity":"73c440d2-59ee-4a32-8d57-582d6e388a69","order_by":0,"name":"Canlin Liao","email":"","orcid":"","institution":"Shantou Central Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Canlin","middleName":"","lastName":"Liao","suffix":""},{"id":130942020,"identity":"69b5695e-acab-4eed-a2f7-bff6c64a3d2f","order_by":1,"name":"Haixiong Xu","email":"","orcid":"","institution":"Shantou Central Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Haixiong","middleName":"","lastName":"Xu","suffix":""},{"id":130942021,"identity":"77131ac7-c0e1-4993-8bfe-381c17371811","order_by":2,"name":"Tao Jin","email":"","orcid":"","institution":"Shantou Central Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Jin","suffix":""},{"id":130942022,"identity":"a877b380-558e-4203-80dc-a89bbeb34d45","order_by":3,"name":"Ke Xu","email":"","orcid":"","institution":"Shantou Central Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ke","middleName":"","lastName":"Xu","suffix":""},{"id":130942023,"identity":"6e8200c9-4bbd-4484-a431-17d21a819f07","order_by":4,"name":"Zhennan Xu","email":"","orcid":"","institution":"Shantou Central Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhennan","middleName":"","lastName":"Xu","suffix":""},{"id":130942024,"identity":"4ee51031-2c54-416f-95ab-ead4e2943294","order_by":5,"name":"Lingzhen Zhu","email":"","orcid":"","institution":"Shantou Central Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lingzhen","middleName":"","lastName":"Zhu","suffix":""},{"id":130942025,"identity":"7c3cae80-b78e-45e2-b5b8-f32f1b72c886","order_by":6,"name":"Mingfa Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYFAC5gaGCgMJOXn2BoYDxOlgY2xgOFNgYWzYc4AkLR8qEhluJBDpLPn5ja0bDhhIJDDOfP7wcEENgzy/GAHLDI4xtt0Aasljl84xODzjGIPhzNkErDNgY2y7/cFAophxdg7DYR42hgSD2wS0yLdBbElsuHn8wWGef0RoYTgG03KDweAwbxsRWgyOJYK1AAMZ6BfePgnCfpFvPnzsxoE/dcCoPP74M883G3l+aUIOQwMSpCkfBaNgFIyCUYAdAADp2Eiv31DnswAAAABJRU5ErkJggg==","orcid":"","institution":"Shantou Central Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Mingfa","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2022-08-18 02:44:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1972856/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1972856/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":25937301,"identity":"9c068b7c-b296-4eec-81db-a20ba48cd1af","added_by":"auto","created_at":"2022-09-01 15:29:53","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":101344,"visible":true,"origin":"","legend":"\u003cp\u003eFigure legend not available with this version.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1972856/v1/856b970264944fef80b7294f.jpg"},{"id":25936676,"identity":"71d15976-ae57-406b-90ff-dd82deaa8af4","added_by":"auto","created_at":"2022-09-01 15:24:53","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":87537,"visible":true,"origin":"","legend":"\u003cp\u003eFigure legend not available with this version.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1972856/v1/2c6f0ef81ea58fe72d267d8f.jpg"},{"id":25937302,"identity":"b66f2854-e476-4d9d-b06d-51976e67f8c7","added_by":"auto","created_at":"2022-09-01 15:29:53","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3100957,"visible":true,"origin":"","legend":"\u003cp\u003eFigure legend not available with this version.\u003c/p\u003e","description":"","filename":"Figure3a3e.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1972856/v1/b05b628e76ad805e05554dd2.jpg"},{"id":25937478,"identity":"a4a1aa92-1ec4-4f5b-8a4c-a4a67145b302","added_by":"auto","created_at":"2022-09-01 15:34:53","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":21609,"visible":true,"origin":"","legend":"\u003cp\u003eFigure legend not available with this version.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1972856/v1/4f06c9a5d85f170c4b924e7c.jpg"},{"id":26152621,"identity":"8742f700-86e5-415b-8df0-31b7be09c9a9","added_by":"auto","created_at":"2022-09-07 05:03:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":771541,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1972856/v1/3dee0321-a315-4a12-b5e2-c84bda8bb207.pdf"},{"id":25936679,"identity":"1e60ff32-6d67-4bd8-b50d-bcd961577242","added_by":"auto","created_at":"2022-09-01 15:24:53","extension":"zip","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":29503,"visible":true,"origin":"","legend":"","description":"","filename":"PRISMA2020Checklist.zip","url":"https://assets-eu.researchsquare.com/files/rs-1972856/v1/ff76d83b946eeb194ef3923c.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Triglyceride-Glucose Index and the Incidence of Stroke: A Meta-Analysis of Cohort Studies","fulltext":[{"header":"1. Background","content":"\u003cp\u003eStroke is one of the most devastating diseases in the world today. Globally, it is the second leading cause of the increase in years of life lost (YLL).\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e In addition, the increasingly youthful trend of stroke deserves our great attention.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Ischemic stroke is the result of blood circulation disorders in the cerebral blood vessels, caused by occlusion of the large cerebral arteries which happens more commonly in the middle cerebral artery \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e or cerebral small vessel disease. \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Previous studies have demonstrated that insulin resistance plays an important role in the pathogenesis of ischemic stroke.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe gold standard for the assessment of insulin resistance is the hyperinsulinemic-euglycemic clamp test (HIEC). Due to the complexity of the test process, the extensive time required and the high cost, its clinical application is very limited.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e The homeostasis model assessment of insulin resistance (HOMA-IR) index is also not so convenient and economical in clinical application, although it is the most accessible indicator for evaluating insulin resistance in clinical practice.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAs a novel surrogate indicator of insulin resistance, the TyG index, derived from the fasting triglyceride and glucose levels, is convenient and fast to obtain as well as economical and reliable.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e The TyG index can be calculated as: ln [TG (mg/dL) \u0026times; FBG (mg/dL)/2]. \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Studies have confirmed that the TyG index has a significant correlation with both HIEC and HOMA-IR. \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Therefore, the TyG index can be used as an easily accessible and operational indicator for evaluating insulin resistance.\u003c/p\u003e \u003cp\u003eObservational studies have revealed the relationship between high TyG index and stroke in the population. However, most of them were cross-sectional studies.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Recently, as more and more cohort studies on stroke and TyG index have been published, we have found inconsistent results. \u003csup\u003e\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Therefore, our study aims to summarize the association between baseline TyG index and stroke incidence in people without stroke at baseline.\u003c/p\u003e"},{"header":"2. Research Methods","content":"\u003cp\u003eThis Meta-analysis was based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA)\u003csup\u003e18\u003c/sup\u003e(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.prisma-statement.org/\u003c/span\u003e\u003cspan address=\"http://www.prisma-statement.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Cochrane's Handbook.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, 20\u003c/sup\u003eElectronic databases including PubMed, the Cochrane Library (CENTRAL) and EMBASE were searched for relevant studies and literature.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Selection\u003c/h2\u003e \u003cp\u003eThe studies adhering to all the following criteria were included: (1) Participants were adults with no stroke at baseline; (2) Cohort studies were published as full-length articles in English; (3) TyG index was measured at baseline; (4) The outcome included the occurrence of a stroke or ischemic stroke; (5) Risk factors adjusted for potential confounders were reported. On the contrary, studies were excluded from the meta-analysis for at least one of the following criteria: (1) Participants were less than 18 years old; (2) The studies were not Cohort study; (3) There was no reporting of stroke; (4) There was no measuring of TyG index; (5) Reported data was based on univariate analysis rather than multivariate analysis.\u003c/p\u003e \u003cp\u003eTwo researchers (C.L. Liao and K. Xu) used PICOS principles to search related literature and independently evaluated the literature. Any disputes were resolved after the discussion with a third researcher (L.Z. Zhu).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data extraction\u003c/h2\u003e \u003cp\u003eTwo researchers (C.L. Liao and K. Xu) independently extracted data from the articles. The extracted content included: name of the authors, publication year, study design, country, participant characteristics, average age, proportion of males, proportion of diabetic patients, TyG index analysis, follow-up duration, result validation, \u003cem\u003eetc\u003c/em\u003e. After data extraction, the two researchers exchanged data for verification.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.3 Literature search\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePubMed, the Cochrane Library (CENTRAL) and EMBASE databases were searched with the combination of following terms: (1) \u0026ldquo;triglyceride and glucose index\u0026rdquo; OR \u0026ldquo;triglyceride-glucose index *\u0026rdquo; OR \u0026ldquo;TyG index\u0026rdquo; OR \u0026ldquo;triglyceride glucose index\u0026rdquo; OR \u0026ldquo;triacylglycerol glucose index\u0026rdquo;; (2) \u0026ldquo;stroke\u0026rdquo; OR \u0026ldquo;Cerebrovascular Accident\u0026rdquo; OR \u0026ldquo;Cerebrovascular Accidents\u0026rdquo; OR \u0026ldquo;CVA\u0026rdquo; OR \u0026ldquo;CVAs\u0026rdquo; OR \u0026ldquo;Apoplexy\u0026rdquo; OR \u0026ldquo;Brain Vascular Accident\u0026rdquo; OR \u0026ldquo;Brain Vascular Accidents\u0026rdquo;. The final literature search was performed on 16 December 2021.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.4 Literature screening\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe search results obtained from PubMed, the Cochrane Library (CENTRAL) and EMBASE databases were exported to Endnote X9, whose function of \u0026ldquo;duplicate finder\u0026rdquo; help search and remove repetitive literature. The screening of literature was divided into 2 stages. First, conduct a preliminary screening based on the titles and abstracts of the literature to obtain possibly eligible literature, eligibility-unknown literature and clearly eligible literature. For the literature that might be eligible and those whose eligibility was unknown, their full-length texts would be obtained and further selected according to the inclusion and exclusion criteria, thus getting eligible studies. The selection of the title, abstract and full-length text were carried out by two researchers (Z.N. Xu and L.Z. Zhu) strictly and independently based on the inclusion and exclusion criteria. When the screening results were inconsistent, the two researchers would discuss and negotiate with each other to reach a consensus. If the negotiation failed, we would consult with the third researcher (T. Jin) and adopted his opinion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Quality Evaluation\u003c/h2\u003e \u003cp\u003eThe Newcastle-Ottawa Scale\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e was used to evaluate the quality of each study according to the selection of the study groups, comparability of the groups and ascertainment of the outcome of interest. The scale ranges from 1 to 9 and research with test results of 7 or more are classified as high quality. The assessment was performed independently by 2 researchers (L.Z. Zhu and Z.N. Xu). If there was any disagreement between the researchers, it was resolved by consensus. If the negotiation failed, we would consult with the third researcher (T. Jin) and adopted his opinion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Data analyses\u003c/h2\u003e \u003cp\u003eHazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) were used as a general measure for the association between TyG index and stroke in people who had no stroke at the baseline examination. For the study that analyzed the TyG index as a categorical variable, the HRs of the incidence of stroke in participants with the highest TyG index level compared to those with the lowest TyG index level were extracted. Cochran's Q test and estimation of I\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e were used to assess the heterogeneity of the included cohort studies.\u003csup\u003e21\u003c/sup\u003eIf I\u003csup\u003e2\u003c/sup\u003e༜50%, it was considered that there was no significant heterogeneity. In addition, a random-effect model was used to synthesize HR data, as this model was considered as a more general method that could incorporate potential heterogeneity into the study.\u003csup\u003e19\u003c/sup\u003eFurthermore, sensitivity analyses, excluding one individual study at a time, were carried out to test the stability of the results. \u003csup\u003e22\u003c/sup\u003e Pre-defined subgroup analyses were also performed to evaluate the impact of study characteristics including outcome reports, study design, characteristics of participants and ethnicity on the association between TyG index and the risk of the incidence of stroke. The potential publication bias was assessed by visual inspection of the symmetry of the funnel plots. RevMan (Version 5.3; Cochrane Collaboration, Oxford, UK) was adopted to perform the statistical analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Research Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Process and results of Literature screening\u003c/h2\u003e \u003cp\u003eThe search strategy retrieved a total of 128 articles through PubMed, the Cochrane Library (CENTRAL) and EMBASE databases (Fig.\u0026nbsp;1). 113 articles were obtained after excluding 15 duplications. 8 studies comprising 5,719,098 participants were included in the meta-analysis after further evaluation of the abstract and full-length text twice according to inclusion criteria. Of them, 4 were prospective cohort studies and the others were retrospective cohort studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 study characteristics and quality evaluation\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 study characteristics\u003c/h2\u003e \u003cp\u003eThe characteristics of eight cohort studies\u003csup\u003e14\u0026ndash;17, 23\u0026ndash;26\u003c/sup\u003e included name of the author, publication year, study design, country, participant characteristics, number, average age, proportion of men, proportion of diabetic patients, TyG index analysis, follow-up duration, result verification, result report and variables adjusted (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Overall, 8 cohort studies with 5,719,098 participants were included. The studies were carried out in China,\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, 24, 25\u003c/sup\u003e South Korea\u003csup\u003e23\u003c/sup\u003e and Spain.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e As for the study design, 4 of them were prospective cohort studies\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, 24, 25\u003c/sup\u003e and the remaining 4 were retrospective cohort studies. \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, 23, 25\u003c/sup\u003e 6 studies reported the occurrence of stroke (including ischemic stroke and hemorrhagic stroke), \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, 23\u0026ndash;25\u003c/sup\u003e of which 3 studies reported the occurrence of ischemic stroke. \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, 26\u003c/sup\u003e These studies were published from 2016 to 2021, where patients at baseline were followed for time ranging from post-intervention to 11.2 years. In most studies, the male to female ratios were not balanced, but were balanced overall. Research subjects of four studies were participants without stroke in the community\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, 23, 26\u003c/sup\u003e while those of the other studies were outpatients or inpatients in hospitals.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, 24, 25\u003c/sup\u003e All studies have adjusted variables. The baseline TyG index was analyzed in eight cohort studies as a categorical variable. Median, quartile or quintile were used to divide research subjects into a higher TyG index group and a lower TyG index group. After variables adjusted, the relative risk and 95% CI of stroke or ischemic stroke were calculated in the higher TyG index group during the follow-up period with the lowest TyG index group as a reference.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Quality Evaluation\u003c/h2\u003e \u003cp\u003eEight studies included in this meta-analysis were cohort studies. The Newcastle-Ottawa Scale\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e was used to evaluate their quality and the results showed that three studies scored 7 points and the other five studies scored 9 points. Above all, all included cohort studies have scored more than 7 points, which meant they were of high quality (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the included cohort studies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDesign\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCharacteristics of participants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNumber of participants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean age (Years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMale (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eProportion of DM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTyG Index\u003c/p\u003e \u003cp\u003eAnalysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eFollow-up duration (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eOutcome validation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003cp\u003ereported\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eVariables adjusted\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSanchez-Inigo\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFirst-time attendee outpatients to an internal medicine department without ASCVDs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e54.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e61.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eQ5:Q1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eICD-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003estroke (157)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eAge, sex, BMI, smoking, alcohol intake, lifestyle pattern, HTN, T2DM, antiplatelet, therapy, HDL-C, and LDL-C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLi\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eParticipants aged over 60 years without stroke who participated in a routine health check-up program\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e70.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e53.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eQ4: Q1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eICD-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003estroke (234)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eAge, sex, living, alone, current, smoker, alcohol, consumption, exercise, BMI, SBP, HDL-C, LDLC, and T2DM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMao\u003csup\u003e24\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003epatients diagnosed with NSTE-ACS without stroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e67.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eM2:M1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eClinical evaluation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eAge, sex, metabolic syndrome, LDL-C, HDL-C, SYNTAX score, CRP, basal insulin, sulfonylurea, metformin, α-glucosidase inhibitor, ACEI/ARB, beta-blocker, and PCI/CABG.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHong\u003csup\u003e23\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKorea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCommunity population without stroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,593,134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e53.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eQ4:Q1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eICD-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eStroke (89,120)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eAge, sex, smoking, alcohol, consumption, regular physical activity, low socioeconomic, status, BMI, HTN, and TC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWang\u003csup\u003e25\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003econsecutive patients with diabetes who underwent coronary angiography for ACS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e66.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e55.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eT3:T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eClinical evaluation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003enon-fatal stroke\u003c/p\u003e \u003cp\u003e(46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eAge, male, smoker, previous MI, previous CABG, BMI, AMI, LVEF, left main disease, multi-vessel disease, HbA1c, hs-CRP, statin, insulin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhao\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003epatients with NSTE-ACS, who received elective PCI without diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e59.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e73.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eM2:M1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eClinical evaluation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003enon-fatal ischemic stroke\u003c/p\u003e \u003cp\u003e(27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eAge, gender, smoking history, hypertension, dyslipidemia, previous history of MI, PCI, stroke and PAD, eGFR, LVEF, LM disease, three-vessel disease, SYNTAX score, number of stents, statins at discharge and ACEI/ARB at discharge ACEI/ARB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWang\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCommunity population without stroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97,653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e51.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e79.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e29.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eQ4:Q1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e11.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eClinical evaluation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eStroke (5122)\u003c/p\u003e \u003cp\u003eischemic stroke\u003c/p\u003e \u003cp\u003e(4277)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eAge, gender, level of education, income, smoking, alcohol abuse, physical activity, BMI, SBP, DBP, history of MI, dyslipidemia, HDL-C, LDL-C, Hs-CRP, antidiabetic drugs, lipid-lowering drugs, HTN, DM, antihypertensive drugs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhao\u003csup\u003e26\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePeople aged\u0026thinsp;\u0026ge;\u0026thinsp;40 years, who were free of stroke and cardiovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11,777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e54.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e40.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eQ4:Q1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eClinical evaluation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eIschemic stroke (677)\u003c/p\u003e \u003cp\u003eHemorrhagic stroke (1024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eAge, gender; marital status, income, education level, smoking, alcohol drinking, physical activity, family history of stroke, SBP, DBP, resting heart rate, BMI, WC, TC, HDL-C and LDL-C.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"14\" nameend=\"c14\" namest=\"c1\"\u003e \u003cp\u003eTyG, triglyceride\u0026ndash;glucose index ; PC, prospective cohort ;RC, retrospective cohort; Q5:Q1, the 5th quintile vs. the 1st quintile; Q4:Q1, the 4th quartile vs. the 1st quartile; T3:T1, the 3th tertile vs. the 1st tertile; M2:M1, the 2th median vs. the 1st median; T2DM, type 2 diabetes mellitus; ICD-10, International Classification of Diseases, tenth edition; PAD, peripheral artery disease; HTN, hypertension; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; BMI, body mass index; WC, wrist circumference; eGFR, estimated glomerular filtrating rate; SBP, systolic blood pressure; DBP, diastolic blood pressure ; TC, total cholesterol; hs-CRP: high-sensitivity C-reactive protein; CABG, Coronary Artery Bypass Grafting; PCI, Percutaneous Transluminal Coronary Intervention; AMI, actue myocardial infarction; MI, myocardial infarction; LVEF, left ventricular ejection fraction;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetails of quality evaluation via the Newcastle\u0026ndash;Ottawa Scale\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRepresentativeness of\u003c/p\u003e \u003cp\u003ethe exposed cohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelection of the\u003c/p\u003e \u003cp\u003enon-exposed cohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAscertainment\u003c/p\u003e \u003cp\u003eof exposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOutcome not\u003c/p\u003e \u003cp\u003epresent at\u003c/p\u003e \u003cp\u003ebaseline\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003cp\u003efor age\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eControl for other\u003c/p\u003e \u003cp\u003econfounding factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAssessment\u003c/p\u003e \u003cp\u003eof outcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSufficient\u003c/p\u003e \u003cp\u003efollow-up\u003c/p\u003e \u003cp\u003eduration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAdequacy of\u003c/p\u003e \u003cp\u003efollow-up of\u003c/p\u003e \u003cp\u003ecohorts\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSanchez-Inigo 2016\u003csup\u003e17\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLi 2019\u003csup\u003e14\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHong 2020\u003csup\u003e23\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMao 2019\u003csup\u003e24\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWang 2020\u003csup\u003e25\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWang 2021\u003csup\u003e16\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhao 2020\u003csup\u003e15\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhao 2021\u003csup\u003e26\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Results of Meta-analysis of cohort studies\u003c/h2\u003e \u003cp\u003eWith a random-effect model, the pooled results of 8 cohort studies\u003csup\u003e14\u0026ndash;17, 23\u0026ndash;26\u003c/sup\u003e showed that compared to participants with the lowest TyG index category at baseline, the participants with the highest TyG index category had a significantly increased risk of stroke during follow-up (HR: 1.32, 95% CI 1.22\u0026ndash;1.43, P༜0.00001; Fig.\u0026nbsp;2). This finding was consistent with the outcomes reported of subgroup analysis: subgroup of stroke(including ischemic stroke and hemorrhagic stroke) (HR:1.26, 95%CI 1.24\u0026ndash;1.29, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0%, P༜0.00001; Fig.\u0026nbsp;3a-3e) and subgroup of ischemic stroke (HR: 1.55, 95%CI 1.29\u0026ndash;1.85, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;20%, P༜0.00001; Fig.\u0026nbsp;3a-3e), which showed that compared to participants with the lowest TyG index category at baseline, the participants with the highest TyG index category may had a higher risk to suffer from ischemic stroke (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;4.78, P\u0026thinsp;=\u0026thinsp;0.03). Besides, subgroup analyses also showed consistent association between prospective studies (HR: 1.52, 95%CI 1.18\u0026ndash;1.97, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;43%, P\u0026thinsp;=\u0026thinsp;0.001; Fig.\u0026nbsp;3a-3e) and retrospective studies (HR: 1.26, 95%CI 1.23\u0026ndash;1.29, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.00001; Fig.\u0026nbsp;3a-3e), between community population (HR: 1.30, 95%CI 1.20\u0026ndash;1.42, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;53%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.00001; Fig.\u0026nbsp;3a-3e) and outpatients or inpatients population (HR: 1.76, 95%CI 1.19\u0026ndash;2.60, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0%, P\u0026thinsp;=\u0026thinsp;0.005; Fig.\u0026nbsp;3a-3e), between Chinese (HR: 1.47, 95%CI 1.21\u0026ndash;1.79, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;32%, P\u0026thinsp;=\u0026thinsp;0.0001; Fig.\u0026nbsp;3a-3e) and non - Chinese (HR: 1.26, 95%CI 1.23\u0026ndash;1.29, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.00001; Fig.\u0026nbsp;3a-3e). Subgroup analysis according to the weight of studies demonstrated that without two larger studies the smaller studies also had consistent association (HR: 1.58, 95%CI 1.28\u0026ndash;1.97, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0%, P༜0.0001;Figure 3a-3e). The sensitivity analysis by excluding one study at a time showed similar results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Publication bias\u003c/h2\u003e \u003cp\u003eThe funnel plots were drawn using stroke as an outcome indicator in order to observe the publication bias of eight cohort studies. The funnel plots were symmetric on visual inspection, suggesting a low risk of publication bias (Fig.\u0026nbsp;4). Since only 8 studies\u003csup\u003e14\u0026ndash;17, 23\u0026ndash;26\u003c/sup\u003e were included, less than 10 studies were required, Egger's regression test was unable to perform in this study.\u003csup\u003e27\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eBased on the fact that insulin resistance was involved in the pathogenesis of atherosclerosis, TyG index was selected as a new surrogate indicator of insulin resistance and a Meta-analysis was performed to assess the association between baseline TyG index and the incidence of stroke in the population without stroke at baseline. After being assessed through the Newcastle-Ottawa scale, all studies included in this study were of high quality. Only cohort studies were included and thus the potential recall bias associated with the cross-sectional design was avoided. Only cohort studies with multivariate adjustments were included, and therefore potential confounding bias was avoided to the greatest extent. In addition, sensitivity and subgroup analyses were carried out in all the included studies to ensure the robustness of the results. Moreover, no significant heterogeneity was observed among all the included cohort studies. The main research results were as follows: (1) A higher baseline TyG index might be an independent predictor of an increase in the incidence of stroke in people without stroke at baseline; (2) Subgroup analyses showed that the correlation between the baseline TyG index and the incidence of stroke was not significantly affected by study design, characteristics of participants (i.e. community population or patients) and ethnicity of participants (i.e. Chinese or non- Chinese) (3)The subgroup analyses based on the outcomes reported demonstrated that the difference between two groups was statistically significant and the HR of ischemic stroke was higher than that of stroke group.\u003c/p\u003e \u003cp\u003eTyG index, as a result of triglycerides and fasting blood glucose, has been recognized as a simple and reliable surrogate indicator of insulin resistance.\u003csup\u003e28\u003c/sup\u003e In clinical applications, it is relatively economical to measure blood triglycerides and fasting blood glucose, and the TyG index can be obtained through simple calculations. A previous study proved that the TyG index has high sensitivity and specificity in detecting insulin resistance,\u003csup\u003e10\u003c/sup\u003ewhich is superior to the homeostasis model assessment of insulin resistance (HOMA-IR).\u003csup\u003e29\u003c/sup\u003e In addition, compared to HOMA-IR, the TyG index, not requiring measurement of insulin levels, can be conveniently and economically used for all patients and healthy people and is also suitable for large-scale screening of insulin resistance.\u003c/p\u003e \u003cp\u003eIt is currently believed that insulin resistance is a key point in the incidence of ischemic stroke, which plays an important role in the pathogenesis of ischemic stroke by promoting the late changes of atherosclerosis. In addition, insulin resistance not only enhances adhesion, activation and aggregation of platelets, but also causes hemodynamics disturbance, all of which are conducive to the occurrence of ischemic stroke.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Furthermore, insulin resistance can also lead to an imbalance of glucose metabolism, resulting in chronic hyperglycemia. This in turn triggers oxidative stress and inflammation, leading to cell damage. In addition, Insulin resistance can alter systemic lipid Metabolism, bringing about dyslipidemia and the development of the well-known lipid triad: (1) high levels of plasma triglycerides, (2) low levels of high-density lipoproteins, and (3) small and dense low-density lipoproteins. This triad, together with endothelial dysfunction (which can also be caused by abnormal insulin signaling), gives rise to the formation of atherosclerotic plaques.\u003csup\u003e30\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eCurrent studies support that the association between TyG index and hemorrhagic stroke was tenuous. Pathologically, hemorrhagic stroke is different from ischemic stroke. The most common causes of hemorrhagic stroke are hypertension (30\u0026ndash;60%), cerebral amyloid angiopathy (10\u0026ndash;30%), anticoagulation (1\u0026ndash;20%) and vascular structural disease (3\u0026ndash;8%), while the cause is unknown in about 5\u0026ndash;20% of cases.\u003csup\u003e31\u003c/sup\u003e A previous study pointed out that there was no evidence of an association between insulin resistance and the incidence of hemorrhagic stroke.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Moreover, a multicenter cross-sectional study demonstrated that the TyG index was not related to the risk of hospitalization and ICU death in patients with severe hemorrhagic stroke.\u003csup\u003e32\u003c/sup\u003e Furthermore, the results of the subgroup analysis according to the outcome reported in our study suggest that there is a subgroup effect about the difference between the stroke subgroup (including ischemic stroke and hemorrhagic stroke) and the ischemic stroke subgroup. The HR of the ischemic stroke subgroup is higher than that of the stroke subgroup, probably resulting from the fact that hemorrhagic stroke events are included in the stroke subgroup, contributing to a decrease in the HR. This result is consistent with the above research, and indirectly reflects that the high TyG index may not have a significant correlation with the incidence of hemorrhagic stroke.\u003c/p\u003e \u003cp\u003eWhen the results of meta-analysis are interpreted, some limitations should be observed. 1. Despite the systematic search, the number of studies eventually included was limited. Due to the limitation of the included research data, the TyG index was used as categorical data for the study and the TyG index used as a continuous variable was in shortage. Therefore, it is not clear whether the association between the TyG index and the incidence of stroke is linear. 2. In the subgroup analysis, only the reported results, the study design, the characteristics of participants and the ethnicity of the population were analyzed. More research is needed to determine whether other research characteristics will affect the results, such as gender, diabetes status, follow-up time, concurrent medications used, \u003cem\u003eetc.\u003c/em\u003e 3. Among the studies we eventually included, there were six Chinese studies and only two non-Chinese studies, one of which was from Asia and the other from Europe. Data from other states such as the Americas, Australia and Africa are still scarce, so more detailed ethnic subgroup analysis should be conducted further. 4. Due to the limitations of research data, hemorrhagic stroke cannot be evaluated in a systematical way. 5. Although the cohort studies included were all adjusted for multivariate, the influence of unadjusted participating factors in the cohort studies could not be ruled out on the HR of the study and the association between TyG index and the incidence of stroke. Similarly, we do not know if the data before multivariate adjustment has an impact on the study. 6. Even though we conducted a subgroup analysis shown the significant affect after excluding two larger studies (Hong 2020 and Wang 2021), which have a combined weight of 86.3% that have a major influence on the meta-analysis.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe current evidence from the included observational cohort studies suggests that a higher TyG index may be related to an increase in the incidence of stroke in people without stroke at baseline. Therefore, a higher TyG index may be an independent predictor of stroke in people without stroke at baseline. The above findings need to be verified by a large-scale prospective cohort study to further clarify the underlying pathophysiological mechanism between TyG index and stroke.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTyG: Triglyceride\u0026ndash;glucose;\u003c/p\u003e\n\u003cp\u003eHRs: Hazard ratios;\u003c/p\u003e\n\u003cp\u003eCIs: Confidence intervals\u003c/p\u003e\n\u003cp\u003eHIEC: hyperinsulinemic-euglycemic clamp test\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHOMA-IR: homeostasis model assessment of insulin resistance\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eAcknowledgements\u003c/h3\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003ch3\u003eAuthors\u0026rsquo; contributions\u003c/h3\u003e\n\u003cp\u003eCL, KX and LZ conceived and designed research; CL, KX and LZ performed the literature search and data extraction; ZN, LZ and TJ performed the literature screening and quality evaluation; CL and KX analyzed data and wrote the initial paper; HX and MF revised the paper; MF had primary responsibility for final content. All authors reviewed and revised the manuscript, and approved the final manuscript for submission.\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003ch3\u003eAvailability of data and material\u003c/h3\u003e\n\u003cp\u003eAll data generated or analysis during this study are included in this published article.\u003c/p\u003e\n\u003ch3\u003eEthics approval and consent to participate\u003c/h3\u003e\n\u003cp\u003eNot declared.\u003c/p\u003e\n\u003ch3\u003eConsent for publication\u003c/h3\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u003c/h3\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003e1.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Collaborators GBDCoD. 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Clinical usefulness of lipid ratios, visceral adiposity indicators, and the triglycerides and glucose index as risk markers of insulin resistance. \u003cem\u003eCardiovasc Diabetol\u003c/em\u003e. 2014;13:146\u003c/p\u003e\n\u003cp\u003e29.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;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. \u003cem\u003eDiabetes Res Clin Pract\u003c/em\u003e. 2011;93:e98-e100\u003c/p\u003e\n\u003cp\u003e30.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Ormazabal V, Nair S, Elfeky O, Aguayo C, Salomon C, Zuniga FA. Association between insulin resistance and the development of cardiovascular disease. \u003cem\u003eCardiovasc Diabetol\u003c/em\u003e. 2018;17:122\u003c/p\u003e\n\u003cp\u003e31.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Cordonnier C, Demchuk A, Ziai W, Anderson CS. Intracerebral haemorrhage: Current approaches to acute management. \u003cem\u003eThe Lancet\u003c/em\u003e. 2018;392:1257-1268\u003c/p\u003e\n\u003cp\u003e32.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Zhang B, Liu L, Ruan H, Zhu Q, Yu D, Yang Y, et al. Triglyceride-glucose index linked to hospital mortality in critically ill stroke: An observational multicentre study on eicu database. \u003cem\u003eFront Med (Lausanne)\u003c/em\u003e. 2020;7:591036\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Triglyceride-glucose index, Insulin resistance, stroke, Meta-analysis","lastPublishedDoi":"10.21203/rs.3.rs-1972856/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1972856/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eInsulin resistance has been confirmed to be involved in atherosclerosis pathogenesis. As a new indicator, the triglyceride-glucose (TyG) index has greater operability in the evaluation of insulin resistance. Previous studies have shown inconsistent results in evaluating the association between TyG index and stroke incidence in people without stroke at baseline. Therefore, this study was to systematically assess the association by conducting a meta-analysis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eCohort studies on TyG index and stroke were obtained by searching the PubMed, the Cochrane Library (CENTRAL) and EMBASE databases. The multivariate-adjusted correlation of end points was studied, including TyG index and stroke (including ischemic stroke and hemorrhagic stroke) or ischemic stroke. Review Manager 5.3 and Stata 16 were adopted for meta-analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eEight cohort studies with 5,719,098 participants were included in this meta-analysis. The results showed that participants with the highest TyG index category at baseline, compared to those with the lowest TyG index category, were independently associated with a higher risk of stroke [Hazard ratio (HR): 1.32, 95% confidence interval (CI): 1.22\u0026ndash;1.43, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;32%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.00001]. Subgroups analysis remained that study designs, ethnicity and characteristics of participants had no subgroup effects (for subgroup analysis, all P༞0.05), except outcome report(stroke or ischemic stroke) which suggested that it may had a stronger effect on the association(χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;4.78, P\u0026thinsp;=\u0026thinsp;0.03).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eA higher TyG index may be independently associated with a higher risk of stroke in people without stroke at baseline. Keywords: Triglyceride-glucose index, Insulin resistance, stroke, Meta-analysis\u003c/p\u003e","manuscriptTitle":"Triglyceride-Glucose Index and the Incidence of Stroke: A Meta-Analysis of Cohort Studies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-09-01 15:24:51","doi":"10.21203/rs.3.rs-1972856/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"87747756-65a8-4cc3-bd41-8b45ddf8b8e1","owner":[],"postedDate":"September 1st, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-09-07T05:03:12+00:00","versionOfRecord":[],"versionCreatedAt":"2022-09-01 15:24:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1972856","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1972856","identity":"rs-1972856","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}