Association of the triglyceride glucose-waist height ratio with asymptomatic intracranial arterial stenosis

Abstract

Background and objective

This study evaluated the associations of the triglyceride-glucose (TyG) index or its attendant parameters, known as reliable measures of insulin resistance, with asymptomatic intracranial arterial stenosis (aICAS), along with their value for distinguishing individuals with a notable aICAS burden.

Methods

This study enrolled 2000 participants (aged ≥ 40 years) based on the Rose asymptomatic IntraCranial Artery Stenosis study. Transcranial Doppler ultrasound combined with magnetic resonance angiography was utilized to confirm aICAS. Logistic regression was applied to assess the associations between TyG or TyG-related indices (TyG-body mass index, TyG-waist circumference [TyG-WC], TyG-waist-to-hip ratio [TyG-WHR], or TyG-waist-to-height ratio [TyG-WHtR]) and aICAS. The diagnostic potential of them was investigated using receiver operating characteristic (ROC) analysis.

Results

Among the 2000 participants, 146 (7.3%) had a diagnosis of aICAS. TyG-WC, TyG-WHR, or TyG-WHtR were independently related to the prevalence of aICAS (TyG-WC: OR 1.26, 95%CI 1.03–1.54; TyG-WHR: OR 1.29, 95%CI 1.07–1.55; TyG-WHtR: OR 1.25, 95%CI 1.04–1.51). ROC analysis disclosed that TyG-WHtR had significant superior performance in identifying aICAS compared with other parameters (all P < 0.05). Subgroup analysis revealed that higher TyG-WHtR values showed a positive association with a heightened prevalence of aICAS observed in elderly participants aged ≥ 65 years (OR 1.69, 95%CI 1.20–2.38), and hypertensive patients (OR 1.30, 95%CI 1.06–1.59).

Conclusion

The association of TyG-WHtR with aICAS showed that TyG-WHtR might be a more effective indicator for identifying populations with early-stage aICAS.

Introduction

Stroke is associated with significant mortality and disability rates, placing a substantial burden on nations, especially those with low and middle incomes [1]. Ischemic stroke is the predominant form of stroke [2]. Intracranial arterial stenosis (ICAS) is a principal contributor to ischemic stroke universally, making it a critical objective for stroke prophylaxis, especially in Asians [3, 4]. Asymptomatic ICAS (aICAS) serves as the subclinical stage of ICAS and is progressively acknowledged as a crucial risk determinant on the development of ischemic stroke [5]. Identifying biomarkers for aICAS at an early stage is essential for ischemic stroke prevention.

The primary feature of insulin resistance (IR) is the diminished responsiveness of tissues to insulin. Disturbances in glycolipid metabolic processes caused by IR can elicit oxidative stress, systemic inflammatory responses [6,7,8], and endothelial dysfunction, thereby accelerating the development of arterial vascular disease [8, 9]. Accumulating researches highlight that IR is a pivotal contributor to the pathogenic mechanisms underlying stroke, primarily through its association with atherosclerosis [10,11,12]. It can be posited that the degree of IR could function as a pivotal prognostic marker for anticipating aICAS. The triglyceride-glucose (TyG) index is served as a pivotal instrument for assessing IR [13, 14]. Evidence from several studies supports its use as a reliable, independent marker for ischemic stroke risk assessment [15, 16]. Although some studies have probed the TyG index and ICAS, the evidence is limited, and the results of some studies remain inconsistent [17,18,19]. Further research should be performed to confirm the association between TyG and intracranial artery lesions.

It is worth noting that adipose tissue is widely acknowledged as a significant endocrine organ, and alterations in the spectrum of adipokines can have profound effects on insulin sensitivity and various metabolic disorders [20,21,22]. Earlier investigations highlighted a notable link between abdominal adiposity indices and ICAS [23]. Several integrated indices that combine the TyG index with obesity-related parameters, including TyG-body mass index (TyG-BMI) [24], TyG-waist circumference (TyG-WC) [25], TyG-waist-to-hip ratio (TyG-WHR) [26], and TyG-waist-to-height ratio (TyG-WHtR) [27], have been proposed as effective tools for evaluating IR. These integrated indices have demonstrated superior presentation in contrast to TyG alone in identifying IR [27], and might offer a more comprehensive and accurate evaluation of vascular lesions. Emerging evidence from several studies demonstrates that TyG-derived composite indices exhibit superior discriminative capacity for cardiovascular and cerebrovascular lesions compared to the standalone TyG index [28,29,30,31,32]. However, the existing studies investigating TyG and TyG-related parameters have focused predominantly on the cardiovascular diseases. There are relatively few studies in the field of cerebrovascular diseases, particularly with regard to aICAS, and the findings remain inconclusive [33,34,35].

Among all TyG-derived indices, whether used individually or in combination with anthropometric measures, there remains no consensus on which index exhibits superior sensitivity in identifying the early stages of stroke. This study evaluated the associations between TyG or TyG-related indicators and aICAS in an asymptomatic Chinese rural community population, using aICAS status as the primary outcome, while also assessing whether combined TyG indicators are superior to the TyG alone in screening high-prevalence stroke population.

Methods

Study designs

The data originated from the Rose asymptomatic IntraCranial Artery Stenosis (RICAS) project, a longitudinal cohort study initiated in 2017 that targeted rural populations in Kongcun Town, Pingyin County, Shandong, China. The methodology of the RICAS project has been thoroughly outlined in earlier publications [36].

At baseline, 2,474 rural residents over the age of 40 years with no reported transient ischemic attack and no history of clinically diagnosed stroke underwent standardized face-to-face interviews followed by comprehensive physical examinations. The following participants were excluded: (1) participants lacking full baseline data (n = 163); (2) participants with missing clinical data, including results from blood detection, transcranial Doppler (TCD) scans, magnetic resonance imaging (MRI) and magnetic resonance angiography (MRA) (n = 284); (3) participants without waist circumference (n = 2) or height values (n = 25). Eventually, 2,000 participants in this study were eligible for analysis. Figure 1 shows the study flowchart.

Fig.1

Flow chart of the study participants. RICAS, Rose asymptomatic IntraCranial Artery Stenosis; aICAS, asymptomatic intracranial arterial stenosis; TCD, transcranial Doppler; MRI, magnetic resonance imaging; MRA, magnetic resonance angiography

Data collection and assessments

Baseline data and cardiovascular risk factors (CRFs) data were collected via standardized methods, consistent with definitions in previous studies [36]. Divide weight (kg) by height squared (m²) to get BMI. WHR is the ratio of waist length (cm) to hip circumference(cm). WHtR was measured as WC (cm) divided by height (cm). Other details, such as blood tests, have been thoroughly documented in prior researches [36, 37].

Assessment of TyG and TyG-related indices

TyG was computed from fasting triglycerides (TG) and fasting blood glucose (FBS) following the same approach as in the previous study [13, 14]. TyG-related indices were obtained by multiplying TyG with each obesity-related parameter [26]. The specific formulae for all of the above indicators are shown in Table S1. The indicators mentioned above were subsequently categorized as categorical variables by quartile.

Diagnosis of asymptomatic intracranial atherosclerosis

The aICAS evaluation protocols have been elaborated in detail in previous studies [36, 38]. Briefly, the diagnosis of aICAS was divided into two stages. First, the participants remained at rest for five minutes in a quiet room while two ultrasound specialists used the Visas Companion III TCD system to inspect the intracranial arteries through the temporal, occipital, and ocular windows. Subsequently, any participants diagnosed with ≥ 50% vascular stenosis by TCD were subjected to brain MRI and MRA imaging in accordance with the standard protocol [39]. Stenosis was classified as normal (no degree of stenosis), mild (stenosis < 50%), moderate (50%− 70%), severe (stenosis ≥ 70%), or occlusive [40]. aICAS referred to the detection of any stenosis in one or more intracranial arteries on MRA.

Statistical analysis

The participants were classified into two categories depending on whether aICAS was present. Continuous variables in this study, due to their nonnormal distribution, were depicted as medians and interquartile ranges (IQRs). Mann-Whitney U test was employed to assess continuous variables. For categorical variables, counts and percentages (%) were utilized for representing the variables and chi-square tests were used for analytical comparisons. The associations of TyG or TyG-related indices with aICAS were assessed via logistic regression analysis. The crude odds ratios (ORs), adjusted ORs, and 95% confidence intervals (CIs) indicate the strength of the association, with the results calculated from three models: Model 1 accounted for sex and age; Model 2 incorporated smoking habits, drinking habits, educational experience, and hypertension in addition to Model 1; and Model 3 included high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and high-sensitivity C-reactive protein (hs-CRP) in addition to Model 2. Variables with multicollinearity were excluded from the model. Receiver operating characteristic (ROC) curves were generated to assess the diagnostic effect of TyG or TyG-related indices on aICAS, with area under the curve (AUC) values computed and compared across these indicators. To investigate the potential nonlinear relationships between the independent variables and aICAS more deeply, this study utilized the restricted cubic spline (RCS) method. Additionally, further statistical analyses were conducted in multiple subgroups such as age (over 65 years), sex, and the presence or absence of hypertension, diabetes or dyslipidemia to ensure the robustness of the results. To address the issue of multiple comparisons in subgroup analyses, we used the Benjamini–Hochberg method to control for false discovery rate (FDR). Raw p-values for all subgroup analyses were corrected for FDR, with FDR < 0.05 as the statistical significance threshold. For other statistical procedures, statistical importance was established by a P-value of less than the 0.05 threshold on both sides. The data analysis outlined above was implemented using IBM SPSS Statistics, v26.0, and R Studio, v4.2.3, for Windows.

Results

Baseline characteristics

Among the 2000 participants, a diagnosis of aICAS was made in totally 146 individuals (7.3%). The median age was 56 (49–65) years and 958 (47.9%) were males. Participants with an aICAS diagnosis were typically older and exhibited a greater incidence of hypertension, diabetes, and dyslipidemia. Moreover, versus participants without aICAS, those with aICAS demonstrated enhanced levels of hs-CRP, TyG index and four TyG-related indices (all P < 0.001). (Table 1).

Table 1 Baseline characteristics of subjects categorized by aICAS

Associations of TyG index or related indices with aICAS

Among TyG and the other 4 TyG-related indices, elevated TyG-WC, TyG-WHR, and TyG-WHtR levels were each obviously related to the prevalence of aICAS, but neither TyG nor TyG-BMI showed a statistically significant relationship with aICAS (Table 2). In the highest quartile (Q4), only TyG-WHtR demonstrated a noteworthy association with the presence of aICAS in contrast to the lowest quartile (Q1) (Table 2). The ROC analysis exhibited that TyG-WHtR showed significantly better diagnostic efficacy for aICAS than the other parameters did (Fig. 2, Table S2). The addition of TyG-WHtR to known traditional risk factors revealed a significant increase in AUC (Fig. S1). In addition, to further explore the associations between the independent variables and aICAS, the RCS model was developed in this study, and the RCS models revealed linear associations between all the parameters and aICAS (Fig. S2). When aICAS was categorized by stenosis severity, TyG-BMI, TyG-WC, TyG-WHR, and TyG-WHtR were correlated with only moderate-to-severe stenosis. As categorical data, only TyG-BMI, TyG-WC, and TyG-WHtR in Q4 revealed an associated trend with moderate-to-severe stenosis (Table 3, Table S3).

Table 2 Associations of TyG index and related parameters with aICAS

Fig.2

Receiver operative characteristic curves for identifying aICAS by TyG index and related parameters AUC comparison: TyG-BMI vs. TyG: P = 0.615; TyG-WC vs. TyG: P = 0.282; TyG-WHR vs. TyG: P = 0.846; TyG-WHtR vs. TyG: P = 0.045. AUC, area under the curve; CI, confidence interval; aICAS, asymptomatic intracranial arterial stenosis; TyG, triglyceride-glucose index; BMI, body mass index; WC: waist circumference; WHR, waist-to-hip ratio; WHtR, waist-to-height ratio

Table 3 Associations of TyG-WHtR with the degree of aICAS

Subgroup analyses

Figure 3 presented the findings from subgroup analyses of TyG-WHtR, and others were exhibited in the supplementary materials. A higher prevalence of aICAS was observed with increasing TyG-WHtR among elder individuals (≥ 65 years) and individuals with hypertension (Fig. 3).

Fig.3

Subgroup analyses for the association of TyG-WHtR as continuous variable with aICAS OR and 95% CI were estimated from the multivariable logistic regression models that were adjusted for age, sex, smoking habit, drinking habit, education, hypertension, LDL-C, HDL-C, hs-CRP (except for the stratified variable) OR, odds ratio. CI, confidence interval; FDR, false discovery rate; aICAS, asymptomatic intracranial arterial stenosis; TyG, triglyceride-glucose index; WHtR, waist-to-height ratio; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; hs-CRP: high-sensitivity C-reactive protein

Significant associations between TyG-WC and aICAS were identified in older individuals, and individuals with hypertension (Fig. S3). The associations between TyG-WHR and aICAS was observed in males, older individuals, and individuals with hypertension (Fig. S4). For TyG and TyG-BMI, they were not associated with aICAS in any of the subgroups (Fig. S5, Fig. S6).

Discussion

This rural community-based study revealed that elevated TyG-WHtR levels were independently correlated with elevated prevalance of aICAS, particularly moderate-to-severe cases, even after accounting for multiple clinical risk factors. Based on the results of this study, TyG-WHtR showed superior performance compared with TyG alone or other TyG-related indices in identifying populations with aICAS, particularly among older individuals and those with hypertension.

TyG was first presented as a credible indicator for the assessment of IR [14]. Recently, novel TyG-related indicators, including TyG-BMI, TyG-WC, TyG-WHR, and TyG-WHtR, have been developed in conjunction with anthropometric data to augment the comprehensiveness and precision of the TyG index [27]. Extensive research has indicated that TyG and the combined TyG-based indicators may act as latent biomarkers for evaluating the occurrence of cardiovascular and cerebrovascular diseases. [33, 35, 41]. A retrospective study demonstrated that TyG may be established as a valuable metric for assessing ICAS and its severity [17], and another community-based study reached similar conclusions [42]. However, an analysis incorporating cross-sectional and longitudinal data from a Chinese adult cohort indicated that an elevated TyG index did not show significant association with ICAS [18]. In this study, although an initial pronounced association of TyG and aICAS was observed, this association was attenuated in the final model following adjustments for potential confounding variables. Several underlying factors may explain this discrepancy. First, stronger effects from confounding variables might diminish the observed association between TyG levels and aICAS. Second, considering TyG alone without incorporating indicators of obesity may limit its comprehensiveness and predictive power for aICAS. To date, some studies have found that TyG-related parameters exhibit superior performance compared with TyG in distinguishing participants with high cardiovascular risk [26, 41]. In this study, an evident association was found between TyG-WHtR and aICAS, even after multiple clinical risk factors were considered. Furthermore, ROC curves revealed that TyG-WHtR exhibited superior diagnostic performance for the prevalence of aICAS compared with other indicators, and its diagnostic efficiency was significantly greater than that of TyG alone. Compared with TyG index and other TyG-related parameters, TyG-WHtR had shown the strongest association with arterial stiffness in a study involving obese participants in China [30]. More and more studies had confirmed that TyG-WHtR was strongly associated with cardiovascular diseases [29, 31]. Notably, a comprehensive cross-sectional analysis in Hunan Province, China highlighted that TyG-WHtR may hold a comparative advantage over both TyG and TyG-BMI in determining the prevalence of carotid atherosclerosis [34]. Additionally, a current study from a public database showed that TyG-WHtR identified the strongest predictor of angina pectoris and heart failure compared with other TyG-related parameters [43], which is consistent with the outcomes of this study. The superior performance of TyG-WHtR can be attributed to several potential factors as follows: First, TyG-WHtR combines obesity parameters and provides more comprehensive information on metabolic disorders. Second, WHtR, which includes height and waist circumference, provides a more complete description of body shape and is more responsive to central obesity than the other obesity parameters involved in this study [44, 45]. Furthermore, previous studies have demonstrated the outperformance of WHtR over other obesity indicators for detecting cardiometabolic risk factors [45,46,47], and a prospective cohort study have found that WHtR was linearly associated with some ischemic cardiovascular and cerebrovascular diseases [48], which may elucidate the advantages of TyG-WHtR over TyG or other combined indicators in this study.

The mechanisms behind the association of TyG-WHtR with aICAS are complex. In brief, IR exacerbates endothelial dysfunction, promotes vascular inflammation, and accelerates atherosclerosis, all of which are key contributors to ischemic cerebrovascular disease [49]. As a marker of IR, the TyG index can capture these metabolic disturbances. Meanwhile, fat mass and visceral obesity contribute to the progression of atherosclerotic endothelial dysfunction through metabolic disturbances, ultimately leading to the development of atherosclerotic cardiovascular disease [50]. As mentioned before, WHtR is linearly associated with ischemic stroke [48], and serves as a robust indicator of visceral adiposity, offering a standardized measurement that is more objective than WC alone and better reflects body fat distribution compared to BMI [51]. Collectively, the TyG-WHtR index may be a more comprehensive and more suitable marker for assessing IR. In conclusion, TyG-WHtR better suggests the process of dyslipidemia, sodium and water retention, inhibition of fibrinolysis and promotion of inflammatory responses that accelerate atherogenesis [52], and is therefore associated with aICAS.

Further stratified analysis exhibited a stronger association between TyG-WHtR and aICAS in participants aged ≥ 65 years, but not in middle-aged individuals. This outcome is in consonance with the antecedent investigations [35, 53]. Furthermore, previous studies have hypothesized that IR has the potential to elevate blood pressure through mechanisms such as enhanced sodium reabsorption in the kidneys, along with dysregulated initiation of the renin–angiotensin–aldosterone system, thus promoting arterial vascular disease [54, 55]. Our study revealed that TyG-WHtR might be a risk parameter for aICAS events in individuals with hypertension, but that association is diminished in non-hypertensive participants, which provides further evidence in support of the hypothesis above. These subgroups are based on common clinical risk factors, and the results of the subgroup analysis are preliminary and can provide a reference for exploring aICAS in populations with different clinical characteristics. However, owing to the uncertainty in the association of the aforementioned factors in a population, additional external validation in a more representative population is necessary to confirm this conjecture.

Comparative analysis and novel contributions to existing knowledge

As mentioned earlier, TyG-WHtR is an effective surrogate for IR [27] and demonstrated significantly associations with carotid atherosclerosis, angina pectoris, and heart failure [34, 43]. This study represents the first thorough exploration of the association between TyG-WHtR and aICAS, demonstrating that TyG-WHtR is independently linked to aICAS, particularly moderate-to-severe stenosis, and outperforms other TyG-related parameters. This suggests that a more comprehensive combined index may have a stronger ability to identify early stages of stroke, and selecting effective composite indicators is beneficial for the primary prevention of stroke.

Study strengths and limitations

As far as is currently documented, the present study investigated firstly the connections of TyG combined with obesity indices and aICAS in rural Chinese residents, showing that TyG-WHtR demonstrates superior diagnostic efficacy for aICAS compared with other TyG-related indicators. TyG-WHtR could act as a useful instrument for facilitating early intervention in individuals with aICAS. Nevertheless, the study possesses certain limitations. First, this is a single-centre study, which limits the ability to generalize the conclusions. Second, this study ultimately concluded that the relationship between TyG-WHtR and aICAS is linear, but the logistic regression when TyG-WHtR was used as a categorical variable showed a tendency to inflect. A more precise relationship curve between TyG-WHtR and aICAS needs to be explored by multicenter studies with larger sample size. Third, not all covariates were included in this study, and some potentially confounding variables, such as dietary habits, physical activity, and other lifestyle factors, may have influenced the results. Finally, the cross-sectional design precludes ultimate deductions concerning a causative relationship between TyG-WHtR and aICAS.

Conclusion

In this community-based study, an elevated TyG-WHtR was notably associated with aICAS among TyG and TyG-related parameters, with particularly pronounced effects in elderly participants aged ≥65 years, and those with hypertension. These findings imply that TyG-WHtR could serve as a straightforward and efficient tool for evaluating aICAS risk. Meanwhile, the tests used to calculate TyG-WHtR were all inexpensive and readily available, which may be beneficial for the primary prevention of ischemic stroke.