Study population
The APAC study is a community-based, prospective, long-term follow-up study to investigate the epidemiology of asymptomatic polyvascular abnormalities in Chinese adults. The detailed of this study design has been published previously.[16, 17] In brief, the APAC study included 5440 participants who were aged over or equal to 40 years, free of coronary artery disease, transient ischemic attack, and stroke and follow-up biennially. In the current study, we excluded 59 participants with missing data on TG, FBG and vascular examination results at baseline (2010), leaving 5,381 participants in the cross-sectional analysis. In the longitudinal cohort analysis, we further excluded 712 participants with ICAS at baseline and 1,222 participants without ICAS measurement at 2012, therefore, 3,447 participants were enrolled in the longitudinal cohort study to analysis the association between the TyG index and incident ICAS, similarly, 1,853 participants were enrolled for the analysis of incident ECAS (Figure 1). The study was performed according to the guidelines from the Helsinki Declaration and was approved by the Ethics Committees of the Kailuan General Hospital and the Beijing Tiantan Hospital. Written informed consent was obtained from all participants. Subjects were also informed of abnormal findings and recommended treatment.
Calculation of the TyG index
Fasting blood samples were collected from the antecubital vein after an 8- to 12-h overnight fast. All the plasma samples were assessed using an auto-analyzer (Hitachi 747, Tokyo, Japan) at the central laboratory of Kailuan Hospital. FBG levels were measured using the hexokinase/glucose-6-phosphate dehydrogenase method with the coefficient of variation using blind quality control specimens < 2.0%. Serum total cholesterol, TG, low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) levels were measured with the enzymatic colorimetric method. The TyG index was calculated as ln [(fasting TG (mg/dl) × FBG (mg/dl)/2].[18]
Assessment of ICAS and ECAS
ICAS was assessed by peak systolic flowing velocity measurements via Transcranial Doppler Ultrasonography, which was performed by two experienced neurologists who were blinded to the baseline information of the participants with portable devices (EME Companion, Nicolet, Madison, WI, USA).[19] The definition of ICAS was according to published criteria[20, 21]: >140 cm/s for the middle cerebral artery, >120 cm/s for the anterior cerebral artery, >100 cm/s for the posterior cerebral artery and vertebral-basilar, and >120 cm/s for the siphon internal carotid artery. In addition to the above mentioned criteria, the patients’ age, the presence of turbulence sound or disturbance in echo frequency, and whether the abnormal velocity was segmental were also taken into account for ICAS diagnosis. Subjects without a good temporal window were considered without stenosis. ICAS was diagnosed if at least one of the studied arteries showed evidence of stenosis or occlusion.
ECAS was defined as the presence of common or extacranial internal carotid artery stenosis or extracranial vertebral artery stenosis. All participants underwent a bilateral
carotid duplex ultrasound (Philips iU-22 ultrasound system, Philips Medical Systems, Bothell, WA) in the supine position of structures including the common carotid, internal carotid artery, external carotid artery, vertebral artery and subclavian artery. Both sides of the carotid stenosis were assessed for the presence of ECAS(>0%), which was graded based on recommendations from the Society of Radiologists in Ultrasound Consensus Conference.[22]
Association of covariates
Data on other related baseline and medical information were collected via standard questionnaire by trained investigators, including age, sex, smoking status, drinking status, physical activity. Education was classified as illiteracy or primary school, middle school, and high school or above. Income was categorized into > 3000 and ≤ 3000 yuan/month. Active physical activity was defined as ≥ 4 times per week and ≥ 20 minutes at a time. Smoking and drinking status stratified into never, former or current. Body mass index (BMI) was calculated by dividing body weight (kg) by the square of height (m). Blood pressure was measured in the in the seated position using a mercury sphygmomanometer, and the average of three readings was calculated as systolic blood pressure (SBP) and diastolic blood pressure (DBP). High-sensitivity C-reactive protein (hs-CRP) levels were measured with high-sensitivity particle-enhanced immunonephelometry assay. Hypertension was defined as SBP ≥140 mm Hg or DBP ≥90 mm Hg, any use of antihypertensive drugs, or a self-reported history of hypertension. Diabetes was defined as FBG levels ≥7.0mmol/L, any use of glucose-lowing drugs, or any self-reported history of diabetes. Dyslipidemia was defined as any self-reported history or use of lipid-lowering drugs, or total cholesterol levels ≥5.17 mmol/L.
Statistical analysis
Participants were categorized into three groups according to tertiles of the TyG index. Continuous variables are described as median and interquartile range (IQR) owing the skewed distribution. Categorical variables are described as frequencies and percentages. The Wilcoxon or Kruskal–Wallis test was used to analyze group differences for continuous variables, and the chi-square test was used for categorical variables. Multivariable logistic regression models were constructed to assess the association of the TyG index with the outcomes in the cross-sectional and cohort study, by calculating the odds ratios (ORs) and their 95% confidence intervals (CIs). Model 1 was unadjusted, Model 2 was adjusted for age and sex, Model 3 was further adjusted for BMI, education, income physical activity, smoking status, drinking status, history of hypertension, diabetes, dyslipidemia, antihypertensive agents, antidiabetic agents, lipid-lowering agents, HDL-C, LDL-C, and hs-CRP. P-values for trend were computed using tertiles as ordinal variables. In addition, we also analyzed the effect of the TyG index on the outcomes as a continuous variable using a restricted cubic spline with 5 knots (at the 5th, 25th, 50th, 75th, and 95th percentiles), adjusted for all the covariates above-mentioned.
In addition, we calculated C-statistics, integrated discrimination improvement (IDI) and category-free net reclassification improvement (NRI) to evaluate the incremental predictive value of the TyG index beyond conventional risk factors. Linear regression was used to evaluate the correlation between the TyG index and vascular stenosis risk factors. Furthermore, subgroup analyses according to age (<60 years and ≥60 years), sex, BMI (<28 and ≥28 kg/m2), and diabetes (no and yes) were performed to examine the consistence of the effect of a high TyG index on the outcomes, where the interaction of the TyG index with stratified variables was examined by likelihood ratio tests. All analyses were conducted using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). A two-sided P<0.05 was considered statistically significant.