Changes in Triglyceride-glucose Index Predict the Risk of Cardiovascular Diseases in the General Population: a Prospective Cohort Study

Changes in Triglyceride-glucose Index Predict the Risk of Cardiovascular Diseases in the General Population: a Prospective Cohort Study | 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 Original investigation Changes in Triglyceride-glucose Index Predict the Risk of Cardiovascular Diseases in the General Population: a Prospective Cohort Study Anxin Wang, Xue Tian, Yingting Zuo, Shuohua Chen, Xia Meng, Shouling Wu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-375689/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background: The relationship between baseline triglyceride-glucose (TyG) index and cardiovascular disease (CVD) has been confirmed by former studies. However, the effect of longitudinal changes in TyG index on CVD remains uncertain. This study aimed to investigate the association of changes in TyG index with CVD in the general population. Methods: The current study included 62,443 Chinese population who were free of CVD. TyG index was calculated as ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2], changes in TyG index was defined as the difference in TyG index between 2010 and 2006. Cox proportional hazard model and restricted cubic spline was used to examine the association between changes in TyG index and CVD. Results: During a median follow-up of 7.01 years, 2,530 (4.05%) incident CVD occurred, including 2,018 (3.23%) stroke and 545 (0.87%) MI. Risk of CVD was increased with quartiles of changes in TyG index, the adjusted hazard ratio (HR) in Q4 group versus Q1 group was 1.37 (95% confidence interval [CI], 1.21-1.54) for the overall CVD, 1.38 (95% CI, 1.19-1.60) for stroke, and 1.36 (95% CI, 1.05-1.76) for MI. Restricted cubic spline also showed cumulative increased risk of CVD with increasing changes in TyG index. Furthermore, the addition of changes in TyG index to a baseline risk model for CVD improved the C-statistics ( P =0.0097), the integrated discrimination improvement ( P <0.0001), and the category-free net reclassification improvement ( P <0.0001). Similar results were observed for stroke and MI. Conclusions: Substantial changes in TyG index can independently predict the risk of CVD in the general population. Monitoring long-term changes in TyG may be helpful in the early identification of individuals at high risk of CVD. Cardiac & Cardiovascular Systems Triglyceride-glucose index Longitudinal changes Cardiovascular disease Stroke Myocardial infarction Predictive value Figures Figure 1 Figure 2 Figure 3 Background Insulin resistance, the critical mechanism of the pathogenesis of diabetes mellitus, has been extensively demonstrated to be significantly related to be the development of cardiovascular disease (CVD).[ 1 – 3 ] Insulin resistance has been reported not only to be associated with CVD risk factors such as diabetes mellitus[ 4 ], hypertension[ 5 ], dyslipidemia[ 6 ], and obesity[ 7 ], but also is an independent risk factor for CVD[ 1 – 3 ], thus an early detection and control of insulin resistance may contribute to the prevention of CVD. Although the hyperinsulinemic-euglycemic clamp is the gold-standard test for IR assessment, it is not commonly used in clinical settings and large population studies due to the complex testing process and expensive cost.[ 8 ] In this regard, triglyceride-glucose (TyG) index, a product of triglyceride (TG) and fasting blood glucose (FBG), appears as a simple surrogate for insulin resistance with high correlation with the gold-standard test.[ 9 – 11 ] Cohort studies have found that TyG index was an importance risk factor for incident CVD.[ 12 – 16 ] However, an inherent limitation of previous studies is the TyG index was evaluated on a single time point, there has been no consideration of how the TyG index varies within individuals over time and the subsequent effect, which may yield a biased estimate of the relationship of the TyG index and CVD risk. While the effect of longitudinal changes in TyG index over time on CVD has not been fully studied up to date. We therefore conducted the present study to identify the potential association of changes in TyG index with CVD and its subtypes based on a large community-based prospective cohort study. Methods Study population The Kailuan study is a prospective cohort study in the Kailuan community in Tangshan, China. The detailed study design and procedures have been described previously.[17-19] During June 2006 to October 2007, a total of 101,510 participants (81 110 men and 20 400 women; aged 18 to 98 yeas) were enrolled in the first survey (baseline) and underwent a comprehensive biennial health examination. All participants were followed up until their death or December 31, 2017. Changes in TyG index was developed from 2006 to 2010 to predict CVD risk from 2010 to 2017 (Figure S1). We excluded 3,669 and 2,042 participants with MI or stroke in or prior 2010, 30,971 participants who did not finish the survey at 2010, 1,282 and 1,103 participants with missing data on FBG or TG at baseline or the survey at 2010. Ultimately, a total of 62,443 participants were enrolled in the present study (Figure S2). The study was performed according to the guidelines of the Helsinki Declaration and was approved by the Ethics Committee of Kailuan General Hospital (approval number: 2006-05) and Beijing Tiantan Hospital (approval number: 2010-014-01). All participants were agreed to take part in the study and provided written informed consent. Data collection and definitions Information on demographic characteristics, lifestyle factors (smoking status, drinking status, and physical activity), and medical history were collected via standardized questionnaire by trained staffs. Education was classified as illiteracy or primary school, middle school, and high school or above. Income was categorized into > 800 and ≤ 800 yuan/month. Smoking and drinking status were stratified into never, former or current. Physically active was classified as ≥4 times per week and ≥20 minutes at a time, <80 minutes per week, or none. Body mass index (BMI) was calculated by dividing body weight (kg) by the square of height (m 2 ). Blood pressure was measured in the in the seated position using a mercury sphygmomanometer, the average of 3 readings were calculated as systolic blood pressure (SBP) and diastolic blood pressure (DBP). All the blood samples were analyzed using an auto-analyzer (Hitachi 747, Hitachi, Tokyo, Japan) on the day of the blood draw. The biochemical indicators tested included fasting blood glucose, serum lipids, serum creatinine, and high-sensitivity C-reactive protein (hs-CRP). Hypertension was defined as SBP ≥140 mm Hg or DBP ≥90 mm Hg, any use of the antihypertensive drug, or self-reported history of hypertension. Diabetes was defined as FBG≥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 TC ≥ 5.17 mmol/L. Calculation of changes in TyG index The TyG index was calculated as ln (fasting TG [mg/dl] × FBG [mg/dl]/2) as previous done.[20, 21] Changes in TyG index was calculated as TyG index value at 2010 minus that at baseline (2006). Assessment of outcomes The outcome in the present study was the first occurrence of CVD events. The types of CVD included stroke and MI. We defined CVD events as described previously.[17, 22, 23] The database of CVD diagnoses was obtained from the Municipal Social Insurance Institution and Hospital Discharge Register and was updated annually during the follow-up period. An expert panel collected and reviewed annual discharge records from 11 Kailuan hospitals to identify patients who were suspected of CVD. Incident stroke was diagnosis based on neurological signs, clinical symptoms, and neuroimaging tests, including computed tomography or magnetic resonance, according to the World Health Organization criteria.[24] MI was diagnosed according to the criteria of the World Health Organization on the basis of clinical symptoms, changes in the serum concentrations of cardiac enzymes and biomarkers, and electrocardiographic results.[17, 25] Statistical analysis Participants were divided into four categories according to quartiles of changes in TyG index. The baseline characteristics were presented as mean±standard deviation (SD) or frequency with percentage as appropriate. Tests of differences in characteristics across changes in TyG index categories were performed using analysis of variance or the Kruskal-Wallis test for continuous variables according to distribution and chi-square for categorical variables. The person-years were determined from the date when the message was collected at baseline to either the date of MI onset, death, or the date of participating in the last examination in this analysis, whichever came first. Kaplan-Meier methods were performed to evaluate the incidence rate of CVD and its subtypes, and differences among groups were evaluated by log-rank test. Cox proportional hazard regression model was applied to calculated hazard ratio (HR) and 95% confidence interval (CI) for CVD and its subtypes. The proportional hazard assumption was evaluated with visualization of Schoenfeld residuals and no potential violation was observed. Two models were constructed. Model 1 was adjusted for age, sex, and TyG index at baseline. Model was additionally adjusted for education, income, smoking status, drinking status, physical activity, BMI, SBP, DBP, a history of hypertension, diabetes mellitus, and dyslipidemia, antidiabetic drugs, lipid-lowering drugs, antihypertensive drugs, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and hs-CRP at baseline. P -values for trend were computed using quartiles as ordinal variables. To capture the dose-response relationship between changes in TyG index and CVD, restricted cubic splines with four knots at the 5th, 35th, 65th, and 95th percentiles of TyG index change distribution with median of the Q1 group as the reference point.[26] Additional analyses were performed to validate the robustness of the results. First, competing risk model was applied to assess the association between changes in TyG index and the outcomes considering non-CVD death as a competing risk event. Second, restricted analysis was conducted by excluding participants with abnormal FBG level (≥7.0 mmol/L) or abnormal TG level (≥1.7 mmol/L) at baseline.[20] Third, to explore the potential impact of reverse causality, we repeated the primary analysis using a 2-year lag period by excluding incident stroke cases from the first 2 years of follow-up. Subgroup analyses were conducted stratified participants by age (< 60 and ≥ 60 years), sex (women and men), BMI (<25 and ≥ 25 kg/m 2 ), and FBG (<5.6, 5.6-7.0, and ≥ 7.0 mmol/L) to assess the possible effect modification by these variables, interactions between subgroups were tested using likelihood ratio tests comparing models with and those without multiplicative interaction terms. Additionally, we used C statistics, integrated discrimination improvement (IDI), and net reclassification index (NRI) to evaluate the incremental predictive value of change in TyG index beyond conventional risk factors. All analyses were performed using SAS version 9.4 (SAS Institute, Cary, North Carolina) and R software version 3.6.1 (R Core Team, Vienna, Austria). All statistical tests were 2-sided, and P < 0.05 was considered statistically significant. Results Baseline characteristics A total of 62,443 eligible participants were included, the mean age was 49.07 ± 11.84 years, and 76.59% were men. Comparison of baseline characteristics between and participants and non-participants due to missing the 2010 survey or incomplete data was presented in Table S1. There was a significant difference between participants and non-participants in age, sex, education, income, smoking, drinking, medical history, and laboratory indexes. Baseline characteristics of participants according to quartiles of changes in TyG index are presented in Table 1 . Compared with participants in the Q1 group, participants in other groups were more likely to be older, men, less educated, had lower income, more current smokers and drinkers, a higher prevalence of hypertension, diabetes, and dyslipidemia, more likely to table antihypertensive agents and antidiabetic agents, had a high BMI, SBP, DBP, TC, LDL-C, and hs-CRP level, and a lower HDL-C level. Table 1 Baseline characteristics of participants according to quartiles of changes in TyG index from 2006 to 2010. Characteristics Overall Quartiles of changes in TyG index P value Q1 (<-0.31) Q2 (-0.31-0.05) Q3 (0.05–0.41) Q4 (≥ 0.41) No. of participants 62443 15610 15611 15611 15611 Age, years 49.07 ± 11.84 47.35 ± 11.77 48.82 ± 11.98 49.96 ± 11.95 50.15 ± 11.46 < 0.0001 Men, n (%) 47827 (76.59) 12059 (77.25) 11562 (74.06) 11806 (75.63) 12400 (79.44) < 0.0001 High school or above, n (%) 13614 (22.56) 3755 (25.09) 3649 (24.17) 3322 (21.98) 2888 (19.02) 800 RMB/month, n (%) 8878 (14.72) 2385 (15.95) 2410 (15.98) 2090 (13.84) 1993 (13.14) < 0.0001 Body mass index, kg/m 2 25.03 ± 3.46 24.84 ± 3.44 24.86 ± 3.47 25.01 ± 3.46 25.40 ± 3.46 < 0.0001 Systolic blood pressure, mm Hg 128.36 ± 19.81 126.14 ± 19.20 126.98 ± 19.40 128.71 ± 19.95 131.60 ± 20.24 < 0.0001 Diastolic blood pressure, mm Hg 82.63 ± 11.41 81.42 ± 11.23 81.86 ± 11.16 82.78 ± 11.35 84.44 ± 11.65 < 0.0001 Current smoker, n (%) 20552 (33.81) 5082 (33.28) 4739 (31.10) 4975 (32.67) 5756 (38.25) < 0.0001 Current alcohol use, n (%) 23413 (38.51) 5665 (37.06) 5429 (35.62) 5816 (38.19) 6503 (43.22) < 0.0001 Active physical activity, n (%) 55080 (91.46) 13387 (89.74) 13850 (91.92) 13922 (92.31) 13921 (91.85) < 0.0001 Hypertension, n (%) 6035 (9.66) 1416 (9.07) 1485 (9.51) 1470 (9.42) 1664 (10.66) < 0.0001 Diabetes Mellitus, n (%) 1489 (2.38) 339 (2.17) 288 (1.85) 337 (2.16) 525 (3.36) < 0.0001 Dyslipidemia, n (%) 3183 (5.10) 737 (4.72) 833 (5.34) 770 (4.93) 843 (5.40) 0.0166 Antihypertensive agents, n (%) 5185 (8.30) 1209 (7.75) 1273 (8.16) 1282 (8.21) 1421 (9.10) 0.0002 Antidiabetic agents, n (%) 1144 (1.83) 269 (1.72) 222 (1.42) 252 (1.61) 401 (2.57) < 0.0001 Lipid-lowering agents, n (%) 465 (0.74) 102 (0.65) 134 (0.86) 108 (0.69) 121 (0.77) 0.1526 Total cholesterol, mmol/L 4.91 ± 1.13 4.90 ± 1.04 4.94 ± 1.02 4.97 ± 1.02 4.84 ± 1.40 < 0.0001 HDL cholesterol, mmol/L 1.56 ± 0.39 1.58 ± 0.40 1.56 ± 0.39 1.55 ± 0.39 1.53 ± 0.40 < 0.0001 LDL cholesterol, mmol/L 2.29 ± 0.89 2.25 ± 0.94 2.28 ± 0.86 2.31 ± 0.87 2.32 ± 0.89 < 0.0001 Hs-CRP,mg/dL 2.29 ± 6.37 2.14 ± 4.96 2.21 ± 5.37 2.23 ± 6.94 2.60 ± 7.79 < 0.0001 Abbreviations: LDL, low-density lipoprotein; HDL, high-density lipoprotein; hs-CRP, high-sensitivity C-reactive protein; TyG, triglyceride glucose. Association of changes in TyG index with CVD and its subtypes During a median follow-up of 7.01 years (interquartile range: 6.64–7.31 years), 2,530 (4.05%) incident CVD were identified, of which 2,018 (3.23%) were incident stroke and 545 (0.87%) were incident MI. The incidence of CVD increased substantially with increasing changes in TyG index quartiles, reaching an incidence of 6.73 (95% CI, 6.25–7.24) per 1,000 person-years. The cumulative risk of CVD increased over time by changes in TyG index quartile (Fig. 1 A) and remained significant even after adjustment for potential confounding factors ( P for trend < 0.001), the fully adjusted HR (model 2) was 1.18 (95% CI, 1.06–1.32), 1.26 (95% CI, 1.12–1.42), and 1.42 (95% CI, 1.26–1.60) for Q2, Q3, and Q4 groups versus Q1 group of changes in TyG index (Table 2 ). Moreover, there was a linear association between changes in TyG index and risk of CVD, per 1 SD increase in changes in TyG was associated with 16% higher risk of CVD (HR, 1.16; 95% CI, 1.11–1.21; Fig. 2 A). In the subtype analyses of CVD, similar results were yield for stroke and MI, with HR increased across increasing changes in TyG quartiles (Table 2 , Fig. 1 B-C, and Fig. 2 B-C). Table 2 HR and 95% CI for the association between changes in TyG index from 2006 to 2010 and cardiovascular diseases and its subtypes Quartiles of changes in TyG index Q1 (<-0.31) Q2 (-0.31-0.05) Q3 (0.05–0.41) Q4 (≥ 0.41) P for trend CVD Case, n (%) 585(3.75) 594(3.81) 646(4.14) 705(4.52) Incidence rate, per 1000 person-y 5.54(5.11–6.01) 5.65(5.21–6.12) 6.17(5.71–6.66) 6.73(6.25–7.24) Model 1 Reference 1.18(1.06–1.32) 1.26(1.12–1.42) 1.42(1.26–1.60) < 0.0001 Model 2 Reference 1.17(1.04–1.30) 1.24(1.11–1.40) 1.37(1.21–1.54) < 0.0001 Stroke Case, n (%) 465(2.98) 473(3.03) 528(3.38) 552(3.54) Incidence rate, per 1000 person-y 4.39(4.01–4.80) 4.48(4.10–4.91) 5.03(4.62–5.47) 5.24(4.82–5.70) Model 1 Reference 1.24(1.08–1.41) 1.27(1.11–1.47) 1.44(1.24–1.66) < 0.0001 Model 2 Reference 1.22(1.07–1.40) 1.26(1.09–1.45) 1.38(1.19–1.60) < 0.0001 MI Case, n (%) 126(0.81) 126(0.81) 128(0.82) 165(1.06) Incidence rate, per 1000 person-y 1.18(0.99–1.40) 1.19(1.00-1.41) 1.21(1.02–1.44) 1.55(1.33–1.81) Model 1 Reference 1.05(0.82–1.34) 1.22(0.95–1.57) 1.41(1.09–1.82) 0.0050 Model 2 Reference 1.02(0.80–1.30) 1.19(0.93–1.53) 1.36(1.05–1.76) 0.0115 Abbreviations: CI, confidence interval; CVD, cardiovascular disease; MI, mypcardial infarction; HR, hazard ratio; TyG index, triglyceride-glucose index. Model 1: adjusted for age, sex, and TyG index at baseline. Model 2: further adjusted for education, income, smoking status, drinking status, physical activity, body mass index, systolic blood pressure, diastolic blood pressure, a history of hypertension, diabetes mellitus, and dyslipidemia, antidiabetic agents, lipid-lowering agents, antihypertensive agents, high-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and high-sensitivity C-reactive protein at baseline. The sensitivity analyses with competing risk model (Fig. 3 A), excluding participants with abnormal FBG or TG level at baseline (n = 21,901, Fig. 3 B), and excluding the outcome events occurred within the first 2 years of the follow-up period (n = 1,162, Fig. 3 C), all generated similar findings with the primary analysis. Subgroup analyses Results of subgroup analyses are presented in Table S2. The association of changes in TyG index with the risk of CVD and its subtypes were consistent across difference subgroups, including age (< 60 and ≥ 60 years), sex (women and men), BMI (< 25 and ≥ 25 kg/m 2 ), and FBG ( 0.05 for all). Incremental predictive value of changes in TyG index We evaluated whether changes in TyG index would further increase the predictive value of conventional risk (Table 3 ). The C statistics by the conventional model significantly improve with the addition of change in TyG index (from 0.739 to 0.742, P = 0.0097), the discriminatory power and risk reclassification also appeared to be substantially better, with the IDI of 0.09% (95%CI, 0.05–0.13; P < 0.0001), and the NRI of 12.58% (95% CI, 8.61–16.56; P < 0.0001). Similar results were observed when stroke and MI was the outcome of interest. Table 3 Reclassification and discrimination statistics for baseline and changes in TyG index C statistics IDI Category-free NRI Estimate (95% CI) P Estimate (95% CI), % P Estimate (95% CI), % P CVD Conventional model * 0.739(0.731–0.748) Reference Reference Conventional model +changes in TyG index 0.742(0.733–0.751) 0.0097 0.09(0.05–0.13) < 0.0001 12.58(8.61–16.56) < 0.0001 Stroke Conventional model * 0.740(0.730–0.750) Reference Reference Conventional model +changes in TyG index 0.742(0.732–0.752) 0.0435 0.06(0.02–0.09) 0.0010 10.83(6.40-15.26) < 0.0001 MI Conventional model * 0.749(0.731–0.766) Reference Reference Conventional model +changes in TyG index 0.752(0.735–0.770) 0.0412 0.03(0.01–0.05) 0.0365 16.69(8.26–25.12) 0.0001 Abbreviations: CVD, cardiovascular disease; IDI, integrated discrimination improvement; MI, myocardial infarction; NRI, net reclassification index; TyG index, triglyceride-glucose index. *Conventional model was adjusted for age, sex, TyG index, education, income, smoking status, drinking status, physical activity, body mass index, systolic blood pressure, diastolic blood pressure, a history of hypertension, diabetes mellitus, and dyslipidemia, antidiabetic agents, lipid-lowering agents, antihypertensive agents, high-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and high-sensitivity C-reactive protein at baseline. Discussion In this prospective cohort study, we found that substantial changes in TyG index was significant associated with the risk of CVD. Notably, the risk of CVD increased with elevated TyG index over time. Similar patterns were observed for stroke and MI. The trend remained robust among multiple sensitivity analyses and the stratified analyses. Furthermore, the addition of changes in TyG index to the baseline risk model including traditional risk factors significantly promoted the ability of risk stratification. The present analyses showed that participants with elevated TyG index over time had a higher risk of developing CVD relative to their counterparts with decreased TyG index over time. Previous studies, which were generally based on a single TyG index assessment, generated the consistent results regarding the association between baseline TyG index and CVD risk. The Vascular Metabolic CUN cohort with 5,014 subjects found that a higher level of TyG index was significantly associated with an increased risk of developing CVD in Caucasian population, participants in the highest quintile group had 2.32-fold higher risk of CVD than the lowest quintile group.[ 12 ] Another retrospective cohort analysis of 6,078 participants aged over 60 years showed the fourth quarter of TyG index was associated with 72% higher risk of CVD events.[ 13 ] The Tehran Lipid and Glucose Study with 7,521 Iranians revealed that the significant relationship between the TyG index and risk of CVD/coronary heart disease was more prominent among the younger population.[ 14 ] Similarly, data from the National Health Information Database showed that participants in the highest TyG index quartile were at higher risk for stroke and MI, independently of other traditional cardiovascular risk factors.[ 15 ] Of note, the TyG index is calculated based on TG and FBG, both of which vary over time, thus evaluating the TyG index only at baseline was unable to examine the longitudinal association between the dynamic changes in TyG index and CVD risk. Moreover, one single measurement of the TyG index was also subject to potential regression dilution bias and reverse causation issue.[ 27 ] To address these knowledge gaps and methodological limitations, the concept of assessing the effect of change in TyG index on clinical outcomes has been proposed. In a rural Chinese cohort study, the author used the difference in TyG index between follow-up and baseline to predict the risk of type 2 diabetes, the results showed that risk of incident diabetes was increased with quartiles of TyG difference in normal-weight people.[ 28 ] While the relationship between changes in TyG index and CVD has not been established by previous studies. In line with above-mentioned study, our study found that risk of CVD was increased with quartiles of changes in TyG index, suggesting that participants with a huge increase in TyG index may warrant particular vigilance and should be followed up closely in case of the development of CVD . Another important finding of our study was that the addition of changes in TyG to the conventional risk model had an incremental effect on the predictive value for CVD. The predictive role of baseline TyG index for CVD has been confirmed by previous studies. Data from the Kaohsiung Medical University Hospital showed that the TyG index was a useful parameter and a stronger predictive factor than hemoglobin A 1c for events and may offer an additional prognostic benefit in patients with type 2 diabetes.[ 16 ] The Vascular Metabolic CUN cohort showed that the areas under the curve of receiver-operating characteristics curve increased from 0.708 to 0.719 by adding TyG index to the Framingham model.[ 12 ] Our findings showed that a longitudinal changes in TyG index predicts a high risk of CVD that is beyond the models including baseline TyG index, highlighting the importance of monitoring longitudinal patterns of changes in TyG index in clinical practice. The potential mechanism underlying the association of changes in TyG index with development and progression of CVD remains uncertain, several theories have been proposed. First, study have shown that FBG mainly reflects IR from liver, whereas fasting TGs mainly reflects IR from adipose cell.[ 29 ] Therefore, it can be concluded that elevated TyG index over time may reflect a severe insulin resistance from two aspects. Insulin resistance can play a critical role in the formation of atherosclerotic plaques by leading to chronic inflammation, oxidative stress, endothelium dysfunction, and the facilitated the formation of foam cells, and by changing the gene expression pattern associated with estrogen receptor, as reported in animal models.[ 30 – 33 ] Second, in our study, participants with substantial changes in TyG index tended to combine with more severe and complex clinical conditions in terms of BMI, blood pressure, lipid profiles, hypertension, diabetes, and dyslipidemia, which were a cluster of risk factors of CVD.[ 34 , 35 ] Changes in TyG index could modify and influence the role of cardiovascular risk factors and contribute to the progression of CVD. Third, it has been demonstrated that the TyG index was related to artery stiffness evaluated by pulse wave velocity, ankle–brachial index, and carotid intima–media thickness through affecting platelet adhesion, activation and aggregation[ 36 , 37 ], elevating TyG index over time may accelerate the development of artery stiffness thus may lead to the development of CVD. The strengths of the study include its prospective design, large community-based sample, long follow-up period, and consider the effect of changes in TyG index on CVD and its subtypes in the general population. The results should be interpreted in the context of some limitations. First, due to the shortage of records insulin, we could not compare the predictive value of TyG index with homeostasis model assessment insulin resistance (HOMA-IR) and the hyperinsulinaemic euglycaemic clamp test for occurrence of CVD. Second, the distribution of gender is unbalanced due to a large proportion of participants were coal miners, however, the association of changes in TyG index and CVD and subtypes were statistically robust, as the results did not show significant interaction when stratified by gender. Third, owing to the observational nature of the study, we could not establish a causal association between TyG index and the risk of CVD and our findings need to be confirmed in future studies. Finally, although potential cardiac risk factor were adjusted for, we still cannot exclude the possibility of residual or unmeasured confounding given the observational study design of the present analysis. Conclusions In conclusion, we found that changes in TyG index was an independent predictor of CVD and its subtypes. Elevated TyG index over time was associated with higher risk of CVD, stroke and MI. Our findings emphasize the importance of monitoring the longitudinal changes in TyG index for identifying individuals at high risk of development CVD. Abbreviations BMI=body mass index; CI=confidence interval; CVD=cardiovascular disease; DBP=diastolic blood pressure; FBG=fasting blood glucose; HDL-C=high-density lipoprotein cholesterol; HOMA-IR=homeostasis model assessment insulin resistance; HR=hazard ratio; hs-CRP=high-sensitivity C-reactive protein; IDI=integrated discrimination improvement; LDL-C=low-density lipoprotein cholesterol; NRI=net reclassification index; SBP=systolic blood pressure; SD=standard deviation; TG=triglyceride; TyG=triglyceride-glucose Declarations Ethics approval and consent to participate The study was performed according to the guidelines of the Helsinki Declaration and was approved by the Ethics Committee of Kailuan General Hospital (approval number: 2006-05) and Beijing Tiantan Hospital (approval number: 2010-014-01). All participants were agreed to take part in the study and provided informed written consent. Consent for publication Not applicable Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request Competing interests These authors declare that they have no conflicts of interests. Funding This work was supported by grants from National Key R&D Program of China (2016YFC0901002, 2017YFC1310901, 2018YFC1312903), Beijing Municipal Science & Technology Commission (D171100003017002), National Science and Technology Major Project (2017ZX09304018), Beijing Municipal Administration of Hospitals Incubating Program (PX2020021), Beijing Excellent Talents Training Program (2018000021469G234), and Young Elite Scientists Sponsorship Program by CAST (2018QNRC001). Author Contributions S.W. and Y.W. contributed to the conception and design of the study; A.W. and X.T. contributed to manuscript drafting; A.W., X.T., Y.Z. and S.C. contributed to the statistics analysis; S.C. and X.M. contributed to the acquisition of data; S.W., Y.W. and A.W. contributed to critical revisions of the manuscript. All authors read and approved the final manuscript. Acknowledgments We thank all study participants, their relatives, the members of the survey teams at the 11 regional hospitals of the Kailuan Medical Group; and the project development and management teams at the Beijing Tiantan Hospital and the Kailuan Group. References Ormazabal V, Nair S, Elfeky O, Aguayo C, Salomon C, Zuñiga F: Association between insulin resistance and the development of cardiovascular disease. Cardiovascular diabetology 2018, 17(1):122. Bersch-Ferreira Â, Sampaio G, Gehringer M, Torres E, Ross-Fernandes M, da Silva J, Torreglosa C, Kovacs C, Alves R, Magnoni C et al : Association between plasma fatty acids and inflammatory markers in patients with and without insulin resistance and in secondary prevention of cardiovascular disease, a cross-sectional study. Nutrition journal 2018, 17(1):26. Xun P, Wu Y, He Q, He K: Fasting insulin concentrations and incidence of hypertension, stroke, and coronary heart disease: a meta-analysis of prospective cohort studies. The American journal of clinical nutrition 2013, 98(6):1543-1554. van der Schaft N, Schoufour J, Nano J, Kiefte-de Jong J, Muka T, Sijbrands E, Ikram M, Franco O, Voortman T: Dietary antioxidant capacity and risk of type 2 diabetes mellitus, prediabetes and insulin resistance: the Rotterdam Study. European journal of epidemiology 2019, 34(9):853-861. Han T, Lan L, Qu R, Xu Q, Jiang R, Na L, Sun C: Temporal Relationship Between Hyperuricemia and Insulin Resistance and Its Impact on Future Risk of Hypertension. Hypertension (Dallas, Tex : 1979) 2017, 70(4):703-711. Huang-Doran I, Tomlinson P, Payne F, Gast A, Sleigh A, Bottomley W, Harris J, Daly A, Rocha N, Rudge S et al : Insulin resistance uncoupled from dyslipidemia due to C-terminal PIK3R1 mutations. JCI insight 2016, 1(17):e88766. Gangel M, Dollar J, Brown A, Keane S, Calkins S, Shanahan L, Wideman L: Childhood social preference and adolescent insulin resistance: Accounting for the indirect effects of obesity. Psychoneuroendocrinology 2020, 113:104557. Cersosimo E, Solis-Herrera C, Trautmann M, Malloy J, Triplitt C: Assessment of pancreatic β-cell function: review of methods and clinical applications. Current diabetes reviews 2014, 10(1):2-42. Khan S, Sobia F, Niazi N, Manzoor S, Fazal N, Ahmad F: Metabolic clustering of risk factors: evaluation of Triglyceride-glucose index (TyG index) for evaluation of insulin resistance. Diabetology & metabolic syndrome 2018, 10:74. Mazidi M, Kengne A, Katsiki N, Mikhailidis D, Banach M: Lipid accumulation product and triglycerides/glucose index are useful predictors of insulin resistance. Journal of diabetes and its complications 2018, 32(3):266-270. Irace C, Carallo C, Scavelli FB, De Franceschi MS, Esposito T, Tripolino C, Gnasso A: Markers of insulin resistance and carotid atherosclerosis. A comparison of the homeostasis model assessment and triglyceride glucose index. International journal of clinical practice 2013, 67(7):665-672. Sánchez-Íñigo L, Navarro-González D, Fernández-Montero A, Pastrana-Delgado J, Martínez J: The TyG index may predict the development of cardiovascular events. European journal of clinical investigation 2016, 46(2):189-197. Li S, Guo B, Chen H, Shi Z, Li Y, Tian Q, Shi S: The role of the triglyceride (triacylglycerol) glucose index in the development of cardiovascular events: a retrospective cohort analysis. Scientific reports 2019, 9(1):7320. Barzegar N, Tohidi M, Hasheminia M, Azizi F, Hadaegh F: The impact of triglyceride-glucose index on incident cardiovascular events during 16 years of follow-up: Tehran Lipid and Glucose Study. Cardiovascular diabetology 2020, 19(1):155. Hong S, Han K, Park C: The triglyceride glucose index is a simple and low-cost marker associated with atherosclerotic cardiovascular disease: a population-based study. BMC medicine 2020, 18(1):361. Su W, Chen S, Huang Y, Huang J, Wu P, Hsu W, Lee M: Comparison of the Effects of Fasting Glucose, Hemoglobin A, and Triglyceride-Glucose Index on Cardiovascular Events in Type 2 Diabetes Mellitus. Nutrients 2019, 11(11). Jin C, Chen S, Vaidya A, Wu Y, Wu Z, Hu FB, Kris-Etherton P, Wu S, Gao X: Longitudinal Change in Fasting Blood Glucose and Myocardial Infarction Risk in a Population Without Diabetes. Diabetes care 2017, 40(11):1565-1572. Wang A, Sun Y, Liu X, Su Z, Li J, Luo Y, Chen S, Wang J, Li X, Zhao Z et al : Changes in proteinuria and the risk of myocardial infarction in people with diabetes or pre-diabetes: a prospective cohort study. Cardiovascular diabetology 2017, 16(1):104. Wu S, An S, Li W, Lichtenstein AH, Gao J, Kris-Etherton PM, Wu Y, Jin C, Huang S, Hu FB et al : Association of Trajectory of Cardiovascular Health Score and Incident Cardiovascular Disease. JAMA network open 2019, 2(5):e194758. Tian X, Zuo Y, Chen S, Liu Q, Tao B, Wu S, Wang A: Triglyceride-glucose index is associated with the risk of myocardial infarction: an 11-year prospective study in the Kailuan cohort. Cardiovascular diabetology 2021, 20(1):19. Wang A, Wang G, Liu Q, Zuo Y, Chen S, Tao B, Tian X, Wang P, Meng X, Wu S et al : Triglyceride-glucose index and the risk of stroke and its subtypes in the general population: an 11-year follow-up. Cardiovascular diabetology 2021, 20(1):46. Wang C, Yuan Y, Zheng M, Pan A, Wang M, Zhao M, Li Y, Yao S, Chen S, Wu S et al : Association of Age of Onset of Hypertension With Cardiovascular Diseases and Mortality. Journal of the American College of Cardiology 2020, 75(23):2921-2930. Wu S, Song Y, Chen S, Zheng M, Ma Y, Cui L, Jonas J: Blood Pressure Classification of 2017 Associated With Cardiovascular Disease and Mortality in Young Chinese Adults. Hypertension (Dallas, Tex : 1979) 2020, 76(1):251-258. Stroke--1989. Recommendations on stroke prevention, diagnosis, and therapy. Report of the WHO Task Force on Stroke and other Cerebrovascular Disorders. Stroke 1989, 20(10):1407-1431. Tunstall-Pedoe H, Kuulasmaa K, Amouyel P, Arveiler D, Rajakangas A, Pajak A: Myocardial infarction and coronary deaths in the World Health Organization MONICA Project. Registration procedures, event rates, and case-fatality rates in 38 populations from 21 countries in four continents. Circulation 1994, 90(1):583-612. Desquilbet L, Mariotti F: Dose-response analyses using restricted cubic spline functions in public health research. Statistics in medicine 2010, 29(9):1037-1057. VanderWeele T, Jackson J, Li S: Causal inference and longitudinal data: a case study of religion and mental health. Social psychiatry and psychiatric epidemiology 2016, 51(11):1457-1466. Zhang M, Wang B, Liu Y, Sun X, Luo X, Wang C, Li L, Zhang L, Ren Y, Zhao Y et al : Cumulative increased risk of incident type 2 diabetes mellitus with increasing triglyceride glucose index in normal-weight people: The Rural Chinese Cohort Study. Cardiovasc Diabetol 2017, 16(1):30. Low S, Khoo K, Irwan B, Sum C, Subramaniam T, Lim S, Wong T: The role of triglyceride glucose index in development of Type 2 diabetes mellitus. Diabetes research and clinical practice 2018, 143:43-49. Matulewicz N, Karczewska-Kupczewska M: Insulin resistance and chronic inflammation. Postepy higieny i medycyny doswiadczalnej (Online) 2016, 70(0):1245-1258. Janus A, Szahidewicz-Krupska E, Mazur G, Doroszko A: Insulin Resistance and Endothelial Dysfunction Constitute a Common Therapeutic Target in Cardiometabolic Disorders. Mediators of inflammation 2016, 2016:3634948. Oh J, Riek AE, Darwech I, Funai K, Shao J, Chin K, Sierra OL, Carmeliet G, Ostlund RE, Jr., Bernal-Mizrachi C: Deletion of macrophage Vitamin D receptor promotes insulin resistance and monocyte cholesterol transport to accelerate atherosclerosis in mice. Cell reports 2015, 10(11):1872-1886. Min J, Weitian Z, Peng C, Yan P, Bo Z, Yan W, Yun B, Xukai W: Correlation between insulin-induced estrogen receptor methylation and atherosclerosis. Cardiovasc Diabetol 2016, 15(1):156. Francula-Zaninovic S, Nola IA: Management of Measurable Variable Cardiovascular Disease' Risk Factors. Current cardiology reviews 2018, 14(3):153-163. Cortesi P, Fornari C, Madotto F, Conti S, Naghavi M, Bikbov B, Briant P, Caso V, Crotti G, Johnson C et al : Trends in cardiovascular diseases burden and vascular risk factors in Italy: The Global Burden of Disease study 1990-2017. European journal of preventive cardiology 2020:2047487320949414. Zhao S, Yu S, Chi C, Fan X, Tang J, Ji H, Teliewubai J, Zhang Y, Xu Y: Association between macro- and microvascular damage and the triglyceride glucose index in community-dwelling elderly individuals: the Northern Shanghai Study. Cardiovasc Diabetol 2019, 18(1):95. Won K, Park G, Lee S, Cho I, Kim H, Lee B, Chang H: Relationship of insulin resistance estimated by triglyceride glucose index to arterial stiffness. Lipids in health and disease 2018, 17(1):268. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major Revision 02 May, 2021 Review # 1 received at journal 20 Apr, 2021 Reviewer # 3 agreed at journal 05 Apr, 2021 Review # 3 received at journal 05 Apr, 2021 Review # 2 received at journal 05 Apr, 2021 Reviews received at journal 02 Apr, 2021 Reviewer # 2 agreed at journal 02 Apr, 2021 Reviewer # 1 agreed at journal 31 Mar, 2021 Reviewers invited by journal 29 Mar, 2021 First submitted to journal 28 Mar, 2021 Editor assigned by journal 28 Mar, 2021 Submission checks completed at journal 28 Mar, 2021 Editor invited by journal 28 Mar, 2021 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-375689","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Original investigation","associatedPublications":[],"authors":[{"id":19228815,"identity":"29c94d98-5492-4940-9da1-b3489b5f6e62","order_by":0,"name":"Anxin Wang","email":"","orcid":"","institution":"Beijing Tiantan Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Anxin","middleName":"","lastName":"Wang","suffix":""},{"id":19228816,"identity":"3fc2d616-c27f-4154-a07e-53110f582720","order_by":1,"name":"Xue Tian","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Tian","suffix":""},{"id":19228817,"identity":"26e83185-f779-4ef1-abcb-6069a9e6a24f","order_by":2,"name":"Yingting Zuo","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yingting","middleName":"","lastName":"Zuo","suffix":""},{"id":19228818,"identity":"4097f0dc-26d3-4f46-8aaa-2eab6b50a189","order_by":3,"name":"Shuohua Chen","email":"","orcid":"","institution":"Kailuan General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shuohua","middleName":"","lastName":"Chen","suffix":""},{"id":19228819,"identity":"0b08f6c2-5367-4c54-9f3b-e88779eae778","order_by":4,"name":"Xia Meng","email":"","orcid":"","institution":"Beijing Tiantan Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xia","middleName":"","lastName":"Meng","suffix":""},{"id":19228820,"identity":"1e0d52b7-731e-4618-979c-d136ef96043b","order_by":5,"name":"Shouling Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAo0lEQVRIiWNgGAWjYDCCAyCiwIaHn7+BJC0GaTKSMw6QpuWwjUFDApE6+G6fMZP8YXCex4DhAOOHjzlEaJE8l2MmzWNwm8ecuYFZcuY2IrQYnOHdJs0A1GLZcICNmZdYLUCHneMxOJBAghYJoHoStEie4f9szWOQzCM542AzcX7hO8OWePNHhZ09P3/zwQ8fidGCBBgbSFM/CkbBKBgFowA3AACDkzL8EhRBcQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-7095-6022","institution":"Kailuan General Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Shouling","middleName":"","lastName":"Wu","suffix":""},{"id":19228821,"identity":"b5e66fa1-8029-439b-bf41-ebb9f0e207f5","order_by":6,"name":"Yongjun Wang","email":"","orcid":"","institution":"Beijing Tiantan Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yongjun","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2021-03-30 07:15:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-375689/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-375689/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":7588428,"identity":"54c3d427-1527-4550-9ab3-5d0b855d7f20","added_by":"auto","created_at":"2021-04-01 21:00:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":172554,"visible":true,"origin":"","legend":"Kaplan-Meier estimation of (A) cardiovascular diseases (B) stroke (C) myocardial infarction by quartiles of changes in TyG index.\nAbbreviation: TyG, triglyceride-glucose","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-375689/v1/f0dcdabf82fb542e8ddae310.png"},{"id":7587862,"identity":"e20d9805-5bfe-4d01-8e39-85b3dca1fa8f","added_by":"auto","created_at":"2021-04-01 20:57:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":77125,"visible":true,"origin":"","legend":"Multivariable-adjusted hazard ratios for (A) cardiovascular diseases (B) stroke (C) myocardial infarction based on restricted cubic spines with 5 knots at 5th, 25th, 50th, 75th, and 95th percentiles of changes in TyG index.\nAbbreviation: HR, hazard ratio; SD, standard deviation; TyG, triglyceride-glucose \nRed line represent references for hazard ratios, and red area represent 95% confidence interval.\nModel was adjusted for age, sex, TyG index, education, income, smoking status, drinking status, physical activity, body mass index, systolic blood pressure, diastolic blood pressure, a history of hypertension, diabetes mellitus, and dyslipidemia, antidiabetic agents, lipid-lowering agents, antihypertensive agents, high-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and high-sensitivity C-reactive protein at baseline.","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-375689/v1/9e1b5964702ea5f08ed9eb31.png"},{"id":7587864,"identity":"e8d269db-f2e4-451f-8821-c7b05d80eaa7","added_by":"auto","created_at":"2021-04-01 20:57:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":69991,"visible":true,"origin":"","legend":"Sensitivity analyses for the association of changes in TyG index from 2006 to 2010 with cardiovascular disease and its subtypes\nAbbreviation: CVD, cardiovascular disease; MI, myocardial infarction; TyG index, triglyceride-glucose index.\nModel was adjusted for age, sex, TyG index, education, income, smoking status, drinking status, physical activity, body mass index, systolic blood pressure, diastolic blood pressure, a history of hypertension, diabetes mellitus, and dyslipidemia, antidiabetic agents, lipid-lowering agents, antihypertensive agents, high-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and high-sensitivity C-reactive protein at baseline.\nA. Taking non-CVD related death as competing risk event rather than censoring.\nB. Restricted analysis was excluded those with abnormal FBG (≥7.0 mmol/L) or abnormal TG level (≥1.7 mmol/L) at baseline (n=21,901).\nC. Excluded person time and stroke events from the first 2 years of follow-up (n=1162).","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-375689/v1/ccc3bd7b3044241ff90e2999.png"},{"id":13683587,"identity":"02dd7f10-72f3-4d3e-b8e0-9f8237e58b9c","added_by":"auto","created_at":"2021-09-17 12:03:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":606246,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-375689/v1/c7f898f9-f832-4c2d-9274-7a0632fc838d.pdf"},{"id":7587865,"identity":"01c6d368-1716-4407-825d-8dc1b98edded","added_by":"auto","created_at":"2021-04-01 20:57:42","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":132979,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-375689/v1/4bf3d08915919f1b243fb47b.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eChanges in Triglyceride-glucose Index Predict the Risk of Cardiovascular Diseases in the General Population: a Prospective Cohort Study\u003c/p\u003e","fulltext":[{"header":"Background","content":" \u003cp\u003eInsulin resistance, the critical mechanism of the pathogenesis of diabetes mellitus, has been extensively demonstrated to be significantly related to be the development of cardiovascular disease (CVD).[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] Insulin resistance has been reported not only to be associated with CVD risk factors such as diabetes mellitus[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], hypertension[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], dyslipidemia[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and obesity[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], but also is an independent risk factor for CVD[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], thus an early detection and control of insulin resistance may contribute to the prevention of CVD. Although the hyperinsulinemic-euglycemic clamp is the gold-standard test for IR assessment, it is not commonly used in clinical settings and large population studies due to the complex testing process and expensive cost.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] In this regard, triglyceride-glucose (TyG) index, a product of triglyceride (TG) and fasting blood glucose (FBG), appears as a simple surrogate for insulin resistance with high correlation with the gold-standard test.[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Cohort studies have found that TyG index was an importance risk factor for incident CVD.[\u003cspan additionalcitationids=\"CR13 CR14 CR15\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] However, an inherent limitation of previous studies is the TyG index was evaluated on a single time point, there has been no consideration of how the TyG index varies within individuals over time and the subsequent effect, which may yield a biased estimate of the relationship of the TyG index and CVD risk. While the effect of longitudinal changes in TyG index over time on CVD has not been fully studied up to date.\u003c/p\u003e \u003cp\u003eWe therefore conducted the present study to identify the potential association of changes in TyG index with CVD and its subtypes based on a large community-based prospective cohort study.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Kailuan study is a prospective cohort study in the Kailuan community in Tangshan, China. The detailed study design and procedures have been described previously.[17-19] During June 2006 to October 2007, a total of 101,510 participants (81 110 men and 20 400 women; aged 18 to 98 yeas) were enrolled in the first survey (baseline) and underwent a comprehensive biennial health examination. All participants were followed up until their death or December 31, 2017. Changes in TyG index was developed from 2006 to 2010 to predict CVD risk from 2010 to 2017 (Figure S1). We excluded 3,669 and 2,042 participants with MI or stroke in or prior 2010, 30,971 participants who did not finish the survey at 2010, 1,282 and 1,103 participants with missing data on FBG or TG at baseline or the survey at 2010. Ultimately, a total of 62,443 participants were enrolled in the present study (Figure S2). The study was performed according to the guidelines of the Helsinki Declaration and was approved by the Ethics Committee of Kailuan General Hospital (approval number: 2006-05) and Beijing Tiantan Hospital (approval number: 2010-014-01). All participants were agreed to take part in the study and provided written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection and definitions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformation on demographic characteristics, lifestyle factors (smoking status, drinking status, and physical activity), and medical history were collected via standardized questionnaire by trained staffs. Education was classified as illiteracy or primary school, middle school, and high school or above. Income was categorized into \u0026gt; 800 and \u0026le; 800 yuan/month. Smoking and drinking status were stratified into never, former or current. Physically active was classified as \u0026ge;4 times per week and \u0026ge;20 minutes at a time, \u0026lt;80 minutes per week, or none. Body mass index (BMI) was calculated by dividing body weight (kg) by the square of height (m\u003csup\u003e2\u003c/sup\u003e). Blood pressure was measured in the in the seated position using a mercury sphygmomanometer, the average of 3 readings were calculated as systolic blood pressure (SBP) and diastolic blood pressure (DBP). All the blood samples were analyzed using an auto-analyzer (Hitachi 747, Hitachi, Tokyo, Japan) on the day of the blood draw. The biochemical indicators tested included fasting blood glucose, serum lipids, serum creatinine, and high-sensitivity C-reactive protein (hs-CRP).\u003c/p\u003e\n\u003cp\u003eHypertension was defined as SBP \u0026ge;140 mm Hg or DBP \u0026ge;90 mm Hg, any use of the antihypertensive drug, or self-reported history of hypertension. Diabetes was defined as FBG\u0026ge;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 TC \u0026ge; 5.17 mmol/L.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCalculation of changes in TyG index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe TyG index was calculated as ln (fasting TG [mg/dl] \u0026times; FBG [mg/dl]/2) as previous done.[20, 21] Changes in TyG index was calculated as TyG index value at 2010 minus that at baseline (2006).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe outcome in the present study was the first occurrence of CVD events. The types of CVD included stroke and MI. We defined CVD events as described previously.[17, 22, 23] The database of CVD diagnoses was obtained from the Municipal Social Insurance Institution and Hospital Discharge Register and was updated annually during the follow-up period. An expert panel collected and reviewed annual discharge records from 11 Kailuan hospitals to identify patients who were suspected of CVD. Incident stroke was diagnosis based on neurological signs, clinical symptoms, and neuroimaging tests, including computed tomography or magnetic resonance, according to the World Health Organization criteria.[24] MI was diagnosed according to the criteria of the World Health Organization on the basis of clinical symptoms, changes in the serum concentrations of cardiac enzymes and biomarkers, and electrocardiographic results.[17, 25]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were divided into four categories according to quartiles of changes in TyG index. The baseline characteristics were presented as mean\u0026plusmn;standard deviation (SD) or frequency with percentage as appropriate. Tests of differences in characteristics across changes in TyG index categories were performed using analysis of variance or the Kruskal-Wallis test for continuous variables according to distribution and chi-square for categorical variables. The person-years were determined from the date when the message was collected at baseline to either the date of MI onset, death, or the date of participating in the last examination in this analysis, whichever came first. Kaplan-Meier methods were performed to evaluate the incidence rate of CVD and its subtypes, and differences among groups were evaluated by log-rank test.\u003c/p\u003e\n\u003cp\u003eCox proportional hazard regression model was applied to calculated hazard ratio (HR) and 95% confidence interval (CI) for CVD and its subtypes. The proportional hazard assumption was evaluated with visualization of Schoenfeld residuals and no potential violation was observed. Two models were constructed. Model 1 was adjusted for age, sex, and TyG index at baseline. Model was additionally adjusted for education, income, smoking status, drinking status, physical activity, BMI, SBP, DBP, a history of hypertension, diabetes mellitus, and dyslipidemia, antidiabetic drugs, lipid-lowering drugs, antihypertensive drugs, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and hs-CRP at baseline. \u003cem\u003eP\u003c/em\u003e-values for trend were computed using quartiles as ordinal variables. To capture the dose-response relationship between changes in TyG index and CVD, restricted cubic splines with four knots at the 5th, 35th, 65th, and 95th percentiles of TyG index change distribution with median of the Q1 group as the reference point.[26]\u003c/p\u003e\n\u003cp\u003eAdditional analyses were performed to validate the robustness of the results. First, competing risk model was applied to assess the association between changes in TyG index and the outcomes considering non-CVD death as a competing risk event. Second, restricted analysis was conducted by excluding participants with abnormal FBG level (\u0026ge;7.0 mmol/L) or abnormal TG level (\u0026ge;1.7 mmol/L) at baseline.[20] Third, to explore the potential impact of reverse causality, we repeated the primary analysis using a 2-year lag period by excluding incident stroke cases from the first 2 years of follow-up. Subgroup analyses were conducted stratified participants by age (\u0026lt; 60 and \u0026ge; 60 years), sex (women and men), BMI (\u0026lt;25 and \u0026ge; 25 kg/m\u003csup\u003e2\u003c/sup\u003e), and FBG (\u0026lt;5.6, 5.6-7.0, and \u0026ge; 7.0 mmol/L) to assess the possible effect modification by these variables, interactions between subgroups were tested using likelihood ratio tests comparing models with and those without multiplicative interaction terms. Additionally, we used C statistics, integrated discrimination improvement (IDI), and net reclassification index (NRI) to evaluate the incremental predictive value of change in TyG index beyond conventional risk factors.\u003c/p\u003e\n\u003cp\u003eAll analyses were performed using SAS version 9.4 (SAS Institute, Cary, North Carolina) and R software version 3.6.1 (R Core Team, Vienna, Austria). All statistical tests were 2-sided, and \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 62,443 eligible participants were included, the mean age was 49.07\u0026thinsp;\u0026plusmn;\u0026thinsp;11.84 years, and 76.59% were men. Comparison of baseline characteristics between and participants and non-participants due to missing the 2010 survey or incomplete data was presented in Table S1. There was a significant difference between participants and non-participants in age, sex, education, income, smoking, drinking, medical history, and laboratory indexes.\u003c/p\u003e\n\u003cp\u003eBaseline characteristics of participants according to quartiles of changes in TyG index are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Compared with participants in the Q1 group, participants in other groups were more likely to be older, men, less educated, had lower income, more current smokers and drinkers, a higher prevalence of hypertension, diabetes, and dyslipidemia, more likely to table antihypertensive agents and antidiabetic agents, had a high BMI, SBP, DBP, TC, LDL-C, and hs-CRP level, and a lower HDL-C level.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eBaseline characteristics of participants according to quartiles of changes in TyG index from 2006 to 2010.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCharacteristics\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eOverall\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eQuartiles of changes in TyG index\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eQ1 (\u0026lt;-0.31)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eQ2 (-0.31-0.05)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eQ3 (0.05\u0026ndash;0.41)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eQ4 (\u0026ge;\u0026thinsp;0.41)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo. of participants\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62443\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15610\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15611\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15611\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15611\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge, years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49.07\u0026thinsp;\u0026plusmn;\u0026thinsp;11.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47.35\u0026thinsp;\u0026plusmn;\u0026thinsp;11.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48.82\u0026thinsp;\u0026plusmn;\u0026thinsp;11.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49.96\u0026thinsp;\u0026plusmn;\u0026thinsp;11.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50.15\u0026thinsp;\u0026plusmn;\u0026thinsp;11.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMen, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47827 (76.59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12059 (77.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11562 (74.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11806 (75.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12400 (79.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh school or above, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13614 (22.56)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3755 (25.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3649 (24.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3322 (21.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2888 (19.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncome\u0026thinsp;\u0026gt;\u0026thinsp;800 RMB/month, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8878 (14.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2385 (15.95)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2410 (15.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2090 (13.84)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1993 (13.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBody mass index, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.03\u0026thinsp;\u0026plusmn;\u0026thinsp;3.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.84\u0026thinsp;\u0026plusmn;\u0026thinsp;3.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.86\u0026thinsp;\u0026plusmn;\u0026thinsp;3.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.01\u0026thinsp;\u0026plusmn;\u0026thinsp;3.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.40\u0026thinsp;\u0026plusmn;\u0026thinsp;3.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSystolic blood pressure, mm Hg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e128.36\u0026thinsp;\u0026plusmn;\u0026thinsp;19.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e126.14\u0026thinsp;\u0026plusmn;\u0026thinsp;19.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e126.98\u0026thinsp;\u0026plusmn;\u0026thinsp;19.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e128.71\u0026thinsp;\u0026plusmn;\u0026thinsp;19.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e131.60\u0026thinsp;\u0026plusmn;\u0026thinsp;20.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiastolic blood pressure, mm Hg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82.63\u0026thinsp;\u0026plusmn;\u0026thinsp;11.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81.42\u0026thinsp;\u0026plusmn;\u0026thinsp;11.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81.86\u0026thinsp;\u0026plusmn;\u0026thinsp;11.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82.78\u0026thinsp;\u0026plusmn;\u0026thinsp;11.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e84.44\u0026thinsp;\u0026plusmn;\u0026thinsp;11.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCurrent smoker, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20552 (33.81)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5082 (33.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4739 (31.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4975 (32.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5756 (38.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCurrent alcohol use, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23413 (38.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5665 (37.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5429 (35.62)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5816 (38.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6503 (43.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eActive physical activity, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e55080 (91.46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13387 (89.74)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13850 (91.92)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13922 (92.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13921 (91.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6035 (9.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1416 (9.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1485 (9.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1470 (9.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1664 (10.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiabetes Mellitus, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1489 (2.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e339 (2.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e288 (1.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e337 (2.16)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e525 (3.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3183 (5.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e737 (4.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e833 (5.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e770 (4.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e843 (5.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0166\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAntihypertensive agents, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5185 (8.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1209 (7.75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1273 (8.16)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1282 (8.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1421 (9.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAntidiabetic agents, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1144 (1.83)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e269 (1.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e222 (1.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e252 (1.61)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e401 (2.57)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLipid-lowering agents, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e465 (0.74)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e102 (0.65)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e134 (0.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e108 (0.69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e121 (0.77)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1526\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal cholesterol, mmol/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.90\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.97\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHDL cholesterol, mmol/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLDL cholesterol, mmol/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHs-CRP,mg/dL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.29\u0026thinsp;\u0026plusmn;\u0026thinsp;6.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.14\u0026thinsp;\u0026plusmn;\u0026thinsp;4.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.21\u0026thinsp;\u0026plusmn;\u0026thinsp;5.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.23\u0026thinsp;\u0026plusmn;\u0026thinsp;6.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.60\u0026thinsp;\u0026plusmn;\u0026thinsp;7.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003eAbbreviations: LDL, low-density lipoprotein; HDL, high-density lipoprotein; hs-CRP, high-sensitivity C-reactive protein; TyG, triglyceride glucose.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation of changes in TyG index with CVD and its subtypes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring a median follow-up of 7.01 years (interquartile range: 6.64\u0026ndash;7.31 years), 2,530 (4.05%) incident CVD were identified, of which 2,018 (3.23%) were incident stroke and 545 (0.87%) were incident MI. The incidence of CVD increased substantially with increasing changes in TyG index quartiles, reaching an incidence of 6.73 (95% CI, 6.25\u0026ndash;7.24) per 1,000 person-years. The cumulative risk of CVD increased over time by changes in TyG index quartile (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA) and remained significant even after adjustment for potential confounding factors (\u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the fully adjusted HR (model 2) was 1.18 (95% CI, 1.06\u0026ndash;1.32), 1.26 (95% CI, 1.12\u0026ndash;1.42), and 1.42 (95% CI, 1.26\u0026ndash;1.60) for Q2, Q3, and Q4 groups versus Q1 group of changes in TyG index (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Moreover, there was a linear association between changes in TyG index and risk of CVD, per 1 SD increase in changes in TyG was associated with 16% higher risk of CVD (HR, 1.16; 95% CI, 1.11\u0026ndash;1.21; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). In the subtype analyses of CVD, similar results were yield for stroke and MI, with HR increased across increasing changes in TyG quartiles (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB-C, and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB-C).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eHR and 95% CI for the association between changes in TyG index from 2006 to 2010 and cardiovascular diseases and its subtypes\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eQuartiles of changes in TyG index\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ1 (\u0026lt;-0.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ2 (-0.31-0.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ3 (0.05\u0026ndash;0.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ4 (\u0026ge;\u0026thinsp;0.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCVD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCase, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e585(3.75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e594(3.81)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e646(4.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e705(4.52)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncidence rate, per 1000 person-y\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.54(5.11\u0026ndash;6.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.65(5.21\u0026ndash;6.12)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.17(5.71\u0026ndash;6.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.73(6.25\u0026ndash;7.24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.18(1.06\u0026ndash;1.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.26(1.12\u0026ndash;1.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.42(1.26\u0026ndash;1.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.17(1.04\u0026ndash;1.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.24(1.11\u0026ndash;1.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.37(1.21\u0026ndash;1.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStroke\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCase, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e465(2.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e473(3.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e528(3.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e552(3.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncidence rate, per 1000 person-y\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.39(4.01\u0026ndash;4.80)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.48(4.10\u0026ndash;4.91)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.03(4.62\u0026ndash;5.47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.24(4.82\u0026ndash;5.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.24(1.08\u0026ndash;1.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.27(1.11\u0026ndash;1.47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.44(1.24\u0026ndash;1.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.22(1.07\u0026ndash;1.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.26(1.09\u0026ndash;1.45)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.38(1.19\u0026ndash;1.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCase, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e126(0.81)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e126(0.81)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e128(0.82)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e165(1.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncidence rate, per 1000 person-y\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.18(0.99\u0026ndash;1.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.19(1.00-1.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.21(1.02\u0026ndash;1.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.55(1.33\u0026ndash;1.81)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.05(0.82\u0026ndash;1.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.22(0.95\u0026ndash;1.57)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.41(1.09\u0026ndash;1.82)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0050\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.02(0.80\u0026ndash;1.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.19(0.93\u0026ndash;1.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.36(1.05\u0026ndash;1.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0115\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eAbbreviations: CI, confidence interval; CVD, cardiovascular disease; MI, mypcardial infarction; HR, hazard ratio; TyG index, triglyceride-glucose index.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eModel 1: adjusted for age, sex, and TyG index at baseline.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eModel 2: further adjusted for education, income, smoking status, drinking status, physical activity, body mass index, systolic blood pressure, diastolic blood pressure, a history of hypertension, diabetes mellitus, and dyslipidemia, antidiabetic agents, lipid-lowering agents, antihypertensive agents, high-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and high-sensitivity C-reactive protein at baseline.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe sensitivity analyses with competing risk model (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA), excluding participants with abnormal FBG or TG level at baseline (n\u0026thinsp;=\u0026thinsp;21,901, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB), and excluding the outcome events occurred within the first 2 years of the follow-up period (n\u0026thinsp;=\u0026thinsp;1,162, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC), all generated similar findings with the primary analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResults of subgroup analyses are presented in Table S2. The association of changes in TyG index with the risk of CVD and its subtypes were consistent across difference subgroups, including age (\u0026lt;\u0026thinsp;60 and \u0026ge;\u0026thinsp;60 years), sex (women and men), BMI (\u0026lt;\u0026thinsp;25 and \u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e), and FBG (\u0026lt;\u0026thinsp;5.6, 5.6-7.0, and \u0026ge;\u0026thinsp;7.0 mmol/L). We did not observe significant interactions between changes in TyG index and stratified variables (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for all).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIncremental predictive value of changes in TyG index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe evaluated whether changes in TyG index would further increase the predictive value of conventional risk (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The C statistics by the conventional model significantly improve with the addition of change in TyG index (from 0.739 to 0.742, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0097), the discriminatory power and risk reclassification also appeared to be substantially better, with the IDI of 0.09% (95%CI, 0.05\u0026ndash;0.13; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and the NRI of 12.58% (95% CI, 8.61\u0026ndash;16.56; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Similar results were observed when stroke and MI was the outcome of interest.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eReclassification and discrimination statistics for baseline and changes in TyG index\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eC statistics\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eIDI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCategory-free NRI\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEstimate\u003c/p\u003e\n\u003cp\u003e(95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEstimate\u003c/p\u003e\n\u003cp\u003e(95% CI), %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEstimate\u003c/p\u003e\n\u003cp\u003e(95% CI), %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCVD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConventional model\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.739(0.731\u0026ndash;0.748)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConventional model\u003c/p\u003e\n\u003cp\u003e+changes in TyG index\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.742(0.733\u0026ndash;0.751)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0097\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.09(0.05\u0026ndash;0.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.58(8.61\u0026ndash;16.56)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStroke\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConventional model\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.740(0.730\u0026ndash;0.750)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConventional model\u003c/p\u003e\n\u003cp\u003e+changes in TyG index\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.742(0.732\u0026ndash;0.752)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0435\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06(0.02\u0026ndash;0.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.83(6.40-15.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConventional model\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.749(0.731\u0026ndash;0.766)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConventional model\u003c/p\u003e\n\u003cp\u003e+changes in TyG index\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.752(0.735\u0026ndash;0.770)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0412\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03(0.01\u0026ndash;0.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0365\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.69(8.26\u0026ndash;25.12)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003eAbbreviations: CVD, cardiovascular disease; IDI, integrated discrimination improvement; MI, myocardial infarction; NRI, net reclassification index; TyG index, triglyceride-glucose index.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003e*Conventional model was adjusted for age, sex, TyG index, education, income, smoking status, drinking status, physical activity, body mass index, systolic blood pressure, diastolic blood pressure, a history of hypertension, diabetes mellitus, and dyslipidemia, antidiabetic agents, lipid-lowering agents, antihypertensive agents, high-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and high-sensitivity C-reactive protein at baseline.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this prospective cohort study, we found that substantial changes in TyG index was significant associated with the risk of CVD. Notably, the risk of CVD increased with elevated TyG index over time. Similar patterns were observed for stroke and MI. The trend remained robust among multiple sensitivity analyses and the stratified analyses. Furthermore, the addition of changes in TyG index to the baseline risk model including traditional risk factors significantly promoted the ability of risk stratification.\u003c/p\u003e\n\u003cp\u003eThe present analyses showed that participants with elevated TyG index over time had a higher risk of developing CVD relative to their counterparts with decreased TyG index over time. Previous studies, which were generally based on a single TyG index assessment, generated the consistent results regarding the association between baseline TyG index and CVD risk. The Vascular Metabolic CUN cohort with 5,014 subjects found that a higher level of TyG index was significantly associated with an increased risk of developing CVD in Caucasian population, participants in the highest quintile group had 2.32-fold higher risk of CVD than the lowest quintile group.[\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e] Another retrospective cohort analysis of 6,078 participants aged over 60 years showed the fourth quarter of TyG index was associated with 72% higher risk of CVD events.[\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e] The Tehran Lipid and Glucose Study with 7,521 Iranians revealed that the significant relationship between the TyG index and risk of CVD/coronary heart disease was more prominent among the younger population.[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e] Similarly, data from the National Health Information Database showed that participants in the highest TyG index quartile were at higher risk for stroke and MI, independently of other traditional cardiovascular risk factors.[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e\n\u003cp\u003eOf note, the TyG index is calculated based on TG and FBG, both of which vary over time, thus evaluating the TyG index only at baseline was unable to examine the longitudinal association between the dynamic changes in TyG index and CVD risk. Moreover, one single measurement of the TyG index was also subject to potential regression dilution bias and reverse causation issue.[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e] To address these knowledge gaps and methodological limitations, the concept of assessing the effect of change in TyG index on clinical outcomes has been proposed. In a rural Chinese cohort study, the author used the difference in TyG index between follow-up and baseline to predict the risk of type 2 diabetes, the results showed that risk of incident diabetes was increased with quartiles of TyG difference in normal-weight people.[\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e] While the relationship between changes in TyG index and CVD has not been established by previous studies. In line with above-mentioned study, our study found that risk of CVD was increased with quartiles of changes in TyG index, suggesting that participants with a huge increase in TyG index may warrant particular vigilance and should be followed up closely in case of the development of CVD .\u003c/p\u003e\n\u003cp\u003eAnother important finding of our study was that the addition of changes in TyG to the conventional risk model had an incremental effect on the predictive value for CVD. The predictive role of baseline TyG index for CVD has been confirmed by previous studies. Data from the Kaohsiung Medical University Hospital showed that the TyG index was a useful parameter and a stronger predictive factor than hemoglobin A\u003csub\u003e1c\u003c/sub\u003e for events and may offer an additional prognostic benefit in patients with type 2 diabetes.[\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e] The Vascular Metabolic CUN cohort showed that the areas under the curve of receiver-operating characteristics curve increased from 0.708 to 0.719 by adding TyG index to the Framingham model.[\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e] Our findings showed that a longitudinal changes in TyG index predicts a high risk of CVD that is beyond the models including baseline TyG index, highlighting the importance of monitoring longitudinal patterns of changes in TyG index in clinical practice.\u003c/p\u003e\n\u003cp\u003eThe potential mechanism underlying the association of changes in TyG index with development and progression of CVD remains uncertain, several theories have been proposed. First, study have shown that FBG mainly reflects IR from liver, whereas fasting TGs mainly reflects IR from adipose cell.[\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e] Therefore, it can be concluded that elevated TyG index over time may reflect a severe insulin resistance from two aspects. Insulin resistance can play a critical role in the formation of atherosclerotic plaques by leading to chronic inflammation, oxidative stress, endothelium dysfunction, and the facilitated the formation of foam cells, and by changing the gene expression pattern associated with estrogen receptor, as reported in animal models.[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e] Second, in our study, participants with substantial changes in TyG index tended to combine with more severe and complex clinical conditions in terms of BMI, blood pressure, lipid profiles, hypertension, diabetes, and dyslipidemia, which were a cluster of risk factors of CVD.[\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e] Changes in TyG index could modify and influence the role of cardiovascular risk factors and contribute to the progression of CVD. Third, it has been demonstrated that the TyG index was related to artery stiffness evaluated by pulse wave velocity, ankle\u0026ndash;brachial index, and carotid intima\u0026ndash;media thickness through affecting platelet adhesion, activation and aggregation[\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e], elevating TyG index over time may accelerate the development of artery stiffness thus may lead to the development of CVD.\u003c/p\u003e\n\u003cp\u003eThe strengths of the study include its prospective design, large community-based sample, long follow-up period, and consider the effect of changes in TyG index on CVD and its subtypes in the general population. The results should be interpreted in the context of some limitations. First, due to the shortage of records insulin, we could not compare the predictive value of TyG index with homeostasis model assessment insulin resistance (HOMA-IR) and the hyperinsulinaemic euglycaemic clamp test for occurrence of CVD. Second, the distribution of gender is unbalanced due to a large proportion of participants were coal miners, however, the association of changes in TyG index and CVD and subtypes were statistically robust, as the results did not show significant interaction when stratified by gender. Third, owing to the observational nature of the study, we could not establish a causal association between TyG index and the risk of CVD and our findings need to be confirmed in future studies. Finally, although potential cardiac risk factor were adjusted for, we still cannot exclude the possibility of residual or unmeasured confounding given the observational study design of the present analysis.\u003c/p\u003e"},{"header":"Conclusions","content":" \u003cp\u003eIn conclusion, we found that changes in TyG index was an independent predictor of CVD and its subtypes. Elevated TyG index over time was associated with higher risk of CVD, stroke and MI. Our findings emphasize the importance of monitoring the longitudinal changes in TyG index for identifying individuals at high risk of development CVD.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eBMI=body mass index; CI=confidence interval; CVD=cardiovascular disease; DBP=diastolic blood pressure; FBG=fasting blood glucose; HDL-C=high-density lipoprotein cholesterol;\u0026nbsp; HOMA-IR=homeostasis model assessment insulin resistance; HR=hazard ratio; hs-CRP=high-sensitivity C-reactive protein; IDI=integrated discrimination improvement; LDL-C=low-density lipoprotein cholesterol; NRI=net reclassification index; SBP=systolic blood pressure; SD=standard deviation; TG=triglyceride; TyG=triglyceride-glucose\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was performed according to the guidelines of the Helsinki Declaration and was approved by the Ethics Committee of Kailuan General Hospital (approval number: 2006-05) and Beijing Tiantan Hospital (approval number: 2010-014-01). All participants were agreed to take part in the study and provided informed written consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese authors declare that they have no conflicts of interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from National Key R\u0026amp;D Program of China (2016YFC0901002, 2017YFC1310901, 2018YFC1312903), Beijing Municipal Science \u0026amp; Technology Commission (D171100003017002), National Science and Technology Major Project (2017ZX09304018), Beijing Municipal Administration of Hospitals Incubating Program (PX2020021), Beijing Excellent Talents Training Program (2018000021469G234), and Young Elite Scientists Sponsorship Program by CAST (2018QNRC001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.W. and Y.W. contributed to the conception and design of the study; A.W. and X.T. contributed to manuscript drafting; A.W., X.T., Y.Z. and S.C. contributed to the statistics analysis; S.C. and X.M. contributed to the acquisition of data; S.W., Y.W. and A.W. contributed to critical revisions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all study participants, their relatives, the members of the survey teams at the 11 regional hospitals of the Kailuan Medical Group; and the project development and management teams at the Beijing Tiantan Hospital and the Kailuan Group.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOrmazabal V, Nair S, Elfeky O, Aguayo C, Salomon C, Zu\u0026ntilde;iga F: Association between insulin resistance and the development of cardiovascular disease. \u003cem\u003eCardiovascular diabetology \u003c/em\u003e2018, 17(1):122.\u003c/li\u003e\n\u003cli\u003eBersch-Ferreira \u0026Acirc;, Sampaio G, Gehringer M, Torres E, Ross-Fernandes M, da Silva J, Torreglosa C, Kovacs C, Alves R, Magnoni C\u003cem\u003e et al\u003c/em\u003e: Association between plasma fatty acids and inflammatory markers in patients with and without insulin resistance and in secondary prevention of cardiovascular disease, a cross-sectional study. \u003cem\u003eNutrition journal \u003c/em\u003e2018, 17(1):26.\u003c/li\u003e\n\u003cli\u003eXun P, Wu Y, He Q, He K: Fasting insulin concentrations and incidence of hypertension, stroke, and coronary heart disease: a meta-analysis of prospective cohort studies. \u003cem\u003eThe American journal of clinical nutrition \u003c/em\u003e2013, 98(6):1543-1554.\u003c/li\u003e\n\u003cli\u003evan der Schaft N, Schoufour J, Nano J, Kiefte-de Jong J, Muka T, Sijbrands E, Ikram M, Franco O, Voortman T: Dietary antioxidant capacity and risk of type 2 diabetes mellitus, prediabetes and insulin resistance: the Rotterdam Study. \u003cem\u003eEuropean journal of epidemiology \u003c/em\u003e2019, 34(9):853-861.\u003c/li\u003e\n\u003cli\u003eHan T, Lan L, Qu R, Xu Q, Jiang R, Na L, Sun C: Temporal Relationship Between Hyperuricemia and Insulin Resistance and Its Impact on Future Risk of Hypertension. \u003cem\u003eHypertension (Dallas, Tex : 1979) \u003c/em\u003e2017, 70(4):703-711.\u003c/li\u003e\n\u003cli\u003eHuang-Doran I, Tomlinson P, Payne F, Gast A, Sleigh A, Bottomley W, Harris J, Daly A, Rocha N, Rudge S\u003cem\u003e et al\u003c/em\u003e: Insulin resistance uncoupled from dyslipidemia due to C-terminal PIK3R1 mutations. \u003cem\u003eJCI insight \u003c/em\u003e2016, 1(17):e88766.\u003c/li\u003e\n\u003cli\u003eGangel M, Dollar J, Brown A, Keane S, Calkins S, Shanahan L, Wideman L: Childhood social preference and adolescent insulin resistance: Accounting for the indirect effects of obesity. \u003cem\u003ePsychoneuroendocrinology \u003c/em\u003e2020, 113:104557.\u003c/li\u003e\n\u003cli\u003eCersosimo E, Solis-Herrera C, Trautmann M, Malloy J, Triplitt C: Assessment of pancreatic \u0026beta;-cell function: review of methods and clinical applications. \u003cem\u003eCurrent diabetes reviews \u003c/em\u003e2014, 10(1):2-42.\u003c/li\u003e\n\u003cli\u003eKhan S, Sobia F, Niazi N, Manzoor S, Fazal N, Ahmad F: Metabolic clustering of risk factors: evaluation of Triglyceride-glucose index (TyG index) for evaluation of insulin resistance. \u003cem\u003eDiabetology \u0026amp; metabolic syndrome \u003c/em\u003e2018, 10:74.\u003c/li\u003e\n\u003cli\u003eMazidi M, Kengne A, Katsiki N, Mikhailidis D, Banach M: Lipid accumulation product and triglycerides/glucose index are useful predictors of insulin resistance. \u003cem\u003eJournal of diabetes and its complications \u003c/em\u003e2018, 32(3):266-270.\u003c/li\u003e\n\u003cli\u003eIrace C, Carallo C, Scavelli FB, De Franceschi MS, Esposito T, Tripolino C, Gnasso A: Markers of insulin resistance and carotid atherosclerosis. A comparison of the homeostasis model assessment and triglyceride glucose index. \u003cem\u003eInternational journal of clinical practice \u003c/em\u003e2013, 67(7):665-672.\u003c/li\u003e\n\u003cli\u003eS\u0026aacute;nchez-\u0026Iacute;\u0026ntilde;igo L, Navarro-Gonz\u0026aacute;lez D, Fern\u0026aacute;ndez-Montero A, Pastrana-Delgado J, Mart\u0026iacute;nez J: The TyG index may predict the development of cardiovascular events. \u003cem\u003eEuropean journal of clinical investigation \u003c/em\u003e2016, 46(2):189-197.\u003c/li\u003e\n\u003cli\u003eLi S, Guo B, Chen H, Shi Z, Li Y, Tian Q, Shi S: The role of the triglyceride (triacylglycerol) glucose index in the development of cardiovascular events: a retrospective cohort analysis. \u003cem\u003eScientific reports \u003c/em\u003e2019, 9(1):7320.\u003c/li\u003e\n\u003cli\u003eBarzegar N, Tohidi M, Hasheminia M, Azizi F, Hadaegh F: The impact of triglyceride-glucose index on incident cardiovascular events during 16 years of follow-up: Tehran Lipid and Glucose Study. \u003cem\u003eCardiovascular diabetology \u003c/em\u003e2020, 19(1):155.\u003c/li\u003e\n\u003cli\u003eHong S, Han K, Park C: The triglyceride glucose index is a simple and low-cost marker associated with atherosclerotic cardiovascular disease: a population-based study. \u003cem\u003eBMC medicine \u003c/em\u003e2020, 18(1):361.\u003c/li\u003e\n\u003cli\u003eSu W, Chen S, Huang Y, Huang J, Wu P, Hsu W, Lee M: Comparison of the Effects of Fasting Glucose, Hemoglobin A, and Triglyceride-Glucose Index on Cardiovascular Events in Type 2 Diabetes Mellitus. \u003cem\u003eNutrients \u003c/em\u003e2019, 11(11).\u003c/li\u003e\n\u003cli\u003eJin C, Chen S, Vaidya A, Wu Y, Wu Z, Hu FB, Kris-Etherton P, Wu S, Gao X: Longitudinal Change in Fasting Blood Glucose and Myocardial Infarction Risk in a Population Without Diabetes. \u003cem\u003eDiabetes care \u003c/em\u003e2017, 40(11):1565-1572.\u003c/li\u003e\n\u003cli\u003eWang A, Sun Y, Liu X, Su Z, Li J, Luo Y, Chen S, Wang J, Li X, Zhao Z\u003cem\u003e et al\u003c/em\u003e: Changes in proteinuria and the risk of myocardial infarction in people with diabetes or pre-diabetes: a prospective cohort study. \u003cem\u003eCardiovascular diabetology \u003c/em\u003e2017, 16(1):104.\u003c/li\u003e\n\u003cli\u003eWu S, An S, Li W, Lichtenstein AH, Gao J, Kris-Etherton PM, Wu Y, Jin C, Huang S, Hu FB\u003cem\u003e et al\u003c/em\u003e: Association of Trajectory of Cardiovascular Health Score and Incident Cardiovascular Disease. \u003cem\u003eJAMA network open \u003c/em\u003e2019, 2(5):e194758.\u003c/li\u003e\n\u003cli\u003eTian X, Zuo Y, Chen S, Liu Q, Tao B, Wu S, Wang A: Triglyceride-glucose index is associated with the risk of myocardial infarction: an 11-year prospective study in the Kailuan cohort. \u003cem\u003eCardiovascular diabetology \u003c/em\u003e2021, 20(1):19.\u003c/li\u003e\n\u003cli\u003eWang A, Wang G, Liu Q, Zuo Y, Chen S, Tao B, Tian X, Wang P, Meng X, Wu S\u003cem\u003e et al\u003c/em\u003e: Triglyceride-glucose index and the risk of stroke and its subtypes in the general population: an 11-year follow-up. \u003cem\u003eCardiovascular diabetology \u003c/em\u003e2021, 20(1):46.\u003c/li\u003e\n\u003cli\u003eWang C, Yuan Y, Zheng M, Pan A, Wang M, Zhao M, Li Y, Yao S, Chen S, Wu S\u003cem\u003e et al\u003c/em\u003e: Association of Age of Onset of Hypertension With Cardiovascular\u0026nbsp;Diseases and Mortality. \u003cem\u003eJournal of the American College of Cardiology \u003c/em\u003e2020, 75(23):2921-2930.\u003c/li\u003e\n\u003cli\u003eWu S, Song Y, Chen S, Zheng M, Ma Y, Cui L, Jonas J: Blood Pressure Classification of 2017 Associated With Cardiovascular Disease and Mortality in Young Chinese Adults. \u003cem\u003eHypertension (Dallas, Tex : 1979) \u003c/em\u003e2020, 76(1):251-258.\u003c/li\u003e\n\u003cli\u003eStroke--1989. Recommendations on stroke prevention, diagnosis, and therapy. Report of the WHO Task Force on Stroke and other Cerebrovascular Disorders. \u003cem\u003eStroke \u003c/em\u003e1989, 20(10):1407-1431.\u003c/li\u003e\n\u003cli\u003eTunstall-Pedoe H, Kuulasmaa K, Amouyel P, Arveiler D, Rajakangas A, Pajak A: Myocardial infarction and coronary deaths in the World Health Organization MONICA Project. Registration procedures, event rates, and case-fatality rates in 38 populations from 21 countries in four continents. \u003cem\u003eCirculation \u003c/em\u003e1994, 90(1):583-612.\u003c/li\u003e\n\u003cli\u003eDesquilbet L, Mariotti F: Dose-response analyses using restricted cubic spline functions in public health research. \u003cem\u003eStatistics in medicine \u003c/em\u003e2010, 29(9):1037-1057.\u003c/li\u003e\n\u003cli\u003eVanderWeele T, Jackson J, Li S: Causal inference and longitudinal data: a case study of religion and mental health. \u003cem\u003eSocial psychiatry and psychiatric epidemiology \u003c/em\u003e2016, 51(11):1457-1466.\u003c/li\u003e\n\u003cli\u003eZhang M, Wang B, Liu Y, Sun X, Luo X, Wang C, Li L, Zhang L, Ren Y, Zhao Y\u003cem\u003e et al\u003c/em\u003e: Cumulative increased risk of incident type 2 diabetes mellitus with increasing triglyceride glucose index in normal-weight people: The Rural Chinese Cohort Study. \u003cem\u003eCardiovasc Diabetol \u003c/em\u003e2017, 16(1):30.\u003c/li\u003e\n\u003cli\u003eLow S, Khoo K, Irwan B, Sum C, Subramaniam T, Lim S, Wong T: The role of triglyceride glucose index in development of Type 2 diabetes mellitus. \u003cem\u003eDiabetes research and clinical practice \u003c/em\u003e2018, 143:43-49.\u003c/li\u003e\n\u003cli\u003eMatulewicz N, Karczewska-Kupczewska M: Insulin resistance and chronic inflammation. \u003cem\u003ePostepy higieny i medycyny doswiadczalnej (Online) \u003c/em\u003e2016, 70(0):1245-1258.\u003c/li\u003e\n\u003cli\u003eJanus A, Szahidewicz-Krupska E, Mazur G, Doroszko A: Insulin Resistance and Endothelial Dysfunction Constitute a Common Therapeutic Target in Cardiometabolic Disorders. \u003cem\u003eMediators of inflammation \u003c/em\u003e2016, 2016:3634948.\u003c/li\u003e\n\u003cli\u003eOh J, Riek AE, Darwech I, Funai K, Shao J, Chin K, Sierra OL, Carmeliet G, Ostlund RE, Jr., Bernal-Mizrachi C: Deletion of macrophage Vitamin D receptor promotes insulin resistance and monocyte cholesterol transport to accelerate atherosclerosis in mice. \u003cem\u003eCell reports \u003c/em\u003e2015, 10(11):1872-1886.\u003c/li\u003e\n\u003cli\u003eMin J, Weitian Z, Peng C, Yan P, Bo Z, Yan W, Yun B, Xukai W: Correlation between insulin-induced estrogen receptor methylation and atherosclerosis. \u003cem\u003eCardiovasc Diabetol \u003c/em\u003e2016, 15(1):156.\u003c/li\u003e\n\u003cli\u003eFrancula-Zaninovic S, Nola IA: Management of Measurable Variable Cardiovascular Disease' Risk Factors. \u003cem\u003eCurrent cardiology reviews \u003c/em\u003e2018, 14(3):153-163.\u003c/li\u003e\n\u003cli\u003eCortesi P, Fornari C, Madotto F, Conti S, Naghavi M, Bikbov B, Briant P, Caso V, Crotti G, Johnson C\u003cem\u003e et al\u003c/em\u003e: Trends in cardiovascular diseases burden and vascular risk factors in Italy: The Global Burden of Disease study 1990-2017. \u003cem\u003eEuropean journal of preventive cardiology \u003c/em\u003e2020:2047487320949414.\u003c/li\u003e\n\u003cli\u003eZhao S, Yu S, Chi C, Fan X, Tang J, Ji H, Teliewubai J, Zhang Y, Xu Y: Association between macro- and microvascular damage and the triglyceride glucose index in community-dwelling elderly individuals: the Northern Shanghai Study. \u003cem\u003eCardiovasc Diabetol \u003c/em\u003e2019, 18(1):95.\u003c/li\u003e\n\u003cli\u003eWon K, Park G, Lee S, Cho I, Kim H, Lee B, Chang H: Relationship of insulin resistance estimated by triglyceride glucose index to arterial stiffness. \u003cem\u003eLipids in health and disease \u003c/em\u003e2018, 17(1):268.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Triglyceride-glucose index, Longitudinal changes, Cardiovascular disease, Stroke, Myocardial infarction, Predictive value","lastPublishedDoi":"10.21203/rs.3.rs-375689/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-375689/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe relationship between baseline triglyceride-glucose (TyG) index and cardiovascular disease (CVD) has been confirmed by former studies. However, the effect of longitudinal changes in TyG index on CVD remains uncertain. This study aimed to investigate the association of changes in TyG index with CVD in the general population. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe current study included 62,443 Chinese population who were free of CVD. TyG index was calculated as ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2], changes in TyG index was defined as the difference in TyG index between 2010 and 2006. Cox proportional hazard model and restricted cubic spline was used to examine the association between changes in TyG index and CVD.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e During a median follow-up of 7.01 years, 2,530 (4.05%) incident CVD occurred, including 2,018 (3.23%) stroke and 545 (0.87%) MI. Risk of CVD was increased with quartiles of changes in TyG index, the adjusted hazard ratio (HR) in Q4 group versus Q1 group was 1.37 (95% confidence interval [CI], 1.21-1.54) for the overall CVD, 1.38 (95% CI, 1.19-1.60) for stroke, and 1.36 (95% CI, 1.05-1.76) for MI. Restricted cubic spline also showed cumulative increased risk of CVD with increasing changes in TyG index. Furthermore, the addition of changes in TyG index to a baseline risk model for CVD improved the C-statistics (\u003cem\u003eP\u003c/em\u003e=0.0097), the integrated discrimination improvement (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001), and the category-free net reclassification improvement (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001). Similar results were observed for stroke and MI.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eSubstantial changes in TyG index can independently predict the risk of CVD in the general population. Monitoring long-term changes in TyG may be helpful in the early identification of individuals at high risk of CVD.\u003c/p\u003e","manuscriptTitle":"Changes in Triglyceride-glucose Index Predict the Risk of Cardiovascular Diseases in the General Population: a Prospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-04-01 20:57:40","doi":"10.21203/rs.3.rs-375689/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2021-05-02T07:24:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-04-21T00:00:00+00:00","index":1,"fulltext":"Recommendation: Reviewer's comments unavailable due to the journal's policy.\n"},{"type":"reviewerAgreed","content":"","date":"2021-04-06T00:00:00+00:00","index":3,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-04-06T00:00:00+00:00","index":3,"fulltext":"Recommendation: Reviewer's comments unavailable due to the journal's policy.\n"},{"type":"editorInvitedReview","content":"","date":"2021-04-06T00:00:00+00:00","index":2,"fulltext":"Recommendation: Reviewer's comments unavailable due to the journal's policy.\n"},{"type":"editorInvitedReview","content":"","date":"2021-04-03T00:00:00+00:00","index":0,"fulltext":""},{"type":"reviewerAgreed","content":"","date":"2021-04-03T00:00:00+00:00","index":2,"fulltext":""},{"type":"reviewerAgreed","content":"","date":"2021-04-01T00:00:00+00:00","index":1,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-03-30T00:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cardiovascular Diabetology","date":"2021-03-29T01:36:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-03-29T00:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2021-03-28T23:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2021-03-28T23:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"267baef2-03d5-4b8f-999b-d1680c869f3a","owner":[],"postedDate":"April 1st, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":3353800,"name":"Cardiac \u0026 Cardiovascular Systems"}],"tags":[],"updatedAt":"2021-11-12T23:44:33+00:00","versionOfRecord":[],"versionCreatedAt":"2021-04-01 20:57:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-375689","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-375689","identity":"rs-375689","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}