Abstract
Background
Despite the association between the triglyceride-glucose (TyG) index and major adverse cardiovascular events (MACE) has been reported, a notable research gap persists regarding its predictive value in patients with acute coronary syndrome (ACS) and chronic kidney disease (CKD). This study endeavors to bridge this gap by investigating the relationship between the TyG index and outcomes among this unique patient cohort.
Methods
Patients having ACS with CKD were recruited from January 2013 to December 2021. Outcomes included all-cause mortality and MACE. The potential linear relationship was visualized by the restricted cubic spline (RCS) curve. Cox proportional hazards models were employed to rigorously examine the association between the TyG index and study outcomes. Furthermore, to assess the incremental value of the TyG index, we conducted analyses using C-statistics, the continuous net reclassification index (cNRI), and the integrated discrimination index (IDI).
Results
A total of 1094 patients were included in the final analysis. Over a median follow-up period of 30.1 months (IQR: 16.5 to 40.0 months), we recorded 167 (15.3%) all-cause mortality events and 285 (26.1%) MACE. Additionally, each 1-unit increase of it was significantly associated with a 61% elevation in the risk of all-cause mortality (95% CI: 1.28–2.03, P < 0.001) and a 72% increase in the risk of MACE (95% CI: 1.45–2.05, P < 0.001). These associations between TyG index (as quantitative or categorical variables) and endpoints remained robust even after multivariable adjustment. RCS analysis showed linear relationships between TyG and endpoints (all P for non-linear > 0.05). Moreover, subgroup analysis revealed significant interactions of dialysis and renal function (P for interaction = 0.008 and 0.011, respectively) with all-cause mortality. Lastly, combining with the established risk score significantly enhanced the discrimination and reclassification performance of TyG, as evidenced by the C-statistic, cNRI, and IDI values (all P < 0.05).
Conclusion
For patients with both ACS and CKD, TyG index is associated with both MACE and all-cause death. Prognostic classification is enhanced by the TyG index. The results collectively suggest that the TyG index serves as a reliable predictor of outcomes among patients with ACS and CKD, offering a novel metabolic perspective.
Supplementary Information
The online version contains supplementary material available at 10.1186/s13098-025-01775-9.
Keywords: Acute coronary syndrome, Chronic kidney disease, Triglyceride-glucose index
Introduction
The prevalence of cardiovascular disease (CVD) coexisting with chronic kidney disease (CKD) is on the rise, fueled by the escalating population aging and lifestyle modifications that are intimately tied to the heightened occurrence of hypertension, obesity, and diabetes [1–3]. Previous research has indicated that a substantial proportion of patients diagnosed with acute coronary syndrome (ACS), ranging from approximately 30–40%, also suffer from CKD [1]. Furthermore, patients having ACS and CKD confront a significantly increased risk of cardiovascular disease (CVD) mortality, CVD events, and procedural complications, ranging from two to three times higher, compared to those with ACS alone [2, 3]. However, patients with CKD are often underrepresented or excluded from many randomized clinical trials investigating coronary artery disease (CAD) due to concerns about certain conditions such as hyperkalemia, anticoagulation, and high mortality [4]. This under-representation can contribute to underestimating risk for patients with ACS and CKD, leading to poorer outcomes [1, 2, 5]. Therefore, studies adapting widely accepted CVD risk predictors to the CKD population are warranted.
Insulin resistance (IR), characterized by impaired insulin sensitivity requiring increased insulin for appropriate glucose uptake and utilization [6], is typically diagnosed using the hyperinsulinemic–euglycemic clamping approach [7]. The triglyceride-glucose (TyG) index serves as a readily accessible and therapeutically valuable measure of IR [8], exhibiting prognostic value in patients with CVD conditions, such as CAD, heart failure, ischemic stroke, and certain cancers [9–13]. Recently, the American Heart Association introduced cardiovascular-kidney-metabolic (CKM) syndrome for the first time and highlighted IR to be the central process involved in heart-kidney interactions and metabolic risk factor development [14, 15]. Thus, delving into the prognostic implications of the TyG index among individuals concurrently afflicted with ACS and CKD could offer a deeper understanding of the pivotal role of IR in the management of CKM. Additionally, studies evaluating the additional effect of combining the TyG index with established tools, like the Global Registry of Acute Coronary Events (GRACE) risk score [16], could offer metabolic insights into the existing risk stratification tools.
Nonetheless, a conspicuous void persists in our comprehension of the prognostic implications of the TyG index specifically in individuals who concurrently harbor both ACS and CKD. To address this, this research aimed to investigate the relationship between the TyG index and clinical outcomes among individuals diagnosed with ACS concurrent with CKD. Furthermore, another objective of this study was to evaluate the additional prognostic utility of incorporating the TyG index into the GRACE risk score. The primary hypotheses of this research were twofold: firstly, that the TyG index would demonstrate marked correlations with adverse prognoses; and secondly, that incorporating the TyG index into GRACE score would enhance its predictive capability for unfavorable outcomes.
Methods
Study population
The retrospective research initially encompassed 1,304 individuals who had been diagnosed with both ACS and CKD, and had undergone percutaneous coronary intervention (PCI) between January 2013 and December 2021 at the First Affiliated Hospital of Wenzhou Medical University. They would be excluded if met at least one of the following conditions: (1) lack of TyG index data, (2) < 18 years old, (3) lost to follow-up, (4) suspected with familial hypertriglyceridemia (triglyceride [TG] level ≥ 5.65 mmol/L), (5) co-existing malignant tumors, or (6) severe hepatic dysfunction (alanine concentration ≥ 5 times the upper reference limits). Ultimately, 1094 patients were included and divided into tertiles based on baseline TyG index levels as depicted in Fig. 1.
Fig. 1.
Flowchart of the study design
The retrospective cohort study was approved by the Ethics Review Committee of the First Affiliated Hospital of Wenzhou Medical University and adhered to the ethical principles outlined in the Declaration of Helsinki. Due to its retrospective nature, the requirement for informed consent was waived.
Data collection and definitions
The baseline data were gathered by a team of trained doctors, all of whom were kept blinded to the specific objectives of the study. The acquired personal data comprehensively encompassed heart rate, age, gender, along with body mass index (BMI). Clinical data, on the other hand, comprehensively covered ACS type, comorbid conditions such as diabetes, hypertension, dyslipidemia, and dialysis status. Furthermore, laboratory test results, TyG index, and estimated glomerular filtration rate (eGFR) were recorded. Additionally, the GRACE risk score, procedure characteristics, and discharge medications were also documented.
The diagnosis of ACS was in accordance with the published guidelines [2]. CKD was diagnosed based on an eGFR of less than 60 mL/min/1.73 m² that persisted for three months or longer, using the CKD-EPI equation for estimation [17]. The TyG index was calculated as follows: ln[fasting triglycerides (TG) in mg/dL × fasting blood glucose (FBG) in mg/dL/2], with the method described in reference [8]. PCI procedures were performed according to the treatment protocols recommended by experienced surgeons. Furthermore, pre-PCI treatment included loading doses of aspirin (300 mg) and clopidogrel (300 mg), with the exception of patients already receiving standard antiplatelet therapy. After PCI, patients were administered aspirin, clopidogrel, or ticagrelor for a duration of at least 12 months. The GRACE score, a validated risk-scoring system initially devised to predict 6-month mortality and cardiac events, was computed utilizing a panel of clinical variables as outlined in the published literature [16]. Finally, the diagnosis of metabolic syndrome (MetS) was established in accordance with the established guidelines [18].
Outcomes and follow-up
The primary outcome of this study was defined as all-cause mortality, whereas the secondary outcome encompassed major adverse cardiovascular events (MACE), specifically comprising cardiovascular (CV) death, non-fatal myocardial infarction (MI), stroke, or any revascularization procedure. Among the secondary outcomes, CV death was primarily considered as death directly attributable to cardiovascular diseases. Additionally, MI was defined in accordance with the recent guidelines [2]. Furthermore, non-fatal stroke was diagnosed when ischemic stroke was confirmed by supporting evidence from computed tomography or magnetic resonance imaging findings. Lastly, any revascularization procedures, whether performed in the target or non-target vessels during follow-up, were classified as unplanned PCI or coronary artery bypass grafting (CABG).
All endpoint data were systematically acquired through various channels, including the medical electronic system, telephone communication, and outpatient visits.
Statistical analyses
For continuous variables, the mean and standard deviation are reported. For categorical variables, the frequency and percentage are presented. Normally distributed quantitative data underwent the independent-sample t-test to detect significant differences between groups. Conversely, non-normally distributed quantitative data were subjected to the Mann–Whitney U test. Additionally, categorical data were analyzed using either Fisher’s exact test or chi-square tests, depending on the specific characteristics of the data and the sample size, to assess the statistical significance of associations between variables.
Univariable and multivariable Cox proportional hazard models were constructed to estimate the prognostic association. The Kaplan–Meier method was employed to visualize and evaluate the primary and secondary endpoints. Furthermore, the independent value of the TyG index was evaluated through the application of adjusted multivariable models, comprising variables already known as risk factors according to clinical plausibility or significant in the univariable Cox analysis. The adjusted variables were as follows: age, gender, BMI, heart rate, blood pressure, hypertension, diabetes, hyperlipidemia, ACS type, eGFR, GRACE score, discharge medications, and left ventricular ejection fraction (LVEF). To estimate residual confounding, we also calculated E-values [19] for all-cause mortality and MACE, which indicate the strength of association that an unmeasured confounder would require to nullify associations. After adjusting for the specified variables, a restricted cubic spline (RCS) curve was constructed with three nodes positioned at the 25th, 50th, and 75th percentiles. This curve was utilized to visually depict any potential linear or non-linear relationships between the variables of interest.
We conducted a detailed subgroup analysis to explore potential variations in the impact of the TyG index across distinct subgroups defined by age, gender, diabetes status, BMI, hypertension, ACS type, dialysis status, eGFR levels, ACEI/ARB usage, and MetS. Finally, the discrimination and reclassification ability for predicting mortality and MACE were evaluated and compared by calculating the C-index, integrated discrimination improvement (IDI), and continuous net reclassification improvement (cNRI) values.
All P-values reported were two-sided, and statistical significance was established at a threshold of P < 0.05. To evaluate linear trends, the median value within each tertile was utilized for analysis, with the resulting P for trend values indicating the significance of the observed trends. All statistical computations and analyses were meticulously performed using SPSS version 25.0 (IBM Corp, Armonk, NY, USA) and R version 4.2.2.
Results
Baseline characteristics of the study population
1094 individuals with both ACS and CKD (age = 72.37 ± 10.39 years) were included. Table 1 displays the baseline features and endpoints of the included patients according to the TyG index tertile groups. Between the three groups, age, gender, heart rate, comorbidities (including hypertension, diabetes, and hyperlipidemia), current smoker, serum creatinine, eGFR, and medical therapy (including dual antiplatelet therapy, statin, insulin, and oral antidiabetic agents), were found to significantly different. During the 30.1 months (16.5 − 40.0 months) follow-up, 167 (15.3%) all-cause mortality and 285 (26.1%) MACE incidences were cumulatively recorded. Of these, 98 (9%) CV death, 127 (11.6%) any revascularization, 52 (4.8%) non-fatal MI, and 38 (3.5%) non-fatal stroke events were observed. Compared with the surviving patients, the non-survivors were older and had higher heart rate, serum creatinine, FBG, TG, and GRACE score as well as lower SBP, DBP, eGFR, LVEF, and multivessel disease incidence. Moreover, the non-survivors were more likely to have dialysis and STEMI and less likely to be treated with standard medications (including ACEI/ARB usage) at discharge (Supplemental Table 1).
Table 1.
Baseline characteristics and endpoints by TyG index categories
| Variables | T1 (n = 365) | T2 (n = 365) | T3 (n = 364) | P value |
|---|---|---|---|---|
| TyG index | 8.36 (8.12,8.53) | 8.92 (8.8,9.05) | 9.64 (9.41,9.96) | < 0.001 |
| Demographic data | ||||
| Age (years) | 75 (68,81) | 73 (65,79) | 74 (66,79.25) | 0.036 |
| Male | 283 (77.53%) | 250 (68.49%) | 218 (59.89%) | < 0.001 |
| BMI (kg/m2) | 23.31 (20.78,24.97) | 23.64 (21.72,25.47) | 23.35 (21.07,25.24) | 0.091 |
| Heart rate (bpm) | 76 (67,88) | 77 (66,90) | 80 (69,98.25) | < 0.001 |
| SBP (mmHg) | 135 (117,151) | 132 (116,152) | 135 (117,155) | 0.583 |
| DBP (mmHg) | 73 (65,84) | 73 (63,83) | 75 (64,85) | 0.164 |
| Cardiovascular risk factors and prior procedural | ||||
| Hypertension | 263 (72.05%) | 287 (78.63%) | 295 (81.04%) | 0.011 |
| Diabetes | 91 (24.93%) | 145 (39.73%) | 208 (57.14%) | < 0.001 |
| Hyperlipidemia | 107 (29.4%) | 165 (45.21%) | 186 (51.1%) | < 0.001 |
| Dialysis | 42 (11.51%) | 48 (13.15%) | 49 (13.46%) | 0.696 |
| Current smoker | 153 (41.92%) | 118 (32.33%) | 108 (29.67%) | 0.001 |
| Prior MI | 10 (2.74%) | 10 (2.74%) | 8 (2.2%) | 0.867 |
| Prior PCI | 32 (8.77%) | 26 (7.12%) | 40 (10.99%) | 0.186 |
| Prior CABG | 8 (2.19%) | 2 (0.55%) | 4 (1.1%) | 0.132 |
| Type of ACS | 0.191 | |||
| NSTE-ACS | 165 (45.21%) | 166 (45.48%) | 144 (39.56%) | |
| STEMI | 200 (54.79%) | 199 (54.52%) | 220 (60.44%) | |
| Aetiology of CKD | 0.306 | |||
| Chronic glomerulonephritis | 66 (18.08%) | 58 (15.89%) | 51 (14.01%) | |
| Diabetic nephropathy | 65 (17.81%) | 63 (17.26%) | 73 (20.05%) | |
| Hypertensive nephropathy | 94 (25.75%) | 112 (30.68%) | 117 (32.14%) | |
| lgA nephropathy | 10 (2.74%) | 6 (1.64%) | 8 (2.2%) | |
| Membranous nephropathy | 14 (3.84%) | 9 (2.47%) | 12 (3.3%) | |
| Hyperuricemic nephropathy | 21 (5.75%) | 36 (9.86%) | 25 (6.87%) | |
| Other/unknown reasons | 95 (26.03%) | 81 (22.19%) | 78 (21.43%) | |
| Laboratory data | ||||
| Serum creatinine (mg/dL) | 1.38 (1.2,2) | 1.47 (1.21,2) | 1.52 (1.2,2.36) | 0.03 |
| eGFR (mL/min/1.73m2) | 43.6 (29.36,54.62) | 43.01 (23.23,53.54) | 37.5 (18.49,50.29) | 0.002 |
| FBG (mmol/l) | 5.5 (4.8,6.9) | 6.8 (5.6,8.1) | 9.65 (7.3,12.5) | < 0.001 |
| TC (mmol/l) | 4.21 (3.46,4.96) | 4.53 (3.69,5.32) | 4.69 (4,5.69) | < 0.001 |
| TG (mmol/l) | 0.89 (0.7,1.15) | 1.41 (1.16,1.69) | 2.16 (1.67,2.84) | < 0.001 |
| HDL-C (mmol/l) | 0.99 (0.83,1.26) | 0.93 (0.78,1.12) | 0.94 (0.78,1.13) | 0.001 |
| LDL-C (mmol/l) | 2.29 (1.68,3.02) | 2.5 (1.86,3.24) | 2.59 (1.97,3.28) | 0.003 |
| ALT (U/L) | 32.00 (18.00,67.00) | 37.00 (18.00,80.50) | 36.00 (18.25,88.00) | 0.452 |
| AST (U/L) | 82.00 (25.00,294.00) | 107.00 (29.00,333.00) | 106.00 (26.00,319.00) | 0.616 |
| GRACE score | 129 (114,145) | 128 (114,143) | 132 (116,148) | 0.152 |
| Echocardiographic and angiographic data | ||||
| LVEF (%) | 52 (45,60.9) | 52 (44,60) | 49 (43,61.1) | 0.251 |
| Multivessel disease | 122 (33.42%) | 125 (34.25%) | 126 (34.62%) | 0.941 |
| LAD stenosis > = 50% | 301 (82.47%) | 305 (83.56%) | 304 (83.52%) | 0.905 |
| LCX stenosis > = 50% | 215 (58.9%) | 223 (61.1%) | 241 (66.21%) | 0.114 |
| RCA stenosis > = 50% | 212 (58.08%) | 219 (60%) | 205 (56.32%) | 0.602 |
| Calcified lesions | 70 (19.18%) | 57 (15.62%) | 64 (17.58%) | 0.443 |
| Thrombosis | 55 (15.07%) | 59 (16.16%) | 55 (15.11%) | 0.898 |
| Medical therapy | ||||
| DAPT | 310 (84.93%) | 337 (92.33%) | 334 (91.76%) | 0.001 |
| Beta blocker | 211 (57.81%) | 196 (53.7%) | 181 (49.73%) | 0.091 |
| ACEI/ARB | 143 (39.18%) | 139 (38.08%) | 127 (34.89%) | 0.462 |
| Statin | 342 (93.7%) | 333 (91.23%) | 321 (88.19%) | 0.033 |
| Insulin | 26 (7.12%) | 45 (12.33%) | 96 (26.37%) | < 0.001 |
| Oral antidiabetic agents | 69 (18.9%) | 118 (32.33%) | 177 (48.63%) | < 0.001 |
| Primary and secondary endpoints | ||||
| All-cause mortality | 31 (8.49%) | 58 (15.89%) | 78 (21.43%) | < 0.001 |
| MACE | 68 (18.63%) | 89 (24.38%) | 128 (35.16%) | < 0.001 |
| CV death | 13 (3.56%) | 33 (9.04%) | 52 (14.29%) | < 0.001 |
| Any revascularization | 35 (9.59%) | 41 (11.23%) | 51 (14.01%) | 0.17 |
| Non-fatal MI | 14 (3.84%) | 15 (4.11%) | 23 (6.32%) | 0.225 |
| Non-fatal stroke | 14 (3.84%) | 9 (2.47%) | 15 (4.12%) | 0.427 |
Abbreviations: TyG, triglyceride-glucose; T1, tertile 1; T2, tertile 2; T3, tertile 3; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; MI, myocardial infarction; PCI, percutaneous coronary intervention; CABG, coronary artery bypass grafting; ACS, acute coronary syndrome; NSTEMI, non–ST-segment elevation myocardial infarction; STEMI, ST-segment elevation myocardial infarction; CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; FBG, fasting blood glucose; TC, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GRACE, The Global Registry of Acute Coronary Events; LVEF, left ventricular ejection fraction; LAD, left anterior descending coronary; LCX, left circumflex artery; RCA, right coronary artery; DAPT, dual antiplatelet therapy; ACEI/ARB, angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker
Correlation between TyG index and CV risk factors
The TyG index had positive associations with FBG, TG, TC, LDL-C, GRACE score and negative associations with HDL-C, age, and eGFR (all P < 0.05). However, no significant associations were detected in terms of BMI, LVEF, alanine aminotransferase (ALT), aspartate aminotransferase (AST), or use of ACEI/ARB (Table 2).
Table 2.
Correlation between TyG index and cardiovascular risk factors
| Variables | Correlation coefficient (r) | P value |
|---|---|---|
| FBG (mmol/l) | 0.603 | < 0.001 |
| TC (mmol/l) | 0.191 | < 0.001 |
| TG (mmol/l) | 0.803 | < 0.001 |
| HDL-C (mmol/l) | -0.124 | < 0.001 |
| LDL-C (mmol/l) | 0.116 | < 0.001 |
| Age (years) | -0.081 | 0.007 |
| BMI (kg/m2) | 0.012 | 0.683 |
| GRACE score | 0.152 | < 0.001 |
| eGFR (mL/min/1.73m2) | -0.134 | < 0.001 |
| LVEF (%) | -0.055 | 0.071 |
| ALT (U/L) | 0.031 | 0.392 |
| AST (U/L) | 0.02 | 0.588 |
| ACEI/ARB | -0.041 | 0.177 |
Abbreviations: FBG, fasting blood glucose; TC, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; BMI, body mass index; GRACE, The Global Registry of Acute Coronary Events; eGFR, estimated glomerular filtration rate; LVEF, left ventricular ejection fraction
Association between the TyG index and endpoints
The incidence of endpoints rose as TyG index tertiles increasing, exhibiting percentage rates of 8.49%, 15.89%, and 21.43% for all-cause mortality and of 18.63%, 24.38%, and 35.16% for MACE in the first, second, and third tertiles, respectively. Furthermore, compared to those in the first and second tertiles, Kaplan–Meier analysis revealed that individuals in the third tertile have poorer prognosis (log-rank test, all P < 0.001; Fig. 2).
Fig. 2.
Kaplan-Meier curves by the category of the TyG index. All cause mortality (A), MACE (B). TyG, triglyceride-glucose; MACE, major adverse cardiovascular events
Table 3 depicts the adjusted HRs of the TyG index for various outcomes. Upon conducting a multivariable analysis, it was indicated that the TyG index emerged as a significant risk factor, irrespective of its categorization as either a continuous or categorical variable. The multivariable analysis revealed that for every one-point increment in the TyG index, there was a marked 61% elevation in the risk of all-cause mortality, with a HR of 1.61 and a 95% CI ranging from 1.28 to 2.03. In parallel, each unit increase in the TyG index significantly intensified the risk of MACE by 72%, with a HR of 1.72 and a 95% CI spanning from 1.45 to 2.05. Furthermore, our analysis revealed that when compared to the adjusted HR of all-cause mortality in the first tertile, individuals in the second and third tertiles of the TyG index exhibited significantly higher HRs of 1.78 (95% CI: 1.13–2.78) and 2.08 (95% CI: 1.33–3.27), respectively. Similarly, with regards to the risk of MACE, those in the second and third tertiles demonstrated adjusted HRs of 1.34 (95% CI: 0.97–1.85) and 2.06 (95% CI: 1.51–2.82), respectively, in comparison to the first tertile. The results also showed a significant trend of increasing all-cause mortality (Ptrend = 0.002) and MACE (Ptrend < 0.001) risk from the first to third tertiles. Additionally, adjusted RCS curves were constructed to flexibly configure the adjusted model (Fig. 3). The adjusted RCS results implied that TyG had linear relationships with mortality and MACE (all P for non-linear > 0.05).
Table 3.
Multivariable Cox regression analyses for all-cause mortality and MACE
| All-cause mortality | MACE | |||||
|---|---|---|---|---|---|---|
| HR [95% CI] | P-value | E-value | HR [95% CI] | P-value | E-value | |
| TyG index (continuous) | 1.61[1.28,2.03] | < 0.001 | 2.6[1.88,3.48] | 1.72[1.45,2.05] | < 0.001 | 2.83[2.26,3.52] |
| TyG index (categoricale) | ||||||
| T1 | reference | - | - | reference | - | - |
| T2 | 1.78[1.13,2.78] | 0.014 | 2.96[1.51,4.25] | 1.34[0.97,1.85] | 0.076 | 2.01[1,3.1] |
| T3 | 2.08[1.33,3.27] | < 0.001 | 3.58[1.99,5.99] | 2.06[1.51,2.82] | < 0.001 | 3.54[2.39,5.09] |
| P for trend | 0.002 | < 0.001 | ||||
Adjusted by age, gender, BMI, heart rate, SBP, DBP, hypertension, diabetes, hyperlipidemia, type of ACS, eGFR, GRACE score, LVEF, and discharge medication including beta-blockers, ACEI/ARB, and statins
P for trend were evaluated by a median value within each tertile as a continuous variable
HR, hazard ratio; CI, confidence interval; other abbreviations as in Table 1
Fig. 3.
Adjusted restricted spline curves for the associations between the TyG index and outcomes. Red lines represent the cut-off value, blue shadows and lines represent the 95% confidence intervals. All cause mortality (A), MACE (B). HR (95%CI) was adjusted according to the multivariable Cox analysis. TyG, triglyceride-glucose, HR, hazard ratio; CI, confidence interval; MACE, major adverse cardiovascular event
Moreover, the comprehensive associations between the TyG index and various CV outcomes are clearly outlined in Supplemental Table 2. The predictive value of the TyG index for MACE is primarily driven by events other than non-fatal stroke, as evidenced by its independent association with CV death, any revascularization, and non-fatal myocardial infarction (P < 0.05), but not with non-fatal stroke (P > 0.05).
Our subgroup analysis further underscores the robust and consistent nature of the TyG index as a predictor of both mortality and MACE across most subgroups (Fig. 4). Notably, significant interactions were observed between the TyG index and two specific groups: eGFR (P for interaction = 0.011) and dialysis status (P for interaction = 0.008), both of which had a pronounced impact on the risk of all-cause mortality.
Fig. 4.
Adjusted hazard ratios for outcomes in different subgroups. Hazard ratios of all-cause mortality (A) and MACE (B). MACE, major adverse cardiovascular events; HR, hazard ratios; CI, confidence interval; BMI, body mass index; MetS, metabolic syndrome; ACS, acute coronary syndrome; NSTE-ACS, non–ST-segment elevation acute coronary syndrome; STEMI, ST-segment elevation myocardial infarction; eGFR, estimated glomerular filtration rate; ACEI/ARB, angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker
Additional prognostic value
This study assessed the prediction performance for outcomes when adding the TyG into the GRACE score (Table 4). The results indicated that integrating with the TyG index significantly enhanced the predict performance, as the improved C-statistic values (all-cause mortality: 0.737 to 0.756 and MACE: 0.655 to 0.678) evidenced. Moreover, the discrimination ability after combining the TyG index was examined using cNRI and IDI. The findings suggested the integration of TyG and GRACE score led to an increased cNRI of 0.429 and a 3.9% improvement in IDI for all-cause mortality as well as a cNRI of 0.308 and a 3.9% improvement in IDI (all P < 0.001) for MACE prediction. Finally, the C-statistic, cNRI, and IDI results also demonstrated significant improvements in the performance of predicting CV death after adding the TyG index.
Table 4.
Model performance after the addition of TyG index to the GRACE score for predicting primary and secondary endpoints
| Model | C-statistics[95%CI] | P-value | cNRI[95%CI] | P-value | IDI[95%CI] | P-value |
|---|---|---|---|---|---|---|
| All-cause mortality | ||||||
| GRACE | 0.737[0.675,0.799] | ref | ref | ref | ||
| GRACE + TyG index | 0.756[0.7,0.811] | < 0.001 | 0.429[0.266,0.591] | < 0.001 | 0.039[0.025,0.053] | < 0.001 |
| MACE | ||||||
| GRACE | 0.655[0.589,0.72] | ref | ref | ref | ||
| GRACE + TyG index | 0.678[0.613,0.742] | < 0.001 | 0.308[0.174,0.442] | < 0.001 | 0.039[0.026.0.051] | < 0.001 |
| CV death | ||||||
| GRACE | 0.643[0.588,0.698] | ref | ref | ref | ||
| GRACE + TyG index | 0.714[0.66,0.769] | < 0.001 | 0.511[0.307,0.715] | < 0.001 | 0.039[0.022,0.056] | < 0.001 |
| Any revascularization | ||||||
| GRACE | 0.638[0.584,0.691] | ref | ref | ref | ||
| GRACE + TyG index | 0.665[0.617,0.713] | < 0.001 | 0.076[-0.108,0.26] | 0.42 | 0.004[-0.001,0.009] | 0.114 |
| Non-fatal MI | ||||||
| GRACE | 0.544[0.452,0.635] | ref | ref | ref | ||
| GRACE + TyG index | 0.577[0.496,0.657] | 0.114 | 0.219[-0.059,0.497] | 0.122 | 0.002[-0.002,0.005] | 0.107 |
| Non-fatal stroke | ||||||
| GRACE | 0.586[0.491,0.681] | ref | ref | ref | ||
| GRACE + TyG index | 0.581[0.487,0.675] | 0.892 | 0.082[-0.241,0.405] | 0.619 | 0.002[-0.002,0.002] | 0.508 |
Sensitivity analysis
Based on the multivariable cox regression analysis between the TyG index and endpoints, we calculated the E-values in Table 3. When the TyG index is treated as a continuous variable, the e-value for all-cause mortality is 2.6, which is greater than the upper limit of the CI at 2.03. For MACE, the E-value is 2.83, which is also greater than the upper limit of the CI at 2.05. When considering the TyG index as a categorical variable, the E-value for the all-cause mortality in the T3 group is 3.58, which is greater than the upper limit of the CI at 3.27. Similarly, the E-value for the MACE in the T3 group is 3.54, exceeding the upper limit of the CI for the TyG index at 2.82.
Discussion
In this study, we investigated the long-term prognostic value of the TyG index in patients with ACS who also have CKD, focusing on their outcomes following PCI. This study revealed several key findings supporting the application of the TyG index to manage the specific patient. Firstly, a higher TyG index was significantly associated with an elevated risk of both mortality and MACE, in comparison to a lower TyG index. Secondly, the independent predictive power of the TyG index persisted even after rigorous adjustment for potential confounding factors, demonstrating its robust association with both MACE and all-cause mortality. Thirdly, the TyG index demonstrated a consistent and linear correlation with the study outcomes, indicating a direct proportionality between the index value and the risk of the observed outcomes. Lastly, incorporating the TyG index into the GRACE score significantly enhanced its prognostic power for risk stratification. All these study results confirmed the value of the TyG index, as it could facilitate the implementation of targeted interventions focused on mitigating IR and optimizing risk stratification for improved clinical decision-making.
IR is prevalent among patients with ACS and is a known contributor to the progression of metabolic disease and CKD [14, 17]. However, the hyperinsulinemic–euglycemic clamping approach, the diagnostic tool for IR, has certain limitations in real-world settings for its high costs and complexity [8]. The TyG index is a novel metric that simplifies the quantification process of IR by utilizing the association between the TG and glucose levels of patients [8]. Numerous investigations have examined the TyG index in ACS. For instance, a study involving 438 patients with NSTEMI reported significant associations between the TyG index and coronary burdens, as well as clinical outcomes [20]. Additionally, Zhao et al. discovered that despite maintaining low LDL-C levels among acute myocardial infarction patients, an elevated TyG index remains significantly associated with an increased incidence of MACE [21]. Other studies have demonstrated that patients with ACS who have undergone prior CABG face a higher risk of long-term outcomes, and this finding persists regardless of the presence or absence of diabetes [22]. This aligns with our research findings, where the TyG index emerged as an independent risk factor in our subgroup analysis, regardless of whether the individuals had diabetes or not. However, those studies had limited representation of patients with CKD or excluded them from the investigation, thereby leading to a gap in our understanding of the TyG index in individuals with both ACS and CKD. Previous studies have emphasized the glucolipid metabolism features of the cardio-renal interaction and the correlation between IR and the risk of kidney injury [18, 23, 24]. Our study also revealed that a higher TyG index was associated with a lower eGFR.
The current study explored the TyG index in patients with ACS combined with CKD who underwent PCI for the first time and found independent associations between the TyG index and both long-term outcomes. In the case of CV events, the TyG index retained its association with CV death, any revascularization, and non-fatal MI but not with non-fatal stroke. Further RCS analysis indicated that this association was linear. A recent national population-based cohort including 6496 participants without CVD employed mediation analysis to reveal that impaired renal function partially mediated the relationship between the index and future CV diseases [25]. Another study reported that the TyG index could predict in-hospital and 1-year mortality among individuals having both CAD and CKD from MIMIC database [26]. However, the researchers did not observe any significant interactions, and the association disappeared during the subgroup analysis of patients with ACS. This discrepancy could be attributed to the relatively small sample of 236 participants and the short follow-up period of that study. Furthermore, prior studies have investigated the prognostic influence of the TyG index on 1-year MACE and the coronary burden in patients with CAD accompanied by end-stage renal disease [27, 28]. The existing research has revealed a U-shaped correlation between TyG and both long-term mortality and MACE among hospitalized patients with coronary heart disease [29]. Furthermore, TyG has also been demonstrated to be associated with coronary microvascular dysfunction and MACE in patients with CKD [30]. These studies determined that the association was mostly driven by all-cause mortality rather than other CV events, consistent across the ACS subgroup and partially coinciding with the current study results. Notably, in critically ill ACS patients, acute stress responses such as adrenal hormone release may significantly elevate FBG [21, 31], thereby diminishing its discriminative capacity as an independent predictor. In contrast, the TyG index, which integrates the interaction between TG and FBG, may more stably reflect the chronic metabolic state of insulin resistance. Although this study did not directly compare the predictive ability of TyG versus FBG for in-hospital events, previous studies have demonstrated the consistency of the TyG index in both acute-phase and long-term prognoses [11, 26]. Future research could further explore the unique value of TyG in risk stratification during the acute phase.
Given that IR is a ubiquitous characteristic in cardio-renal disease, it emerges as a pivotal point of intersection where these pathologies converge and potentially interact. However, the mechanisms of IR in patients with both ACS and CKD remain unclear. Chronic IR, characterized by beta-cell dysfunction, is implicated in the progression from MetS to diabetes, amplifying considerable risk for CV and kidney interaction [32]. To further investigate this, a subgroup analysis focusing on MetS was conducted and revealing no significant interaction between the TyG index and MetS. Moreover, fibrinolysis and platelet aggregation driven by IR-induced changes in insulin and glucose may contribute to developing thrombotic events [33]. IR has also been reported to promote the production and release of pro-thrombotic factors, inflammatory factors, and oxidative stress markers [34], which are the established hallmarks of endothelial dysfunction, inflammation, and oxidative stress. These alterations, in turn, mediate the formation and rupture of atherosclerotic plaques [34–36]. Given the conflicting effects of ACEI on insulin sensitivity and the known metabolic benefits of ARBs in regulating glucose and lipid metabolism [37], it is crucial to consider the potential impact of ACEI or ARBs on TyG index, in light of their prevalence in treating renal, cardiovascular diseases, as well as diabetes. Therefore, we conducted subgroup and correlation analyses and found no significant interaction between the use of ACEI or ARB and the TyG index in patients with ACS and CKD.
In the subgroup statistics, the prognostic impact of TyG was more pronounced in women than men; however, no significant interaction was detected. These results partially align with earlier study findings that demonstrated stronger significant associations in females than in males [38]. In contrast, other investigations have observed the relationship to be greater in males than in females [27, 31], although no significant interaction was established. Therefore, these results imply that gender may not influence the value of the TyG index. Recently, researchers have proposed a concept of CKM syndrome, in which IR has been identified as the critical underlying mechanism [14, 15]. Thus, we further investigated this aspect by conducting a MetS subgroup analysis and found the consistent impact of TyG index, indicating that the prognostic impact persisted even in patients without MetS. Furthermore, the TyG index was found to interact with eGFR level and dialysis status in terms of all-cause mortality, implying that an elevated TyG index may not serve as a predictor of mortality in individuals with an eGFR of less than 30 mL/min/1.73 m2 or who are undergoing dialysis. Several mechanisms may explain this study finding. For example, the unclear benefits of statin administration in those with severe renal impairment could modify the prognostic value of TyG index, even after adjusting the statin use [2]. Moreover, blood glucose levels may become prone to fluctuate after and during dialysis due to factors such as insulin retention, the administration of glucose-containing dialysis solutions, and anti-coagulant drugs [39, 40]. Additionally, a previous cohort study found that decreased eGFR mediated approximately 30% of the linkage between TyG index and CV diseases [25]. Consequently, this mediating effect may be attenuated in the subgroup with generally severely impaired renal function, ultimately affecting the risk impact of the TyG index. Furthermore, dyslipidemia, one of the hallmarks of CKD, is aggravated by declining kidney function [41]. In this condition, the TG levels are elevated in the early stages of CKD and peak during dialysis treatment [42]. All these findings imply that renal function could alter the prognostic impact of TyG.
The GRACE risk score has been a widely accepted tool for decades, used extensively in prognosis stratification to predict mortality and MACE among patients with ACS (0.16). Given that the GRACE score incorporates serum creatinine and cardiac markers, this risk score may increase with declining renal function. In line with this notion, prior studies confirmed that this prediction model has an impaired discrimination ability in CKD, particularly among dialysis patients [43]. In terms of discrimination and reclassification performance, this study suggested that integrating TyG with the GRACE score further strengthens the predictive performance of MACE and all-cause mortality, partially supporting earlier study findings [27, 44]. This current research examined the additional predictive ability of the TyG index among ACS patients with CKD, offering metabolic perspectives in the context of patient prognosis identification. Furthermore, the study emphasized the significance of physicians integrating the TyG index into their clinical practice to improve prognosis identification as well as devising subsequent secondary preventive strategies.
However, it is crucial to acknowledge several limitations inherent in the current study. Firstly, the retrospective design of the study introduces inherent limitations, including difficulties in establishing causality and controlling for all potential variables. Further research, including prospective studies, is needed to validate and extend our observations. To evaluate the influence of potential unmeasured confounding, we conducted an E-value analysis, which suggests that it is unlikely for an unmeasured confounder to exert a more significant effect on the primary outcome than the TyG index. Second, the sample size in the present research is modest, possibly limiting the robustness of the statistical analyses performed. Thirdly, this study rely on baseline TyG index measurements. Repeated TyG assessments during hospitalization might more comprehensively reflect dynamic metabolic stress, thereby optimizing risk stratification. Future prospective studies could investigate the trajectory of TyG and its prognostic implications, particularly in ACS patients with CKD. Lastly, notwithstanding the comprehensive subgroup analyses conducted in this study, residual confounding due to unmeasured covariates or unaccounted variables may persist, potentially influencing the interpretation and validity of our conclusions. Therefore, future studies with larger, more diverse populations, and incorporating longitudinal data on potential confounders, are needed to validate and extend our results.
Conclusion
In conclusion, the present results indicate the TyG was associated with mortality and MACE among ACS patients combined with CKD. Incorporating the TyG index into clinical settings significantly enhances the ability to identify patients who are susceptible to death or MACE. Consequently, physicians are encouraged to monitor the TyG index of patients with ACS and CKD from a metabolic view.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
We appreciate all the participants who contributed to this study.
Author contributions
FW: Funding acquisition; conceptualization: Ideas, formulation. WDH, CHS: Writing-original draft, investigation: Conducting the research and investigation process, supervision. WCN: Methodology, writing-original draft, formal analysis, Data supervision. YFZ: Writing-review and editing, Project administration.
Funding
This work was supported by the Zhejiang Provincial Science and Technology Department Research and Development Project (2022C03160) and Traditional Chinese Medicine of Zhejiang Provincial Science and Technology Program Project (GZY-ZJ-KJ-24037).
Data availability
The raw data supporting the conclusion of this article will be made available by the authors under reasonable request.
Declarations
Ethics approval and consent to participate
The First Affiliated Hospital of Wenzhou Medical University’s Ethics Committee in Clinical Research reviewed and approved the studies that involved human participants. The informed consent requirement was waived by the Ethics Committee of the First Affiliated Hospital of Wenzhou Medical University for this retrospective study.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Yongfu Zhu, Email: 656541775@qq.com.
Weicheng Ni, Email: nwc980505@163.com.
References
- 1.Sarnak MJ, Amann K, Bangalore S, et al. Chronic kidney disease and coronary artery disease: JACC State-of-the-Art review. J Am Coll Cardiol. 2019;74(14):1823–38. 10.1016/j.jacc.2019.08.1017 [DOI] [PubMed] [Google Scholar]
- 2.Thygesen K, Alpert JS, Jaffe AS, Chaitman BR, Bax JJ, Morrow DA, et al. Fourth universal definition of myocardial infarction (2018). J Am Coll Cardiol. 2018;72:2231–64. 10.1016/j.jacc.2018.08.1038 [DOI] [PubMed] [Google Scholar]
- 3.Go AS, Chertow GM, Fan D, McCulloch CE, Hsu CY. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med. 2004;351:1296–305. [DOI] [PubMed] [Google Scholar]
- 4.Charytan D, Kuntz RE. The exclusion of patients with chronic kidney disease from clinical trials in coronary artery disease. Kidney Int. 2006;70(11):2021–30. 10.1038/sj.ki.5001934 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Smilowitz NR, Gupta N, Guo Y, Mauricio R, Bangalore S. Management and outcomes of acute myocardial infarction in patients with chronic kidney disease. Int J Cardiol. 2017;227:1–7. 10.1016/j.ijcard.2016.11.026 [DOI] [PubMed] [Google Scholar]
- 6.Lebovitz HE. Insulin resistance: definition and consequences. Exp Clin Endocrinol Diabetes. 2001;109(Suppl 2):S135–48. 10.1055/s-2001-18576 [DOI] [PubMed] [Google Scholar]
- 7.DeFronzo RA, Tobin JD, Rowe JW, Andres R. Glucose intolerance in uremia. Quantification of pancreatic beta cell sensitivity to glucose and tissue sensitivity to insulin. J Clin Invest. 1978;62(2):425–35. 10.1172/JCI109144 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Guerrero-Romero F, Simental-Mendía LE, González-Ortiz M, et al. The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp. J Clin Endocrinol Metab. 2010;95(7):3347–51. 10.1210/jc.2010-0288 [DOI] [PubMed] [Google Scholar]
- 9.Lopez-Jaramillo P, Gomez-Arbelaez D, Martinez-Bello D, et al. Association of the triglyceride glucose index as a measure of insulin resistance with mortality and cardiovascular disease in populations from five continents (PURE study): a prospective cohort study. Lancet Healthy Longev. 2023;4(1):e23–33. 10.1016/S2666-7568(22)00247-1 [DOI] [PubMed] [Google Scholar]
- 10.Ni W, Jiang R, Xu D et al. Association between insulin resistance indices and outcomes in patients with heart failure with preserved ejection fraction. Cardiovasc Diabetol. 2025;24(1):32. Published 2025 Jan 22. 10.1186/s12933-025-02595-x [DOI] [PMC free article] [PubMed]
- 11.Cai W, Xu J, Wu X et al. Association between triglyceride-glucose index and all-cause mortality in critically ill patients with ischemic stroke: analysis of the MIMIC-IV database. Cardiovasc Diabetol. 2023;22(1):138. Published 2023 Jun 13. 10.1186/s12933-023-01864-x [DOI] [PMC free article] [PubMed]
- 12.Tarantino G, Crocetto F, Di Vito C, et al. Association of NAFLD and insulin resistance with Non metastatic bladder Cancer patients: A Cross-Sectional retrospective study. J Clin Med. 2021;10(2):346. 10.3390/jcm10020346. Published 2021 Jan 18. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Fritz J, Bjørge T, Nagel G, et al. The triglyceride-glucose index as a measure of insulin resistance and risk of obesity-related cancers. Int J Epidemiol. 2020;49(1):193–204. 10.1093/ije/dyz053 [DOI] [PubMed] [Google Scholar]
- 14.Ndumele CE, Neeland IJ, Tuttle KR, et al. A synopsis of the evidence for the science and clinical management of Cardiovascular-Kidney-Metabolic (CKM) syndrome: A scientific statement from the American heart association. Circulation. 2023;148(20):1636–64. 10.1161/CIR.0000000000001186 [DOI] [PubMed] [Google Scholar]
- 15.Ndumele CE, Rangaswami J, Chow SL, et al. Cardiovascular-Kidney-Metabolic health: A presidential advisory from the American heart association. Circulation. 2023;148(20):1606–35. 10.1161/CIR.0000000000001184 [DOI] [PubMed] [Google Scholar]
- 16.Eagle KA, Lim MJ, Dabbous OH, et al. A validated prediction model for all forms of acute coronary syndrome: estimating the risk of 6-month postdischarge death in an international registry. JAMA. 2004;291(22):2727–33. 10.1001/jama.291.22.2727 [DOI] [PubMed] [Google Scholar]
- 17.Caccamo G, Bonura F, Bonura F, et al. Insulin resistance and acute coronary syndrome. Atherosclerosis. 2010;211(2):672–5. 10.1016/j.atherosclerosis.2010.03.033 [DOI] [PubMed] [Google Scholar]
- 18.Jia W, Weng J, Zhu D, et al. Standards of medical care for type 2 diabetes in China 2019. Diabetes Metab Res Rev. 2019;35(6):e3158. 10.1002/dmrr.3158 [DOI] [PubMed] [Google Scholar]
- 19.VanderWeele TJ, Ding P. Sensitivity analysis in observational research: introducing the E-Value. Ann Intern Med. 2017;167(4):268–74. 10.7326/M16-2607 [DOI] [PubMed] [Google Scholar]
- 20.Mao Q, Zhou D, Li Y, Wang Y, Xu SC, Zhao XH. The Triglyceride-Glucose index predicts coronary artery disease severity and cardiovascular outcomes in patients with Non-ST-Segment elevation acute coronary syndrome. Dis Markers. 2019;2019:6891537. 10.1155/2019/6891537. Published 2019 Jun 11. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Zhao HW, Wang Y, Wang CF, Meng QK. Association between triglyceride glucose index and adverse clinical outcomes in patients with acute myocardial infarction and LDL-C ≤ 1.8 mmol/l who underwent percutaneous coronary intervention: a prospective cohort study. Front Endocrinol (Lausanne). 2024;14:1323615. 10.3389/fendo.2023.1323615. Published 2024 Jan 18. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Dong S, Zhao Z, Huang X, et al. Triglyceride-glucose index is associated with poor prognosis in acute coronary syndrome patients with prior coronary artery bypass grafting undergoing percutaneous coronary intervention. Cardiovasc Diabetol. 2023;22(1):286. 10.1186/s12933-023-02029-6. Published 2023 Oct 27. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Kunutsor SK, Seidu S, Kurl S, Laukkanen JA. Baseline and usual triglyceride-glucose index and the risk of chronic kidney disease: a prospective cohort study. Geroscience. 2024;46(3):3035–46. 10.1007/s11357-023-01044-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Lv L, Xiong J, Huang Y, He T, Zhao J. Association between the triglyceride glucose index and All-Cause mortality in critically ill patients with acute kidney injury. Kidney Dis (Basel). 2023;10(1):69–78. 10.1159/000535891. Published 2023 Dec 22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Cui C, Liu L, Zhang T, et al. Triglyceride-glucose index, renal function and cardiovascular disease: a National cohort study. Cardiovasc Diabetol. 2023;22(1):325. 10.1186/s12933-023-02055-4. Published 2023 Nov 28. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Ye Z, An S, Gao Y, et al. Association between the triglyceride glucose index and in-hospital and 1-year mortality in patients with chronic kidney disease and coronary artery disease in the intensive care unit. Cardiovasc Diabetol. 2023;22(1):110. 10.1186/s12933-023-01843-2. Published 2023 May 13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Xie E, Ye Z, Wu Y et al. The triglyceride-glucose index predicts 1-year major adverse cardiovascular events in end-stage renal disease patients with coronary artery disease. Cardiovasc Diabetol. 2023;22(1):292. Published 2023 Oct 27. 10.1186/s12933-023-02028-7 [DOI] [PMC free article] [PubMed]
- 28.Xie E, Ye Z, Wu Y, et al. Association of triglyceride-glucose index with coronary severity and mortality in patients on dialysis with coronary artery disease. Eur J Med Res. 2023;28(1):437. 10.1186/s40001-023-01410-1. Published 2023 Oct 17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Hao B, Lyu L, Xu J et al. The relationship between triglyceride-glucose index and prospective key clinical outcomes in patients hospitalised for coronary artery disease. Cardiovasc Diabetol. 2024;23(1):40. Published 2024 Jan 22. 10.1186/s12933-024-02132-2 [DOI] [PMC free article] [PubMed]
- 30.Zhang W, Liu L, Chen H et al. Association between the triglyceride-glucose index and the presence and prognosis of coronary microvascular dysfunction in patients with chronic coronary syndrome. Cardiovasc Diabetol. 2023;22(1):113. Published 2023 May 13. 10.1186/s12933-023-01846-z [DOI] [PMC free article] [PubMed]
- 31.Zhang Y, Ding X, Hua B, et al. High triglyceride-glucose index is associated with adverse cardiovascular outcomes in patients with acute myocardial infarction. Nutr Metab Cardiovasc Dis. 2020;30(12):2351–62. 10.1016/j.numecd.2020.07.041 [DOI] [PubMed] [Google Scholar]
- 32.Wilson PW, D’Agostino RB, Parise H, Sullivan L, Meigs JB. Metabolic syndrome as a precursor of cardiovascular disease and type 2 diabetes mellitus. Circulation. 2005;112(20):3066–72. 10.1161/CIRCULATIONAHA.105.539528 [DOI] [PubMed] [Google Scholar]
- 33.Stegenga ME, van der Crabben SN, Levi M, et al. Hyperglycemia stimulates coagulation, whereas hyperinsulinemia impairs fibrinolysis in healthy humans. Diabetes. 2006;55(6):1807–12. 10.2337/db05-1543 [DOI] [PubMed] [Google Scholar]
- 34.Park K, Gross M, Lee DH, et al. Oxidative stress and insulin resistance: the coronary artery risk development in young adults study. Diabetes Care. 2009;32(7):1302–7. 10.2337/dc09-0259 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Fakhrzadeh H, Sharifi F, Alizadeh M et al. Relationship between insulin resistance and subclinical atherosclerosis in individuals with and without type 2 diabetes mellitus. J Diabetes Metab Disord. 2016;15:41. Published 2016 Oct 1. 10.1186/s40200-016-0263-5 [DOI] [PMC free article] [PubMed]
- 36.Farhan S, Redfors B, Maehara A, et al. Relationship between insulin resistance, coronary plaque, and clinical outcomes in patients with acute coronary syndromes: an analysis from the PROSPECT study. Cardiovasc Diabetol. 2021;20(1):10. 10.1186/s12933-020-01207-0. Published 2021 Jan 7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Wang Y, Qiao S, Han DW, et al. Telmisartan improves insulin resistance: A Meta-Analysis. Am J Ther. 2018;25(6):e642–51. 10.1097/MJT.0000000000000733 [DOI] [PubMed] [Google Scholar]
- 38.Chen J, Wu K, Lin Y, Huang M, Xie S. Association of triglyceride glucose index with all-cause and cardiovascular mortality in the general population. Cardiovasc Diabetol. 2023;22(1):320. 10.1186/s12933-023-02054-5. Published 2023 Nov 22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Frankel AH, Kazempour-Ardebili S, Bedi R, et al. Management of adults with diabetes on the haemodialysis unit: summary of guidance from the joint British diabetes societies and the renal association. Diabet Med. 2018;35(8):1018–26. 10.1111/dme.13676 [DOI] [PubMed] [Google Scholar]
- 40.Tang CC, Tsai JP, Chen YH, Hung SC, Lin YL, Hsu BG. Associations of glucometabolic indices with aortic stiffness in patients undergoing peritoneal Dialysis with and without diabetes mellitus. Int J Mol Sci. 2023;24(23):17094. 10.3390/ijms242317094. Published 2023 Dec 4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Baek J, He C, Afshinnia F, Michailidis G, Pennathur S. Lipidomic approaches to dissect dysregulated lipid metabolism in kidney disease. Nat Rev Nephrol. 2022;18(1):38–55. 10.1038/s41581-021-00488-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Yildirim F, Yildiz AB, Kanbay M. A promising tool: triglyceride-glucose index to stratify the risk of cardiovascular events in chronic kidney disease. Clin Kidney J. 2022;15(9):1653–6. 10.1093/ckj/sfac084. Published 2022 Mar 22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Gurm HS, Gore JM, Anderson FA Jr, et al. Comparison of acute coronary syndrome in patients receiving versus not receiving chronic dialysis (from the global registry of acute coronary events [GRACE] registry). Am J Cardiol. 2012;109(1):19–25. 10.1016/j.amjcard.2011.07.062 [DOI] [PubMed] [Google Scholar]
- 44.Xiong S, Chen Q, Chen X et al. Adjustment of the GRACE score by the triglyceride glucose index improves the prediction of clinical outcomes in patients with acute coronary syndrome undergoing percutaneous coronary intervention. Cardiovasc Diabetol. 2022;21(1):145. Published 2022 Aug 5. 10.1186/s12933-022-01582-w [DOI] [PMC free article] [PubMed]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The raw data supporting the conclusion of this article will be made available by the authors under reasonable request.