The triglyceride-glucose index played an important role in prognosis in critically ill patients with heart disease


 Background

As an alternative method to evaluate insulin resistance (IR), triglyceride-glucose index (TyG) was shown to be related to the severity and prognosis of cardiovascular diseases. The main objective of this study was to explore the association between TyG and in-hospital mortality in critically ill patients with heart disease
Method:

TyG was calculated as previously reported: ln [fasting TGs (mg/dL) * FBG (mg/dL)/2]. All patients were divided into four different categories based on TyG quartiles. Primary outcome was in-hospital mortality. Binary logistic regression analysis was performed to determine the independent effect of TyG.
Result

4839 critically ill patients with heart disease were included. In-hospital mortality increased as TyG quartiles increased (Quartile 4 vs Quartile 1: 12.1 vs 5.3, P < 0.001). Even after adjusting for confounding variables, TyG was still independently associated with the increased risk of in-hospital mortality in critically ill patients with heart disease (Quartile 4 vs Quartile 1: OR, 95% CI: 2,43, 1.79–3.31, P < 0.001, P for trend < 0.001). However, we did not observe the association between increased TyG and the risk of mortality in patients with diabetes. Furthermore, as TyG quartiles increased, the length of intensive care unit (ICU) stay was prolonged (Quartile 4 vs Quartile 1: 2.3, 1.3–4.9 vs 2.1, 1.3–3.8, P = 0.007). And the significant interactions were not found in most subgroups.
Conclusion

TyG was independently correlated with in-hospital mortality in critically ill patients with heart disease.


Introduction
Cardiovascular disease (CVD) is the leading cause of morbidity and mortality worldwide in contemporary society. Especially, in patients with severe CVD, the mortality was greatly increased [1][2]. In order to reduce the mortality of serious CVD patients, coronary artery care unit (CCU) and cardiac intensive care unit (CICU) came into being. After decades of development, they eventually focused on the management of patients with severe CVD which needed meticulous care and targeted treatment [3][4]. Nowadays, the status of CCU and CICU are increasingly important, besides, a variety of studies target on how to predict and improve prognosis of patients. As for clinicians, easily accessible and reliable prognostic indicators for critically ill patients with heart disease are always welcomed. blocker(ACEI/ARB), statin), acute physiology score(APS) and Acute Physiology and Chronic Health Evaluation IV (APACHE IV) [21].
2.3. Grouping and outcomes. All enrolled patients were divided into four different categories based on the TyG quartiles. The primary outcome was in-hospital mortality. Secondary outcomes were length of intensive care unit (ICU) stay and length of hospital stay.
2.4. Statistical analysis. Normally distributed continuous variables were expressed as mean ± standard deviation (SD) and compared between groups using analysis of variance. Skewed data were expressed as median and interquartile range (IQR) and compared using Kruskal-Wallis test. Categorical variables were expressed as number (percentage) and identi ed signi cant heterogeneity in the frequencies using Chi-square test.
The relationship between TyG and in-hospital mortality was identi ed by binary logistic regression analysis and the results were expressed as odds ratio (OR) and 95% con dence interval (CI). P for trend was calculated. Covariates were selected by statistical analysis and clinical doubt to modulate the outcome. The curves that conformed to the general trend were plotted through local weighted regression (Lowess). Subgroup analysis was used to determine the relationship between TyG and in-hospital mortality in different subgroups, P for interaction was calculated. A two-tailed P value < 0.05 was considered statistically signi cant. All data analysis were performed by Stata V.15.1.

Result
3.1. Subjects and baseline characteristics. 4839 patients were analyzed (Fig. 1). According to TyG quartiles, all patients were divided into four groups: TyG < 8.51 (n = 1201), 8.51 ≤ TyG < 8.92 (n = 1221), 8.92 ≤ TyG < 9.37 (n = 1214), TyG ≥ 9.37 (n = 1203). The characteristics of different TyG groups were summarized in Table 1, patients with high TyG levels had the following characteristics: elder, Caucasian, higher blood pressure and higher body mass index. Moreover, patients in higher PLR quartiles also tended to present more diagnoses and comorbidities of coronary artery disease, acute coronary syndrome, STEMI, NSTEMI, cardiac arrest, shock, diabetes, respiratory failure, chronic kidney disease, acute kidney injury, sepsis whereas less congestive heart failure, arrhythmias, bradycardia, atrial brillation, cardiomyopathy, valve disease, COPD. Besides, Table 1 indicated that as TyG quartiles increased, white blood cell, lymphocyte percentage, red blood cell, hemoglobin, hematocrit, platelet, glucose, triglyceride, creatinine, blood nitrogen urea, potassium values tended to increase, while monocyte and neutrophil percentage, sodium values tended to decrease. Of note, there was no statistically signi cant difference in administration of medication among the TyG categories. Furthermore, patients with higher TyG index had signi cantly higher APS score which was used to evaluate the severity of ICU patients and predict their prognosis (Table 1). 3.2. Association between PLR and outcomes. Overall, in-hospital mortality rate was 8.5%. As TyG quartiles increased, in-hospital mortality increased signi cantly (Quartile 4 vs Quartile 1: 12.1 vs 5.3, P < 0.001) ( Table 2). In unadjusted logistic regression analysis, there was a positive association between TyG and in-hospital mortality (Quartile 4 vs Quartile 1: OR, 95% CI: 2,43, 1.79-3.31, P < 0.001, P for trend < 0.001). In model 2, after adjusting for age, gender and ethnicity, higher TyG quartiles were markedly related to the increased risk of in-hospital mortality (Quartile 4 vs Quartile 1: OR, 95% CI: 2.90, 2.12-3.96, P < 0.001, P for trend < 0.001). In model 3, adjusted for more confounding variables, the TyG index was still independently associated with the increased risk of in-hospital mortality (Quartile 4 vs Quartile 1: OR, 95% CI: 1.83, 1.27-2.64, P < 0.001, P for trend < 0.001). Furthermore, when TyG was considered as a continuous variable in the model for analysis, we observed that for each unit increase in the TyG index, the risk of in-hospital mortality increased approximately 0.35-fold in Model 1 (p < 0.001), 0.43-fold in Model 2 (p < 0.001), 0.23-fold in Model 3 (p < 0.001) respectively (Table 3). Interestingly, of the 4069 patients who didn't suffer from diabetes, we found that TyG had a signi cant effect on in-hospital mortality either in logistics regression with or without adjusting for confounding variables, which was consistent with the conclusion drawn in Table 3. Conversely, as we screened patients with diabetes (N = 770) for logistics regression analysis, no signi cant correlation has been shown between TyG and inhospital mortality with or without adjusting for confounding risk factors (Table 4). Besides, from Lowess curve in Fig. 2, we found that the relationship between TyG and mortality was linear. Speci cally, as TyG increased, in-hospital mortality increased.    Models were derived from binary logistic regression analysis. P for trend was calculated using binary logistic analysis to determine whether there was a trend when TyG was included as a grouping variable in the model (Quartile 1-4). When TyG was included as a grouping variable in the model, P values were calculated using binary logistic analysis to determine whether there was a relationship between TyG quartiles and in-hospital mortality with Quartile1 serving as the reference group. When TyG was included as a continuous variable in the model, P values were calculated using binary logistic analysis to determine whether there was a relationship between TyG and in-hospital mortality. In DM group:  Table 2). Moreover, we drew the box plot to re ect the relationship between TyG and length of ICU and hospital stay more intuitively. The obvious association between TyG and length of ICU stay was indicated (Fig. 3).
3.3. Subgroup analysis. Patients complicated by arrhythmias or atrial brillation had higher risk of inhospital mortality for TyG while patients with sepsis had lower risk of in-hospital mortality (Table 5). Binary logistic regression analysis was used and results were presented as OR (odds ratio) and 95% CI (con dence interval). P for interaction was calculated using binary logistic analysis to determine whether there is interaction between different subgroups and TyG quartiles. Abbreviation: STEMI: STelevation myocardial infarction; NSTEMI: non-ST-elevation myocardial infarction; COPD: chronic obstructive pulmonary disease; triglyceride-glucose index; ACEI: angiotensin-converting enzyme inhibitor; ARB: angiotensin receptor blocker; APS: acute physiology score; APACHE IV: Acute Physiology and Chronic Health Evaluation IV. Binary logistic regression analysis was used and results were presented as OR (odds ratio) and 95% CI (con dence interval). P for interaction was calculated using binary logistic analysis to determine whether there is interaction between different subgroups and TyG quartiles. Abbreviation: STEMI: STelevation myocardial infarction; NSTEMI: non-ST-elevation myocardial infarction; COPD: chronic obstructive pulmonary disease; triglyceride-glucose index; ACEI: angiotensin-converting enzyme inhibitor; ARB: angiotensin receptor blocker; APS: acute physiology score; APACHE IV: Acute Physiology and Chronic Health Evaluation IV. Binary logistic regression analysis was used and results were presented as OR (odds ratio) and 95% CI (con dence interval). P for interaction was calculated using binary logistic analysis to determine whether there is interaction between different subgroups and TyG quartiles. Abbreviation: STEMI: STelevation myocardial infarction; NSTEMI: non-ST-elevation myocardial infarction; COPD: chronic obstructive pulmonary disease; triglyceride-glucose index; ACEI: angiotensin-converting enzyme inhibitor; ARB: angiotensin receptor blocker; APS: acute physiology score; APACHE IV: Acute Physiology and Chronic Health Evaluation IV. Binary logistic regression analysis was used and results were presented as OR (odds ratio) and 95% CI (con dence interval). P for interaction was calculated using binary logistic analysis to determine whether there is interaction between different subgroups and TyG quartiles. Abbreviation: STEMI: STelevation myocardial infarction; NSTEMI: non-ST-elevation myocardial infarction; COPD: chronic obstructive pulmonary disease; triglyceride-glucose index; ACEI: angiotensin-converting enzyme inhibitor; ARB: angiotensin receptor blocker; APS: acute physiology score; APACHE IV: Acute Physiology and Chronic Health Evaluation IV. Binary logistic regression analysis was used and results were presented as OR (odds ratio) and 95% CI (con dence interval). P for interaction was calculated using binary logistic analysis to determine whether there is interaction between different subgroups and TyG quartiles. Abbreviation: STEMI: STelevation myocardial infarction; NSTEMI: non-ST-elevation myocardial infarction; COPD: chronic obstructive pulmonary disease; triglyceride-glucose index; ACEI: angiotensin-converting enzyme inhibitor; ARB: angiotensin receptor blocker; APS: acute physiology score; APACHE IV: Acute Physiology and Chronic Health Evaluation IV. Binary logistic regression analysis was used and results were presented as OR (odds ratio) and 95% CI (con dence interval). P for interaction was calculated using binary logistic analysis to determine whether there is interaction between different subgroups and TyG quartiles. Abbreviation: STEMI: STelevation myocardial infarction; NSTEMI: non-ST-elevation myocardial infarction; COPD: chronic obstructive pulmonary disease; triglyceride-glucose index; ACEI: angiotensin-converting enzyme inhibitor; ARB: angiotensin receptor blocker; APS: acute physiology score; APACHE IV: Acute Physiology and Chronic Health Evaluation IV. Binary logistic regression analysis was used and results were presented as OR (odds ratio) and 95% CI (con dence interval). P for interaction was calculated using binary logistic analysis to determine whether there is interaction between different subgroups and TyG quartiles. Abbreviation: STEMI: STelevation myocardial infarction; NSTEMI: non-ST-elevation myocardial infarction; COPD: chronic obstructive pulmonary disease; triglyceride-glucose index; ACEI: angiotensin-converting enzyme inhibitor; ARB: angiotensin receptor blocker; APS: acute physiology score; APACHE IV: Acute Physiology and Chronic Health Evaluation IV. Binary logistic regression analysis was used and results were presented as OR (odds ratio) and 95% CI (con dence interval). P for interaction was calculated using binary logistic analysis to determine whether there is interaction between different subgroups and TyG quartiles. Abbreviation: STEMI: STelevation myocardial infarction; NSTEMI: non-ST-elevation myocardial infarction; COPD: chronic obstructive pulmonary disease; triglyceride-glucose index; ACEI: angiotensin-converting enzyme inhibitor; ARB: angiotensin receptor blocker; APS: acute physiology score; APACHE IV: Acute Physiology and Chronic Health Evaluation IV. Binary logistic regression analysis was used and results were presented as OR (odds ratio) and 95% CI (con dence interval). P for interaction was calculated using binary logistic analysis to determine whether there is interaction between different subgroups and TyG quartiles. Abbreviation: STEMI: STelevation myocardial infarction; NSTEMI: non-ST-elevation myocardial infarction; COPD: chronic obstructive pulmonary disease; triglyceride-glucose index; ACEI: angiotensin-converting enzyme inhibitor; ARB: angiotensin receptor blocker; APS: acute physiology score; APACHE IV: Acute Physiology and Chronic Health Evaluation IV. Binary logistic regression analysis was used and results were presented as OR (odds ratio) and 95% CI (con dence interval). P for interaction was calculated using binary logistic analysis to determine whether there is interaction between different subgroups and TyG quartiles. Abbreviation: STEMI: STelevation myocardial infarction; NSTEMI: non-ST-elevation myocardial infarction; COPD: chronic obstructive pulmonary disease; triglyceride-glucose index; ACEI: angiotensin-converting enzyme inhibitor; ARB: angiotensin receptor blocker; APS: acute physiology score; APACHE IV: Acute Physiology and Chronic Health Evaluation IV. Binary logistic regression analysis was used and results were presented as OR (odds ratio) and 95% CI (con dence interval). P for interaction was calculated using binary logistic analysis to determine whether there is interaction between different subgroups and TyG quartiles. Abbreviation: STEMI: STelevation myocardial infarction; NSTEMI: non-ST-elevation myocardial infarction; COPD: chronic obstructive pulmonary disease; triglyceride-glucose index; ACEI: angiotensin-converting enzyme inhibitor; ARB: angiotensin receptor blocker; APS: acute physiology score; APACHE IV: Acute Physiology and Chronic Health Evaluation IV.

Discussion
This study a rmed the relationship between TyG and in-hospital mortality in critically ill patients with heart disease. (1) TyG index was a strong indicator of in-hospital mortality in critically ill patients with heart disease, even after adjusting for possible confounding variables. Whereas, we failed to reveal a signi cant association between the TyG index and in-hospital mortality in patients with diabetes. Previous studies have indicated that IR was strongly associated with the development and prognosis of cardiovascular disease [22][23][24]. The underlying biological mechanism might be close association between IR and endothelial dysfunction which was able to cause cardiovascular disease [25][26][27].
Moreover, IR might disrupt the metabolic stability of energy substrates used by the heart, resulting in fatty toxicity during cardiac metabolism and eventually led to major adverse cardiac events [28]. As we know, the euglycemic-hyperinsulinemic clamp is the gold standard method for the diagnosis of IR [29]. However, due to the high cost and complex operation of this method, it is relatively di cult to carry out in practical clinical application. The homeostasis model assessment of insulin resistance (HOMA-IR) is a substitutive method for IR evaluation [30]. While it requires insulin concentration which is not routine clinical examination item. In this respect, TyG index which is calculated by fasting TGs and glucose is more readily available in clinical practice. And it has been proven to have a good predictive ability on IR compared with the above-mentioned two methods [31][32]. Therefore, as a good substitute indicator for IR, TyG index may be a risk factor which associated with prognosis of cardiovascular disease.
TyG has been extensively demonstrated to be signi cantly related to the development of a variety of diseases in former studies. A recent meta-analysis which included 13 cohort studies con rmed that TyG index was strongly related to the incidence of diabetes [33]. Furthermore, higher TyG index has been indicated to be associated with the increased risk of ischemic stroke [34]. Similarly, a large number of studies have also con rmed the relationship between TyG and CVD. A previous prospective cohort study proved that higher TyG index was related to the increased complexity of coronary lesions and the risk of worse outcomes in patients with NSTE-ACS [35]. Zhao  This study drawn a similar conclusion that increased TyG was independently related to the in-hospital mortality in critically ill patients with heart disease, providing evidence for the use of TyG in patients with severe cardiovascular disease. While, when conducting multiple logistic regression analysis, there was no signi cant association between TyG and in-hospital mortality among patients with diabetes in model 1-3. The discrepancy might be explained by the fact that the small number of patients with diabetes in the cohort.
Interestingly, gender differences appeared to have an impact on the prediction of adverse outcomes of TyG. The previous study has shown that the ability of TyG to predict adverse cardiovascular events was better in women than men when TyG > 9.53 [19]. The plausible explanation might be that female patients with diabetes had a higher incidence of cardiovascular disease [39]. However, in the gender subgroup in our study, we failed to nd the obvious interaction (P = 0.659). The explain might be that patients enrolled in this study have clearly been diagnosed with cardiovascular disease and mortality of those was extremely high. Therefore, sex differences were attenuated.
Through the Lowess curve, we found that in-hospital mortality increased as the increase of TyG value.
This was consistent with the conclusion that TyG was an independent predictor when considered as a continuous variable in multivariate logistic regression, which recon rmed the reliability of TyG application in critically ill patients with heart disease.
In addition, higher TyG quartiles were associated with the increased length of ICU stay, which brought the psychological, physical, and nancial burden on patients. Most of critically ill patients with heart disease have limited mobility so that complex clinical examination cannot be performed. In this circumstance, some of complex predictive scores can't be calculated. Therefore, easily accessible indicators like TyG are more cost-effective and important for ICU patients.

Limitation
This study is a single-center retrospective cohort study. Due to the limitations of the retrospective study, selection bias and recall bias cannot be avoided, and the causal relationship cannot be determined.
Furthermore, in patients with diabetes, the accuracy of the model is reduced because of the small sample size. And we are not able to demonstrate whether the appropriate treatment which aimed to reduce the TyG value related to the lower incidence of adverse clinical outcomes.

Conclusion
To summarize, the results indicated that TyG was an independent predictor of in-hospital mortality in critically ill patients with heart disease. And through multivariate logistic regression, the in-hospital mortality increased signi cantly as TyG quartiles increased. When considered as a continuous variable, TyG has been proven to signi cantly related to adverse events. Furthermore, high TyG was associated with prolonged ICU stay length. This study was exempted from institutional review Board approval for the following reasons: (1) retrospective design, which was lack of direct patient intervention; (2) Privacert certi cation of reidenti cation risk conforming to safe harbor standards for security protocols (Cambridge, MA) (HIPAA Certi cation no. 1031219-2).

Supplementary material
The original data from the study is in the supplementary le.

Method statement
All methods were carried out in accordance with relevant guidelines and regulations.

Data Availability
The data used in this study was from eICU Collaborative Research Database [24], which is available at: https://doi.org/10.13026/C2WM1R. The author was approved to access to the database through Protecting Human Research Participants exam (certi cate number: 9728458).

Figure 1
Flow chart of study population. Abbreviation: CCU: coronary artery care unit; CICU: cardiac intensive care unit.

Figure 2
Association between the triglyceride-glucose index and in-hospital mortality presented through Lowess smoothing.