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The association between triglyceride-glucose index and its combination with systemic inflammation indicators and all-cause and cardiovascular mortality in the general US population: NHANES 1999–2018

Lipids in Health and Disease volume 23, Article number: 289 (2024) Cite this article

Abstract

Background

The correlation between the triglyceride-glucose (TyG) index and mortality in the general population remains controversial, with inconsistent conclusions emerging from different studies.

Objective

This study aims to investigate whether there is an association between the TyG index and mortality in the general population in the United States, and to explore whether a new index combining the TyG index with systemic inflammation indicators can better predict all-cause and cardiovascular mortality risks in the general population than using the TyG index alone.

Methods

Calculate the systemic inflammation indicators and TyG index for each participant based on their complete blood count, as well as their triglyceride and glucose levels in a fasting state. TyG-inflammation indices were obtained by multiplying the TyG index with systemic inflammation indicators (TyG-NLR, TyG-MLR, TyG-lgPLR, TyG-lgSII, and TyG-SIRI). Based on the weighted Cox proportional hazards model, assess whether the TyG and TyG-Inflammation indices are associated with mortality risk in the general population. Restricted cubic splines (RCS) are used to clarify the dose-response relationship between the TyG and TyG-Inflammation indices and mortality, and to visualize the results. Time-dependent receiver operating characteristic (ROC) curves are used to evaluate the accuracy of the TyG and TyG-Inflammation indices in predicting adverse outcomes.

Results

This study included 17,118 participants. Over a median follow-up period of 125 months, 2595 patients died. The TyG index was not found to be related to mortality after adjusting for potentially confounding factors. However, the TyG-inflammation indices in the highest quartile (Q4), except for TyG-lgPLR, were significantly associated with both all-cause and cardiovascular mortality, compared to those in the lowest quartile (Q1). Among them, TyG-MLR and TyG-lgSII showed the strongest correlations with all-cause mortality and cardiovascular mortality. Specifically, compared to their respective lowest quartiles (Q1), participants in the highest quartile (Q4) of TyG-MLR had a 48% increased risk of all-cause mortality (95% CI: 1.23–1.77, P for trend < 0.0001), while participants in the highest quartile (Q4) of TyG-lgSII had a 92% increased risk of cardiovascular mortality (95% CI: 1.31–2.81, P for trend < 0.001). Time-dependent ROC curve analysis showed that the TyG-MLR had the highest accuracy in predicting long-term mortality outcomes.

Conclusions

The TyG-Inflammation indices constructed based on TyG and systemic inflammation indicators are closely related to mortality in the general population and can better predict the risk of adverse outcomes. However, no association between TyG and mortality in the general population was found.

Introduction

Insulin resistance (IR) plays a significant role in the occurrence and development of metabolic diseases such as obesity, hyperlipidemia, hypertension, and diabetes, and has been proven to significantly increase the risk of mortality for patients [1,2,3]. Currently, the hyperinsulinemic-euglycemic clamp (HIEC) is widely regarded as the most reliable method for evaluating insulin resistance (IR). However, its invasive nature and high cost make implementation in daily clinical practice challenging. In 1985, Turner et al. introduced the use of the homeostasis model assessment-insulin resistance (HOMA-IR) as a means to assess IR, thereby streamlining the IR evaluation process to some extent [4]. However, this method requires obtaining the fasting insulin levels of the patients. Further, it cannot be applied to patients receiving exogenous insulin therapy and those with impaired islet β-cell function, which limits its comprehensive implementation in clinical practice. The triglyceride-glucose (TyG) index, calculated based on triglyceride and glucose levels under fasting conditions, has been proven to be another valuable indicator of IR [5,6,7]. Due to its simplicity, ease of access, and low cost, it has been widely used in clinical settings [8, 9]. Multiple studies have investigated the association between the TyG index and mortality in individuals with metabolic disorders [10,11,12,13,14,15], demonstrating its effectiveness and advantages in predicting adverse outcomes in such patients. However, various factors such as gender, age, race, comorbidities, income level, etc. appear to influence the association between TyG and all-cause and cardiovascular mortality in the general population, and different studies have shown inconsistent conclusions [16,17,18,19,20,21,22]. Previous research based on American and Iranian populations indicated that a high TyG index significantly increases the risk of mortality in the general population [17, 21]. However, Liu et al. found through a meta-analysis of 12 related studies that the correlation between the TyG index and mortality is not significant [18]. Therefore, the association between the TyG index and mortality in the general population remains unclear.

Given the simplicity, ease of access, and low cost of the TyG index [23], it would be optimal to use the TyG index in primary care and health screening to assess the mortality risk in the general population. Therefore, we aimed to optimize and improve the TyG index. Considering that both IR and inflammation play crucial roles in exacerbating the progression of metabolic diseases and leading to adverse events, they exhibit synergistic effects in this process [24,25,26]. Accordingly, we attempted to combine the TyG index with inflammatory markers to construct a variety of novel indices (i.e., TyG-inflammation indices), and assess their associations with the risk of all-cause and cardiovascular mortality in the general population. Systemic inflammation indicators are novel indicators that are calculated based on the complete blood count, such as NLR (neutrophil-to-lymphocyte ratio), PLR (platelet-lymphocyte ratio), MLR (monocyte-lymphocyte ratio), SII (systemic immune inflammation index), and SIRI (system inflammation response index). Compared to single inflammatory markers, systemic inflammation indicators presented in the form of ratios are not only more stable, but the cumulative effects of interaction between multiple blood cells increase the predictability of adverse outcomes [27,28,29]. Similar to the TyG index, these markers are stable, easily accessible, inexpensive, and strongly associated with the risk of death in the general population [30,31,32,33,34,35]. Therefore, in this study, we chose to combine systemic inflammation indicators with the TyG index.

Overall, the aim of this longitudinal cohort study was to determine if the TyG-inflammation indices are more effective in predicting the risk of all-cause and cardiovascular mortality in the general US population compared to the TyG index alone.

Methods

Data source and outcome definition

The National Health and Nutrition Examination Survey (NHANES) is a research program led by the Centers for Disease Control and Prevention (CDC) in the United States, aimed at assessing the health of adults and children in the country. It was initially launched in 1960 and has been a continuous program since 1999, conducting annual nationwide surveys on approximately 5,000 individuals. These surveys cover a wide range of data, including demographics, socioeconomics, diet, and health [36]. This study utilized data from 10 cycles of NHANES from 1999 to 2018. Additionally, we linked the NHANES data with the National Death Index (NDI) data to obtain participants’ follow-up information, including follow-up time, survival status, and cause of death. Figure 1 shows the participant screening flowchart.

Fig. 1
figure 1

Flow chart of the sample selection from NHANES 1999–2018

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Calculation of the TyG index, systemic inflammation indicators, and TyG-inflammation indices

The TyG index is obtained by calculating the product of triglyceride and glucose levels in the fasting state of each participant, taking its natural logarithm. Systemic inflammation indicators were calculated based on the complete blood cell counts. Owing to the excessively large base value of platelets, we applied a logarithmic transformation to the PLR and SII. TyG-inflammation indices were obtained by multiplying the TyG index by different systemic inflammation indicators, including the TyG-NLR, TyG-monocyte-MLR, TyG-lgPLR, TyG-lgSII, and TyG-SIRI. The calculation formulas for the above indicators are as follows:

$$\:\mathbf{T}\mathbf{y}\mathbf{G}=\text{l}\text{n}\left[triglyceride\:\right(\text{m}\text{g}/\text{d}\text{L})\times\:fasting\:blood\:glucose\:(\text{m}\text{g}/\text{d}\text{L})/2]$$
$$\:\mathbf{N}\mathbf{L}\mathbf{R}=\text{N}\text{e}\text{u}\text{t}\text{r}\text{o}\text{p}\text{h}\text{i}\text{l}\:\text{C}\text{o}\text{u}\text{n}\text{t}/\text{L}\text{y}\text{m}\text{p}\text{h}\text{o}\text{c}\text{y}\text{t}\text{e}\:\text{C}\text{o}\text{u}\text{n}\text{t}$$
$$\:\mathbf{M}\mathbf{L}\mathbf{R}=\text{M}\text{o}\text{n}\text{o}\text{c}\text{y}\text{t}\text{e}\:\text{C}\text{o}\text{u}\text{n}\text{t}/\text{L}\text{y}\text{m}\text{p}\text{h}\text{o}\text{c}\text{y}\text{t}\text{e}\:\text{C}\text{o}\text{u}\text{n}\text{t}\:$$
$$\:\mathbf{l}\mathbf{g}\mathbf{P}\mathbf{L}\mathbf{R}=\:\text{l}\text{g}(\text{P}\text{l}\text{a}\text{t}\text{e}\text{l}\text{e}\text{t}\:\text{C}\text{o}\text{u}\text{n}\text{t}/\text{L}\text{y}\text{m}\text{p}\text{h}\text{o}\text{c}\text{y}\text{t}\text{e}\:\text{C}\text{o}\text{u}\text{n}\text{t})$$
$$\:\mathbf{l}\mathbf{g}\mathbf{S}\mathbf{I}\mathbf{I}=\text{l}\text{g}(\text{N}\text{e}\text{u}\text{t}\text{r}\text{o}\text{p}\text{h}\text{i}\text{l}\:\:\text{C}\text{o}\text{u}\text{n}\text{t}\:\times\:\:\text{P}\text{l}\text{a}\text{t}\text{e}\text{l}\text{e}\text{t}\:\:\text{C}\text{o}\text{u}\text{n}\text{t}/\text{L}\text{y}\text{m}\text{p}\text{h}\text{o}\text{c}\text{y}\text{t}\text{e}\:\text{C}\text{o}\text{u}\text{n}\text{t})$$
$$\:\mathbf{S}\mathbf{I}\mathbf{R}\mathbf{I}=\text{N}\text{e}\text{u}\text{t}\text{r}\text{o}\text{p}\text{h}\text{i}\text{l}\:\text{C}\text{o}\text{u}\text{n}\text{t}\:\times\:\:\text{M}\text{o}\text{n}\text{o}\text{c}\text{y}\text{t}\text{e}\:\text{C}\text{o}\text{u}\text{n}\text{t}/\text{L}\text{y}\text{m}\text{p}\text{h}\text{o}\text{c}\text{y}\text{t}\text{e}\:\text{C}\text{o}\text{u}\text{n}\text{t}$$
$$\:\mathbf{T}\mathbf{y}\mathbf{G}-\mathbf{N}\mathbf{L}\mathbf{R}=\text{T}\text{y}\text{G}\times\:\text{N}\text{L}\text{R}$$
$$\:\mathbf{T}\mathbf{y}\mathbf{G}-\mathbf{M}\mathbf{L}\mathbf{R}=\text{T}\text{y}\text{G}\times\:\text{M}\text{L}\text{R}$$
$$\:\mathbf{T}\mathbf{y}\mathbf{G}-\mathbf{l}\mathbf{g}\mathbf{P}\mathbf{L}\mathbf{R}=\text{T}\text{y}\text{G}\times\:\text{l}\text{g}\text{P}\text{L}\text{R}$$
$$\:\mathbf{T}\mathbf{y}\mathbf{G}-\mathbf{l}\mathbf{g}\mathbf{S}\mathbf{I}\mathbf{I}=\text{T}\text{y}\text{G}\times\:\text{l}\text{g}\text{S}\text{I}\text{I}$$
$$\:\mathbf{T}\mathbf{y}\mathbf{G}-\mathbf{S}\mathbf{I}\mathbf{R}\mathbf{I}=\text{T}\text{y}\text{G}\times\:\text{S}\text{I}\text{R}\text{I}$$

Covariates

The NHANES provided all the variables used in this study. The variables included sex, age, race, educational level, family socioeconomic status (assessed by the poverty income ratio [PIR]), smoking status, alcohol consumption, medical history (including hypertension, diabetes, hyperlipidemia, and cancer), medication use, body mass index (BMI), complete blood count, and blood biochemistry tests. Hyperlipidaemia is diagnosed by evaluating several parameters, including low-density lipoprotein cholesterol (LDL-C) levels of 130 mg/dL or higher (equivalent to 3.37 mmol/L or above), total cholesterol (TC) levels of 200 mg/dL or higher (5.18 mmol/L or above), triglycerides (TG) levels of 150 mg/dL or higher (1.7 mmol/L or above), and high-density lipoprotein cholesterol (HDL-C) levels below 40 mg/dL for men (less than 1.04 mmol/L) or below 50 mg/dL for women (less than 1.30 mmol/L). Furthermore, the consideration of lipid-lowering drugs is also taken into account when determining hyperlipidaemia [37]. Hypertension is diagnosed by considering various factors, such as self-reported medical history of the illness, current use of blood pressure-lowering medication, and having an average systolic blood pressure of 140 mmHg or higher, and/or an average diastolic blood pressure of 90 mmHg or higher [38]. The diagnostic criteria for diabetes mellitus (DM) include a confirmed diagnosis by a clinician, fasting glucose levels of 7.0 mmol/L or higher, HbA1c levels of 6.5% or above, and/or the current usage of anti-DM drugs. The use of medications and the presence of cancer are determined based on data from questionnaire surveys. The classification of smoking status is as follows: never smokers (individuals who have smoked less than 100 cigarettes in their lifetime), former smokers (individuals who have smoked in the past but have quit smoking now), and current smokers (individuals who have smoked at least 100 cigarettes in their lifetime and are still smoking now) [39]. Alcohol consumption is determined based on specific criteria: heavy drinking is defined as consuming ≥ 3 drinks per day for women, ≥ 4 drinks per day for men, or engaging in binge drinking on ≥ 5 days per month; moderate drinking is characterized by consuming two drinks per day for females, three drinks per day for males, or binge drinking on ≥ 2 days per month; mild drinking is designated for those who do not meet the criteria for heavy or moderate drinking, while never drinking refers to individuals who have consumed < 12 drinks in their lifetime [40].

Statistical analysis

All statistical analyses were conducted following the recommendations of the official NHANES guidelines [41]. Because we used fasting data, we chose fasting subsample weights. The data are presented as the unweighted frequency (weighted percentage) for categorical variables and median (interquartile range) for continuous variables.

We used the Kaplan–Meier method to calculate the survival probability of each subgroup at different quartiles of the TyG index and TyG-inflammation indices and compared the survival differences between groups using the log-rank test. Further, we assessed the association of the TyG index and TyG-inflammation indices with mortality using a weighted Cox proportional hazards model. Three different models were constructed to evaluate the impact of potential confounding factors on this association. Specifically, Model 1 was not adjusted for confounding factors. Model 2 was adjusted for sex and age. Model 3 was further adjusted for race, PIR, educational level, BMI, smoking status, alcohol consumption, hypertension, diabetes mellitus, hyperlipidemia, cancer, lipid-lowering drugs, ALT, and AST, based on Model 2.

We used a restricted cubic spline (RCS) with four knots to assess the dose-response relationship pattern between the TyG index and TyG-inflammation indices and mortality. Additionally, we evaluated the accuracy of the TyG index and TyG-inflammation indices in predicting survival outcomes at different time points by using time-dependent ROC curves. All statistical analyses were performed using R (4.2.2) software, and statistical significance was set at P < 0.05.

Results

Participants characteristics

This study ultimately included 17,118 participants, with 50.94% being male. During a median follow-up period of 125 months, a total of 2,595 participants experienced outcome events. Table 1 shows baseline data for survival group and non-survival group participants. Compared to survivors, non-survivors were characterized by being older; male; non-Hispanic whites; smokers; non-drinkers; having a history of comorbidities and cancer; using lipid-lowering drugs; higher fasting blood glucose, triglyceride, aspartate aminotransferase, white blood cell, neutrophil, and monocyte levels; and lower levels of lymphocytes. Most importantly, all non-survivors had higher TyG and TyG-inflammation indices values than survivors. Supplementary Table 1 summarize the detailed baseline information for each group of participants categorized based on different quartile levels of TyG and TyG-inflammation indices.

Table 1 Characteristics of participants
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Kaplan–Meier survival curves of TyG and TyG-inflammation indices

Without correcting for any potential confounding factors, the groups with different TyG indices had significantly different long-term survival outcomes (all-cause mortality: Supplementary Fig. 1A, P < 0.0001; cardiovascular mortality: Supplementary Fig. 2A, P < 0.0001). Similar results were found in TyG-inflammation indices (all-cause mortality: Supplementary Fig. 1B–F, both P < 0.0001; cardiovascular mortality: Supplementary Fig. 2B–F, both P < 0.001).

Association of TyG and TyG-inflammation indices with all-cause mortality

We used three distinct models to assess the association between TyG and TyG-inflammation indices and the risk of all-cause mortality (Table 2). In the fully adjusted model (Model 3), we found no significant association between TyG (hazard ratio [HR] = 0.93, 95% confidence interval [CI]: 0.76–1.14, P for trend = 0.765) and TyG-lgPLR (HR = 1.11, 95%CI: 0.97–1.27, P for trend = 0.162) and the risk of all-cause mortality. However, the other TyG-inflammation indices showed significant positive associations with the risk of all-cause mortality. Specifically, compared to participants in the lowest quartile (Q1), those in the highest quartile (Q4) had significantly increased risks of all-cause mortality for TyG-NLR (HR = 1.31, 95%CI: 1.13–1.53, P for trend < 0.0001), TyG-MLR (HR = 1.48, 95%CI: 1.23–1.77, P for trend < 0.0001), TyG-SIRI (HR = 1.34, 95%CI: 1.11–1.63, P for trend < 0.0001), and TyG-lgSII (HR = 1.20, 95%CI: 1.02–1.41, P for trend = 0.004). We also analyzed the association between TyG-BMI, single systemic inflammation indicators, and all-cause mortality, as shown in Supplementary Table 2. However, both TyG-BMI and single systemic inflammation indicators have lower HR for all-cause mortality compared to TyG-MLR.

Table 2 Association of TyG and TyG-Inflammation indicators with all-cause mortality
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Association of TyG and TyG-inflammation indices with cardiovascular mortality

We analyzed the association of TyG and TyG-inflammation indices with cardiovascular mortality. All the TyG-inflammation indices were positively associated with cardiovascular mortality (Table 3). Specifically, compared to participants in the first quartile (Q1) based on their TyG-inflammation indices, the risk of cardiovascular mortality for participants in the highest quartile (Q4) increased by 79% (TyG-NLR: HR = 1.79, 95% CI: 1.17–2.72, P for trend < 0.0001), 73% (TyG-MLR: HR = 1.73, 95% CI: 1.16–2.56, P for trend < 0.0001), 65% (TyG-SIRI: HR = 1.65, 95% CI: 1.03–2.64, P for trend = 0.003), 92% (TyG-lgSII: HR = 1.92, 95% CI: 1.31–2.81, P for trend < 0.001), and 87% (TyG-lgPLR: HR = 1.87, 95% CI: 1.40–2.49, P for trend < 0.001), respectively. However, no association between the TyG index and cardiovascular death was observed in Model 3. Detailed information on the associations among TyG-BMI, single systemic inflammatory indicators, and cardiovascular mortality is presented in Supplementary Table 3. Interestingly, both TyG-BMI and single systemic inflammation indicators have lower HR for cardiovascular mortality compared to TyG-lgSII.

Table 3 Association of TyG and TyG-Inflammation indicators with cardiovascular mortality
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The dose-response relationship between TyG and TyG-inflammation indices and mortality

We analyzed the dose-response relationship patterns between TyG and TyG-inflammation indices and mortality based on the RCS after adjusting for potential confounding factors (the same as in Model 3). The dose-response relationships between TyG and TyG-inflammation indices and all-cause mortality were nonlinear (all P values for nonlinearity < 0.05), as shown in Fig. 2. However, when evaluating the dose-response relationship between TyG and TyG-inflammation indices and cardiovascular mortality (Fig. 3), we found a linear dose-response relationship between TyG-SIRI (P for nonlinearity = 0.2398) and TyG-lgPLR (P for nonlinearity = 0.1557) and cardiovascular mortality. At the same time, there was a nonlinear dose-response relationship between the TyG index, other TyG-inflammation indices, and cardiovascular death (P for non-linearity < 0.05). Notably, regardless of the specific pattern of the dose-response relationship between the TyG index, TyG-inflammation indices, and mortality, when the respective threshold points were surpassed, there was an elevated risk of mortality as the TyG and TyG-inflammation indices increased.

Fig. 2
figure 2

Dose-response relationship between TyG, TyG-Inflammation indices, and all-cause mortality

Note: The red numbers in the figure represent the TyG and TyG-Inflammation indices corresponding to the threshold points. The solid and shaded areas represent estimates and their corresponding 95% confidence intervals(CIs), respectively. The adjusted potential confounding factors are the same as Model 3

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Fig. 3
figure 3

Dose-response relationship between TyG, TyG-Inflammation indices, and cardiovascular mortality

Note: The red numbers in the figure represent the TyG and TyG-Inflammation indices corresponding to the threshold points. The solid and shaded areas represent estimates and their corresponding 95% confidence intervals(CIs), respectively. The adjusted potential confounding factors are the same as Model 3

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The ability of the TyG and TyG-inflammation indices to predict mortality

Time-dependent ROC curve analysis showed that TyG-SIRI best predicted all-cause mortality at one year, followed by TyG-NLR, by TyG-MLR, by TyG-lgSII, by TyG, and by TyG-lgPLR. However, TyG-MLR demonstrated superior performance in predicting all-cause mortality at 3, 5, and 10 years (Fig. 4). Similarly, TyG-MLR also demonstrated superior performance in predicting cardiovascular mortality at 3, 5, and 10 years (Fig. 5).

Fig. 4
figure 4

Time-dependent ROC curves of the TyG and TyG-Inflammation indices for predicting all-cause mortality

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Fig. 5
figure 5

Time-dependent ROC curves of the TyG and TyG-Inflammation indices for predicting cardiovascular mortality

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The time-dependent AUC further indicated that TyG-MLR exhibited the highest accuracy in predicting long-term all-cause (Fig. 6A) and cardiovascular mortality risk (Fig. 6B). Additionally, TyG-MLR, TyG-SIRI, and TyG-NLR, and TyG-SII significantly outperformed the TyG index in predicting mortality, whereas TyG-lgPLR did not show such an improvement.

Fig. 6
figure 6

Time-dependent AUC values of the TyG and TyG-Inflammation indices for predicting all-cause mortality (A) and cardiovascular mortality (B)

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Discussion

In this cohort study based on the general U.S. population, we found no association between the TyG index and mortality rates, either all-cause or cardiovascular mortality. Interestingly, we built a new indicator based on TyG and systemic inflammation indicators, TyG-inflammation indices, which shows a marked positive correlation with mortality. Furthermore, time-dependent ROC curve results showed that TyG-MLR had the highest accuracy in predicting long-term mortality outcomes in the general population.

IR and inflammation are closely related to various metabolic diseases and adverse outcomes [1,2,3] and have a synergistic effect in promoting the progression of metabolic diseases and the occurrence of adverse events [24,25,26]. Therefore, we combined the TyG index with inflammation-related indicators to construct new indices that may better assess the risk of death in the general population. Systemic inflammation indicators can reflect the systemic inflammatory status of the body [28, 42, 43] and have been shown to be closely associated with the risk of death in the general population [30,31,32,33,34,35]. They are also closely related to adverse outcomes in patients with metabolic diseases and cardiovascular diseases [44,45,46,47,48,49]. Most importantly, systemic inflammation indicators, like TyG index, can be obtained through simple peripheral blood tests, which is one of the important reasons why they can be widely used in clinical practice.

Several studies have explored the association between the TyG index and mortality in the general population; however, they have shown varying results [16,17,18,19,20,21,22]. Chen et al. analyzed data from NHANES 2009–2018 and found that for every one-unit increase in the TyG index, the participants’ risk of all-cause mortality and cardiovascular mortality increased by 16% and 21.3%, respectively, and the association between the TyG index and mortality was influenced by gender. However, when the TyG index was analyzed as a categorical variable, no association with mortality was found [17]. Yu et al. [22] identified the inflection point between TyG and mortality using generalized additive models and penalized spline methods. They found that compared to participants with low TyG (< 8.5 or 8.7), those with high TyG (≥ 8.5 or 8.7) had a 1.39-fold and 1.82-fold increase in all-cause mortality risk and cardiovascular mortality risk, respectively. However, when TyG was grouped into tertiles, the association between TyG and mortality risk was no longer significant. Additionally, a cohort study based on the general population in Iran also found a close association between TyG and mortality, but after adjusting for the potential factor of diabetes, the association between TyG and mortality was no longer significant [21]. A prospective cohort study conducted by Professor Lopez-Jaramillo [19] found that the association between TyG and the general population was influenced by income levels; for example, TyG was not associated with mortality risk in high-income countries. It is worth mentioning that Liu et al., through a meta-analysis of 12 related studies, found that TyG was only associated with the risk of coronary artery disease in the general population but not with mortality [18]. Although some of the above studies found an association between TyG and mortality in the general population, these results may be influenced by various factors such as statistical analysis methods, potential confounding factors, study population, sample size, economic factors, etc. In conclusion, the association between the single indicator of TyG and mortality risk in the general population is not stable.

In our study, after adjustment confounders (e.g., presence of diabetes, income status, etc.), the association between TyG and all-cause and cardiovascular death was no longer significant. This finding is consistent with previous research results [19, 21]. Although several previous studies based on the NHANES database found that TyG is associated with mortality risk in the general population, these studies did not use all available NHANES data, which may introduce selection bias and subsequently affect the final results. In our study, to ensure reliability, we utilized all recorded data from NHANES 1999–2018. Our research findings indicate that, in the final model (Model3), compared to the lowest quartile (Q1), high levels of TyG-Inflammation indices (Q4) are closely associated with all-cause mortality (except for TyG-lgPLR) and cardiovascular mortality. However, it is noteworthy that the association between TyG-Inflammation indices and mortality is not significant when they are in the second and third quartiles. This may be because the association between TyG-Inflammation indices and mortality becomes significant only when they exceed a certain threshold. We believe that the strong association between the TyG-inflammation indices and mortality may be attributed to the following factors. First, insulin has anti-inflammatory effects, and when the body experiences IR, it is often accompanied by systemic low-grade inflammation [50]. Compared to the TyG index alone, the TyG-inflammation indices also consider the potential impact of the body’s inflammatory status. IR is often accompanied by vascular endothelial dysfunction, and inflammation can further exacerbate damage to vascular endothelial function in individuals with IR, leading to target organ damage and an increased risk of mortality [24]. Second, previous studies have shown that individuals with both IR and inflammatory responses have an eight-fold increased risk of developing type 2 diabetes compared to those without IR and inflammation [26]. The subsequent vascular, cardiac, and renal complications of type 2 diabetes also increase the risk of adverse outcomes [51]. Additionally, Anuurad et al. found that IR and inflammation could synergistically increase the risk of coronary heart disease in African-Americans and Caucasians [25].

It is worth mentioning that Dang et al. found that although TyG is not associated with mortality risk in the general population, when TyG is combined with BMI, TyG-BMI shows a positive correlation with mortality in the general population [20]. Based on this conclusion, we also analyzed the association between TyG-BMI and outcomes in our study, but the results were similar to those of TyG alone. This may be related to the inherent limitations of BMI. BMI is commonly used to assess overall obesity, but is ineffective in reflecting the distribution of visceral fat in the body [52]. Additionally, individuals with a normal BMI may still exhibit metabolic disorders; such individuals are referred to as metabolically unhealthy non-obese individuals [53, 54]. In other words, BMI can mask the actual metabolic abnormalities in individuals. However, the systemic inflammatory indices we used are different from BMI; they originate from the results of direct examination of individual blood and more accurately and truly reflect the level of inflammation, which may be one of the reasons why TyG-inflammation indices are closely related to mortality in the general population compared with TyG-BMI.

Notably, our findings and conclusions require further validation in larger cohorts and different populations to determine whether they are influenced by the economic environment of the participants, similar to the TyG index. In addition, the biological mechanisms underlying the association between TyG-inflammation indices and mortality need to be explored. In summary, both the TyG index and systemic inflammatory indicators can be measured through routine blood biochemical tests that are not expensive and do not require complex equipment. Once the effectiveness and stability of the TyG-inflammation indices are further validated in subsequent studies to assess mortality risk in the general population, they can be considered for use in primary care and health screening in the general population. Using the TyG-inflammation indices to stratify risk in these patients may provide specific treatment strategies to improve their prognosis.

Strengths and limitations

The strengths of this study are that it is the first to propose TyG-inflammation indices and to confirm the association between TyG-inflammation indices and mortality in the general population. Furthermore, unlike previous studies that primarily focused on metabolically abnormal populations, our study demonstrated that the TyG-inflammation indices can serve as a predictor of mortality risk in the general population. However, this study has several limitations. Firstly, because this was an observational cohort study, we could not determine a direct cause-and-effect association between TyG-inflammation indices and mortality. Furthermore, the potential mechanisms underlying the significant association between TyG-inflammation indices and mortality remain unclear. Finally, because the subjects of this study were from the US, the conclusions need to be validated in different populations and regions.

Conclusions

Our results showed that the TyG index was not associated with mortality in the general population. However, when the TyG index was combined with systemic inflammation indicators to form TyG-inflammation indices, we discovered a significant positive association between the TyG-inflammation indices and all-cause (except for TyG-lgPLR) and cardiovascular mortality. These simple, easily accessible, and inexpensive TyG-inflammation indices may serve as potential markers for identifying mortality risk in the general population at clinical practice.

Data availability

All data related to this study can be accessed free of charge on the NHANES website (https://www.cdc.gov/nchs/nhanes/).

References

  1. McFarlane SI, Banerji M, Sowers JR. Insulin resistance and cardiovascular disease. J Clin Endocrinol Metab. 2001;86(2):713–8. https://doi.org/10.1210/jcem.86.2.7202

    Article  PubMed  CAS  Google Scholar 

  2. Natali A, Ferrannini E. Hypertension, insulin resistance, and the metabolic syndrome. Endocrinol Metab Clin North Am. 2004;33(2):417–29. https://doi.org/10.1016/j.ecl.2004.03.007

    Article  PubMed  CAS  Google Scholar 

  3. Ferroni P, Basili S, Falco A, Davì G. Inflammation, insulin resistance, and obesity. Curr Atheroscler Rep. 2004;6(6):424–31. https://doi.org/10.1007/s11883-004-0082-x

    Article  PubMed  Google Scholar 

  4. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28(7):412–9. https://doi.org/10.1007/bf00280883

    Article  PubMed  CAS  Google Scholar 

  5. Guerrero-Romero F, Simental-Mendía LE, González-Ortiz M, Martínez-Abundis E, Ramos-Zavala MG, Hernández-González SO, 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. https://doi.org/10.1210/jc.2010-0288

    Article  PubMed  CAS  Google Scholar 

  6. Vasques AC, Novaes FS, de Oliveira Mda S, Souza JR, Yamanaka A, Pareja JC, et al. TyG index performs better than HOMA in a Brazilian population: a hyperglycemic clamp validated study. Diabetes Res Clin Pract. 2011;93(3):e98–100. https://doi.org/10.1016/j.diabres.2011.05.030

    Article  PubMed  CAS  Google Scholar 

  7. Son DH, Lee HS, Lee YJ, Lee JH, Han JH. Comparison of triglyceride-glucose index and HOMA-IR for predicting prevalence and incidence of metabolic syndrome. Nutr Metab Cardiovasc Dis. 2022;32(3):596–604. https://doi.org/10.1016/j.numecd.2021.11.017

    Article  PubMed  CAS  Google Scholar 

  8. Huang D, Ma R, Zhong X, Jiang Y, Lu J, Li Y, et al. Positive association between different triglyceride glucose index-related indicators and psoriasis: evidence from NHANES. Front Immunol. 2023;14:1325557. https://doi.org/10.3389/fimmu.2023.1325557

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Navarro-González D, Sánchez-Íñigo L, Pastrana-Delgado J, Fernández-Montero A, Martinez JA. Triglyceride-glucose index (TyG index) in comparison with fasting plasma glucose improved diabetes prediction in patients with normal fasting glucose: the vascular-metabolic CUN cohort. Prev Med. 2016;86:99–105. https://doi.org/10.1016/j.ypmed.2016.01.022

    Article  PubMed  Google Scholar 

  10. Zhao M, Xiao M, Tan Q, Lu F. Triglyceride glucose index as a predictor of mortality in middle-aged and elderly patients with type 2 diabetes in the US. Sci Rep. 2023;13(1):16478. https://doi.org/10.1038/s41598-023-43512-0

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Yao Y, Wang B, Geng T, Chen J, Chen W, Li L. The association between TyG and all-cause/non-cardiovascular mortality in general patients with type 2 diabetes mellitus is modified by age: results from the cohort study of NHANES 1999–2018. Cardiovasc Diabetol. 2024;23(1):43. https://doi.org/10.1186/s12933-024-02120-6

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Liu Q, Zhang Y, Chen S, Xiang H, Ouyang J, Liu H, et al. Association of the triglyceride-glucose index with all-cause and cardiovascular mortality in patients with cardiometabolic syndrome: a national cohort study. Cardiovasc Diabetol. 2024;23(1):80. https://doi.org/10.1186/s12933-024-02152-y

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Hou XZ, Lv YF, Li YS, Wu Q, Lv QY, Yang YT, et al. Association between different insulin resistance surrogates and all-cause mortality in patients with coronary heart disease and hypertension: NHANES longitudinal cohort study. Cardiovasc Diabetol. 2024;23(1):86. https://doi.org/10.1186/s12933-024-02173-7

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Wei X, Min Y, Song G, Ye X, Liu L. Association between triglyceride-glucose related indices with the all-cause and cause-specific mortality among the population with metabolic syndrome. Cardiovasc Diabetol. 2024;23(1):134. https://doi.org/10.1186/s12933-024-02215-0

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Ding L, Fu B, Zhang H, Dai C, Zhang A, Yu F, et al. The impact of triglyceride glucose-body mass index on all-cause and cardiovascular mortality in elderly patients with diabetes mellitus: evidence from NHANES 2007–2016. BMC Geriatr. 2024;24(1):356. https://doi.org/10.1186/s12877-024-04992-5

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Liu XC, He GD, Lo K, Huang YQ, Feng YQ. The triglyceride-glucose index, an insulin resistance marker, was non-linear associated with all-cause and cardiovascular mortality in the general population. Front Cardiovasc Med. 2020;7:628109. https://doi.org/10.3389/fcvm.2020.628109

    Article  PubMed  CAS  Google Scholar 

  17. 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. https://doi.org/10.1186/s12933-023-02054-5

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Liu X, Tan Z, Huang Y, Zhao H, Liu M, Yu P, et al. Relationship between the triglyceride-glucose index and risk of cardiovascular diseases and mortality in the general population: a systematic review and meta-analysis. Cardiovasc Diabetol. 2022;21(1):124. https://doi.org/10.1186/s12933-022-01546-0

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Lopez-Jaramillo P, Gomez-Arbelaez D, Martinez-Bello D, Abat MEM, Alhabib KF, Avezum Á, 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. https://doi.org/10.1016/s2666-7568(22)00247-1

    Article  PubMed  Google Scholar 

  20. Dang K, Wang X, Hu J, Zhang Y, Cheng L, Qi X, et al. The association between triglyceride-glucose index and its combination with obesity indicators and cardiovascular disease: NHANES 2003–2018. Cardiovasc Diabetol. 2024;23(1):8. https://doi.org/10.1186/s12933-023-02115-9

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Alavi Tabatabaei G, Mohammadifard N, Rafiee H, Nouri F, Maghami Mehr A, Najafian J, et al. Association of the triglyceride glucose index with all-cause and cardiovascular mortality in a general population of Iranian adults. Cardiovasc Diabetol. 2024;23(1):66. https://doi.org/10.1186/s12933-024-02148-8

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Yu Y, Wang J, Ding L, Huang H, Cheng S, Deng Y, et al. Sex differences in the nonlinear association of triglyceride glucose index with all-cause and cardiovascular mortality in the general population. Diabetol Metab Syndr. 2023;15(1):136. https://doi.org/10.1186/s13098-023-01117-7

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Ramdas Nayak VK, Satheesh P, Shenoy MT, Kalra S. Triglyceride glucose (TyG) index: a surrogate biomarker of insulin resistance. J Pak Med Assoc. 2022;72(5):986–8. https://doi.org/10.47391/jpma.22-63

    Article  PubMed  Google Scholar 

  24. Natali A, Toschi E, Baldeweg S, Ciociaro D, Favilla S, Saccà L, et al. Clustering of insulin resistance with vascular dysfunction and low-grade inflammation in type 2 diabetes. Diabetes. 2006;55(4):1133–40. https://doi.org/10.2337/diabetes.55.04.06.db05-1076

    Article  PubMed  CAS  Google Scholar 

  25. Anuurad E, Tracy RP, Pearson TA, Kim K, Berglund L. Synergistic role of inflammation and insulin resistance as coronary artery disease risk factors in African Americans and Caucasians. Atherosclerosis. 2009;205(1):290–5. https://doi.org/10.1016/j.atherosclerosis.2008.11.028

    Article  PubMed  CAS  Google Scholar 

  26. Bennouar S, Bachir Cherif A, Aoudia Y, Abdi S. Additive interaction between insulin resistance, chronic low-grade inflammation and vitamin D deficiency on the risk of type 2 diabetes mellitus: a cohort study. J Am Nutr Assoc. 2024:1–11. https://doi.org/10.1080/27697061.2024.2352401

  27. Ke J, Qiu F, Fan W, Wei S. Associations of complete blood cell count-derived inflammatory biomarkers with asthma and mortality in adults: a population-based study. Front Immunol. 2023;14:1205687. https://doi.org/10.3389/fimmu.2023.1205687

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Tudurachi BS, Anghel L, Tudurachi A, Sascău RA, Stătescu C. Assessment of inflammatory hematological ratios (NLR, PLR, MLR, LMR and monocyte/HDL-cholesterol ratio) in acute myocardial infarction and particularities in young patients. Int J Mol Sci. 2023;24(18). https://doi.org/10.3390/ijms241814378

  29. Xia Y, Xia C, Wu L, Li Z, Li H, Zhang J. Systemic immune inflammation index (SII), system inflammation response index (SIRI) and risk of all-cause mortality and cardiovascular mortality: a 20-year follow-up cohort study of 42,875 US adults. J Clin Med. 2023;12(3). https://doi.org/10.3390/jcm12031128

  30. Fest J, Ruiter TR, Groot Koerkamp B, Rizopoulos D, Ikram MA, van Eijck CHJ, et al. The neutrophil-to-lymphocyte ratio is associated with mortality in the general population: the Rotterdam study. Eur J Epidemiol. 2019;34(5):463–70. https://doi.org/10.1007/s10654-018-0472-y

    Article  PubMed  CAS  Google Scholar 

  31. Song M, Graubard BI, Rabkin CS, Engels EA. Neutrophil-to-lymphocyte ratio and mortality in the United States general population. Sci Rep. 2021;11(1):464. https://doi.org/10.1038/s41598-020-79431-7

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Hua Y, Sun JY, Lou YX, Sun W, Kong XQ. Monocyte-to-lymphocyte ratio predicts mortality and cardiovascular mortality in the general population. Int J Cardiol. 2023;379:118–26. https://doi.org/10.1016/j.ijcard.2023.03.016

    Article  PubMed  Google Scholar 

  33. Mathur K, Kurbanova N, Qayyum R. Platelet-lymphocyte ratio (PLR) and all-cause mortality in general population: insights from national health and nutrition education survey. Platelets. 2019;30(8):1036–41. https://doi.org/10.1080/09537104.2019.1571188

    Article  PubMed  CAS  Google Scholar 

  34. Jin Z, Wu Q, Chen S, Gao J, Li X, Zhang X, et al. The associations of two novel inflammation indexes, SII and SIRI with the risks for cardiovascular diseases and all-cause mortality: a ten-year follow-up study in 85,154 individuals. J Inflamm Res. 2021;14:131–40. https://doi.org/10.2147/jir.S283835

    Article  PubMed  PubMed Central  Google Scholar 

  35. Wang H, Nie H, Bu G, Tong X, Bai X. Systemic immune-inflammation index (SII) and the risk of all-cause, cardiovascular, and cardio-cerebrovascular mortality in the general population. Eur J Med Res. 2023;28(1):575. https://doi.org/10.1186/s40001-023-01529-1

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Patel CJ, Pho N, McDuffie M, Easton-Marks J, Kothari C, Kohane IS, et al. A database of human exposomes and phenomes from the US national health and nutrition examination survey. Sci Data. 2016;3:160096. https://doi.org/10.1038/sdata.2016.96

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Tian Y, Li D, Mu H, Wei S, Guo D. Positive correlation between snoring and dyslipidemia in adults: results from NHANES. Lipids Health Dis. 2023;22(1):73. https://doi.org/10.1186/s12944-023-01839-7

    Article  PubMed  PubMed Central  Google Scholar 

  38. Chen Y, Lu C, Ju H, Zhou Q, Zhao X. Elevated AIP is associated with the prevalence of MAFLD in the US adults: evidence from NHANES 2017–2018. Front Endocrinol (Lausanne). 2024;15:1405828. https://doi.org/10.3389/fendo.2024.1405828

    Article  PubMed  Google Scholar 

  39. Tomar SL, Asma S. Smoking-attributable periodontitis in the United States: findings from NHANES III. J Periodontol. 2000;71(5):743–51. https://doi.org/10.1902/jop.2000.71.5.743

    Article  PubMed  CAS  Google Scholar 

  40. Rattan P, Penrice DD, Ahn JC, Ferrer A, Patnaik M, Shah VH, et al. Inverse association of telomere length with liver disease and mortality in the US population. Hepatol Commun. 2022;6(2):399–410. https://doi.org/10.1002/hep4.1803

    Article  PubMed  CAS  Google Scholar 

  41. Johnson CL, Paulose-Ram R, Ogden CL, Carroll MD, Kruszon-Moran D, Dohrmann SM, et al. National health and nutrition examination survey: analytic guidelines, 1999–2010. Vital Health Stat. 2013;2(161):1–24.

    Google Scholar 

  42. Wang RH, Wen WX, Jiang ZP, Du ZP, Ma ZH, Lu AL, et al. The clinical value of neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), platelet-to-lymphocyte ratio (PLR) and systemic inflammation response index (SIRI) for predicting the occurrence and severity of pneumonia in patients with intracerebral hemorrhage. Front Immunol. 2023;14:1115031. https://doi.org/10.3389/fimmu.2023.1115031

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Islam MM, Satici MO, Eroglu SE. Unraveling the clinical significance and prognostic value of the neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, systemic immune-inflammation index, systemic inflammation response index, and delta neutrophil index: an extensive literature review. Turk J Emerg Med. 2024;24(1):8–19. https://doi.org/10.4103/tjem.tjem_198_23

    Article  PubMed  PubMed Central  Google Scholar 

  44. Marra A, Bondesan A, Caroli D, Grugni G, Sartorio A. The neutrophil to lymphocyte ratio (NLR) positively correlates with the presence and severity of metabolic syndrome in obese adults, but not in obese children/adolescents. BMC Endocr Disord. 2023;23(1):121. https://doi.org/10.1186/s12902-023-01369-4

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Dong G, Gan M, Xu S, Xie Y, Zhou M, Wu L. The neutrophil-lymphocyte ratio as a risk factor for all-cause and cardiovascular mortality among individuals with diabetes: evidence from the NHANES 2003–2016. Cardiovasc Diabetol. 2023;22(1):267. https://doi.org/10.1186/s12933-023-01998-y

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Chen Y, Chen S, Han Y, Xu Q, Zhao X. Neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio are important indicators for predicting in-hospital death in elderly AMI patients. J Inflamm Res. 2023;16:2051–61. https://doi.org/10.2147/jir.S411086

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Chen Y, Xie K, Han Y, Xu Q, Zhao X. An easy-to-use nomogram based on SII and SIRI to predict in-hospital mortality risk in elderly patients with acute myocardial infarction. J Inflamm Res. 2023;16:4061–71. https://doi.org/10.2147/jir.S427149

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Pruc M, Peacock FW, Rafique Z, Swieczkowski D, Kurek K, Tomaszewska M, et al. The prognostic role of platelet-to-lymphocyte ratio in acute coronary syndromes: a systematic review and meta-analysis. J Clin Med. 2023;12(21). https://doi.org/10.3390/jcm12216903

  49. Wang H, Li S, Yu J, Xu J, Xu Y. Role of leukocyte parameters in patients with ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention with high thrombus burden. Front Cardiovasc Med. 2024;11:1397701. https://doi.org/10.3389/fcvm.2024.1397701

    Article  PubMed  PubMed Central  Google Scholar 

  50. Sjöholm A, Nyström T. Endothelial inflammation in insulin resistance. Lancet. 2005;365(9459):610–2. https://doi.org/10.1016/s0140-6736(05)17912-4

    Article  PubMed  Google Scholar 

  51. Sattar N, Presslie C, Rutter MK, McGuire DK. Cardiovascular and kidney risks in individuals with type 2 diabetes: contemporary understanding with greater emphasis on excess adiposity. Diabetes Care. 2024;47(4):531–43. https://doi.org/10.2337/dci23-0041

    Article  PubMed  CAS  Google Scholar 

  52. Weber DR, Leonard MB, Shults J, Zemel BS. A comparison of fat and lean body mass index to BMI for the identification of metabolic syndrome in children and adolescents. J Clin Endocrinol Metab. 2014;99(9):3208–16. https://doi.org/10.1210/jc.2014-1684

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Iacobini C, Pugliese G, Blasetti Fantauzzi C, Federici M, Menini S. Metabolically healthy versus metabolically unhealthy obesity. Metabolism. 2019;92:51–60. https://doi.org/10.1016/j.metabol.2018.11.009

    Article  PubMed  CAS  Google Scholar 

  54. Li H, Zhang Y, Luo H, Lin R. The lipid accumulation product is a powerful tool to diagnose metabolic dysfunction-associated fatty liver disease in the United States adults. Front Endocrinol (Lausanne). 2022;13:977625. https://doi.org/10.3389/fendo.2022.977625

    Article  PubMed  Google Scholar 

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Acknowledgements

We express our gratitude to all the individuals and groups who have participated in the NHANES.

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  1. Yan Chen, Kailing Xie and Yuanyuan Han contributed equally to this work.

Authors and Affiliations

  1. Department of Cardiology, The Second Hospital of Dalian Medical University, Dalian, Liaoning Province, People’s Republic of China

    Yan Chen, Yuanyuan Han, Haonan Ju, Jiaxi Sun & Xin Zhao

  2. The Second Xiangya Hospital, Central South University, Changsha, Hunan Province, People’s Republic of China

    Kailing Xie

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Contributions

Conceptualization: YC; Methodology: YC, KX, and XZ; Validation: YC and HJ; Data Curation: YC, YH, HJ, and JS; Writing – Original Draft Preparation: YC; Visualization: YC, KX, and HJ; Supervision and Funding Acquisition: XZ.

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Correspondence to Xin Zhao.

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Chen, Y., Xie, K., Han, Y. et al. The association between triglyceride-glucose index and its combination with systemic inflammation indicators and all-cause and cardiovascular mortality in the general US population: NHANES 1999–2018. Lipids Health Dis 23, 289 (2024). https://doi.org/10.1186/s12944-024-02277-9

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Keywords

Lipids in Health and Disease

ISSN: 1476-511X