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08 July 2023: Clinical Research  

The Triglyceride Glucose (TyG) Index as a Sensible Marker for Identifying Insulin Resistance and Predicting Diabetic Kidney Disease

Hui Fang Li1ABCDEFG*, Xia Miao1B, Ying Li2AG

DOI: 10.12659/MSM.939482

Med Sci Monit 2023; 29:e939482




BACKGROUND: Patients with insulin-resistant diabetes have the highest risk of kidney disease. The triglyceride glucose (TyG) index is considered a reliable and simple marker of insulin resistance. We studied the relationship between the TyG index, diabetic kidney disease (DKD), and related metabolic disorders in patients with type 2 diabetes.

MATERIAL AND METHODS: This retrospective study included a consecutive case series from January 2021 to October 2022 in the Department of Endocrinology at Hebei Yiling Hospital. In total, 673 patients with type 2 diabetes met the inclusion criteria. The TyG index was calculated by napierian logarithmic (ln) (fasting triglyceride×fasting glucose /2). Patient demographic and clinical indicators were obtained from medical records, and statistical analysis was conducted using SPSS version 23.

RESULTS: The TyG index was significantly related to metabolic indicators (low-density lipoprotein, high-density lipoprotein, alanine aminotransferase, plasma albumin, serum uric acid, triglyceride, and fasting glucose) and urine albumin (P<0.01) but not with serum creatinine and estimated glomerular filtration rate. In multiple regression analysis, an increase in the TyG index was revealed to be an independent risk factor for DKD (OR: 1.699, P<0.001).

CONCLUSIONS: The TyG index was independently related to DKD and related metabolic disorders; therefore, the TyG index can be used as an early sensitive target for clinical guidance in the treatment of DKD with insulin resistance.

Keywords: Diabetes Mellitus, Type 2, Diabetic Nephropathies, Insulin Resistance, Humans, Glucose, Retrospective Studies, Triglycerides, Uric Acid, Blood Glucose, biomarkers, Risk Factors


The incidence and prevalence of end-stage kidney disease (ESKD) is increasing worldwide and is becoming a heavy economic burden for society. Diabetic kidney disease (DKD) is the major cause of chronic kidney disease (CKD) and ESKD [1]. Over the past decades, despite improving blood glucose control and renal protection, the incidence of ESKD related to diabetes has not been significantly reduced. The major characteristics of type 2 diabetes are insulin resistance (IR) and β-cell dysfunction, although there is wide variation in clinical practice. To solve the complexity of diabetes, new subgroups or subtypes of diabetes have recently been proposed. Among them, diabetes with severe IR has the highest risk of kidney disease due to its clinical characteristics. Although the importance of IR in the pathogenesis of diabetes is well known, IR indicators have not been widely used in clinical practice, which may be related to the following factors. First the cost, time, and process of measuring insulin are complex. The normal blood glucose clamp test is considered the criterion standard for measuring IR, although it is complex and difficult to use. Due to the wide range of “normal” values of fasting plasma insulin, the homeostasis model assessment is unpreferable, being more variable than the clamp method, as a normal steady-state relationship between plasma glucose and insulin levels in patients with diabetes no longer exists. The triglyceride glucose (TyG) index is a non-insulin-based IR index that combines triglycerides and fasting blood glucose (FBG) [2]. It has been identified as a reliable biomarker of IR, with better sensitivity than the homeostasis model assessment [3]. The TyG index is simple to use, economical, and reliable, serving as an alternative indicator of IR. Moreover, it has been used to study metabolic syndrome, nonalcoholic fatty liver disease, atherosclerosis, and type 2 diabetes mellitus [4], with findings confirming that a high TyG index can be independently related to the incidence of atherosclerotic cardiovascular disease, heart failure, stroke, and other major vascular diseases [5,6]. Research has shown that IR occurs 20 years before type 2 diabetes mellitus diagnosis; therefore, IR has been confirmed to be an independent predictor of cardiovascular disease without diabetes.

Some prospective small sample studies have reported that IR can precede microalbuminuria in patients with diabetes [7,8]. IR interrelated with hypertension, central obesity, and dyslipidemia is aggravated by the occurrence of DKD, suggesting that reduced insulin sensitivity is involved in the pathogenesis and may be a potential treatment target. Moreover, the improvement of IR in patients with diabetes is related to the protection of the heart and kidney [9]. However, the research between TyG and DKD is still scarce. Therefore, this study used the TyG index level as a breakthrough point and explored the correlation between the TyG index level and DKD risk through a case-control study. Research shows that the incidence of diabetes is concentrated in 40 to 59 year olds [10]. However, we studied type 2 diabetes kidney disease, so the minimum threshold of age for enrollment was 40 years old. The focus of this study was diabetes nephropathy, which is characterized by micro-albuminuria at first, and a large amount of albuminuria (>0.5 g/24 h) may occur in the late stage [11]. In addition, the study object was not a renal pathological result, but a grouping of diabetes nephropathy and non-diabetes nephropathy based on proteinuria. In 321 cases of diabetes patients with renal biopsy, 179 patients were without DKD (55.8%), and membranous nephropathy was the most common non-diabetic renal disease in patients with diabetes [12,13]. Therefore, this study excluded patients with an albumin creatinine ratio (ACR) >1.0 g.

Material and Methods


This was a retrospective study of patients with type 2 diabetes hospitalized in the Department of Endocrinology, Hebei Yiling Hospital Affiliated to Hebei Medical University (Shijiazhuang, Hebei, China) from January 2021 to October 2022. The inclusion criteria were as follows: (1) type 2 diabetes was diagnosed according to the World Health Organization [14] and the Chinese diabetes Society [15]; or the diagnosis was confirmed, and the hypoglycemic treatment was more than 6 months; and (2) age ≥40 years. The exclusion criteria were as follows: (1) patients without type 2 diabetes; (2) type 2 diabetes patients with lactic acid poisoning, or ketoacidosis, or severe infection, or critical patients, including those with conditions such as organ failure, acute myocardial infarction, and cerebral infarction; and (3) the laboratory inspection data was incomplete. For patients with DKD, urinary ACR (UACR) greater than 1.0 g and estimated glomerular filtration rate (eGFR) <45 mL4n.1.73 m2 were additional exclusion criteria. The study was approved by the Ethics Committee of Hebei Yiling Hospital Affiliated to Hebei Medical University and was conducted in accordance with the Declaration of Helsinki. Appropriate consent was obtained from all participants.


Anthropometric parameters included blood pressure assessments. Blood and urine samples were collected in the morning after participants fasted from eating, drinking, and smoking for at least 8 h. All blood and urine samples were tested immediately after collection. The plasma levels of parameters were measured using an automatic biochemical analyzer (7600-020; Hitachi Inc, Tokyo, Japan). The UACR is a dry chemical method used to measure urinary protein and creatinine (Korea Cyber Reader 720.). Some patients with diabetes complicated with hypertension and patients with cardiovascular and cerebrovascular diseases were being treated with statins.


The eGFR was calculated from a serum creatinine and age study equation (CKD-EPI) [16].The TyG index was calculated as follows: TyG=napierian logarithmic (ln) (fasting TG [mg/dL]×FBG [mg/dL]/2) [17]. DKD was defined as UACR ≥30 mg/g at least twice in the consecutive morning urine collections.


SPSS 23.0 software (IBM Corp, Armonk, NY, USA) was used to analyze the data. The data are expressed as the mean±median standard deviation (interquartile interval) for continuous variables and percentage for categorical variables. The t test was used to compare the 2 groups (DKD and non-DKD). Pearson correlation analysis was used to calculate the correlation between TyG and other variables. The constructed subject operating characteristic curve (ROC) was used to evaluate the identification performance of DKD according to the area under the ROC curve (AUC). Binary logistic regression multivariate analysis was used to evaluate DKD risk factors. A P value <0.05 indicated statistical significance.



A total of 328 patients without DKD (non-DKD) and 345 patients with DKD with average age of 63.89 years and a median disease duration of 10.71 years were included. A total of 51.26% (345/673) of the patients with type 2 diabetes were identified as having DKD. Compared with IR in the non-DKD group, IR in patients with DKD was shown by the TyG index (P<0.001). Similarly, patients with DKD had a significantly longer course of disease (P=0.02), older age (P<0.001), and higher serum creatinine (Cre; P<0.001), uric acid (UA; P=0.033), total triglycerides (TG; P=0.023), and FPB levels (P<0.001) than those in the non-DKD group. In addition, lower levels of free triiodothyronine (FT3; P=0.006), albumin (ALB; P<0.001), glutamyl aminotransferase (GGT; P=0.003), high-density lipoprotein (HDL; P=0.014), and eGFR (P<0.001) were observed. However, there was no difference in thyroid stimulating hormone, free thyroxine, platelets, low-density lipoprotein cholesterol (LDL-C), and alanine aminotransferase (ALT) levels between the groups, and the DKD group showed serious glucose and lipid metabolism disorders (Table 1).


Correlation analysis shows that TyG index was positively correlated with UA (r=0.164#), UACR (r=0.172), LDL (r=0.277), and ALT (r=0.184; P<0.001 for each) but negatively correlated with HDL levels (r=−0.269, P<0.001). However, the TyG index was not significantly correlated with Cre level (r=0.075, P=0.051) or eGFR (r=−0.018, P=0.639). The results showed that IR was involved in early metabolic disorders of DKD (Table 2).


Univariate and multivariate regression analyses were performed to identify independent risk factors for DKD (Table 3). The results showed that DKD risk factors included the TyG index (OR=1.521, 95% CI: 1.247–1.854), age (OR=1.028, 95% CI: 1.013–1.044), ALB (OR=0.891, 95% CI: 0.858–0.925), and FPB (OR=1.096, 95% CI: 1.049–1.145). After adjusting for confounders, such as disease course, FT3, GGT, HDL, and TG, the results still showed that the TyG index remained strongly associated with DKD (OR=1.693, 95% CI: 1.022–2.803), indicating that the TyG index was an independent risk factor for DKD.


Adjusted for confounding factors of course, age, FT3, ALB, GGT, Cre, UA, HDL, TG, FPB and eGFR, the results showed that TyG was still a risk factor for DKD, and for every 1 unit increase in TyG, the risk of DKD increased by 1.699 times. According to the regression analysis results, the fitted equations is −1.441× 0.025age× −0.123ALB × 0.53TyG (R2: 0.107; Table 4). ROC analysis was used to evaluate the performance of TyG in identifying the risk of DKD in patients. As shown in Figure 1, the AUC for predicting DKD was 0.681 (95% CI: 0.641–0.72), the cut-off value was 0.485, and the corresponding sensitivity and specificity were 67.0% and 58.5%, respectively.


First, a stratification analysis was conducted according to the TyG median. The patients were divided into 2 groups, a low TyG group (TyG ≤9.249) and high TyG group (TyG >9.25). Simultaneously, eGFR and age were stratified. The results showed that TyG ≤9.249 showed no statistical significance in the eGFR2 (60–89.9 mL/min/1.73 m2) stage and age (>71 years old) subgroup (P=0.674, P=0.57, respectively). However, TyG >9.25 showed significant differences in the eGFR2 (60–89.9 mL/min/1.73 m2) stage and age (>71 years old) subgroup between the non-DKD and DKD groups (P<0.001, P=0.003, respectively; Table 5).


We applied the insulin resistance biomarker TyG to study the relationship between insulin resistance and DKD. We demonstrated that the TyG index was closely related to DKD (P<0.001) and positively related to UA and UACR (P<0.001). The TyG index was an independent risk factor for every 1-unit increase, by which the risk of DKD increased by 1.699 times. TyG >9.25 showed significant differences in eGFR2 (60–89.9 mL/min/1.73 m2) stage and age (>71 years old) between the non-DKD and DKD groups (P<0.001, P=0.003, respectively). Therefore, TyG may be an important target for treatment and prevention of DKD.

Treating DKD is difficult, especially in patients with phase III diabetes and ESKD. Therefore, early identification and attention to the management of high-risk patients with DKD can be an effective way to reduce the incidence rate of DKD. IR is a pathological reaction of hyperinsulinemia and impaired glucose tolerance caused by the decreased insulin sensitivity of target tissues (such as liver, muscle, and adipose tissue). IR can lead to hyperglycemia, hypertension, dyslipidemia, visceral obesity, hyperuricemia, and other conditions. Moreover, IR and its consequences are the basis of the typical pathological and clinical characteristics of type 2 diabetes. Therefore, researching the relationship between IR and DKD will help to identify novel and effective treatments to improve the prognosis of type 2 diabetes.

The results of this study showed that the TyG index in the DKD group was significantly higher than that in the non-DKD group (P<0.001), suggesting that patients with DKD have IR. Patients in the DKD group also showed more serious metabolic disorders related to clinical parameters. The levels of FPG, TG, UA, Cre, and GGT were significantly higher than those in the diabetes group; however, the levels of HDL, ALB, and FT3 were significantly decreased in the DKD group. Several studies have shown that insulin resistance is related to the progress of DKD, although the underlying mechanism has not been fully clarified. Zhao et al [18] showed that among 411 patients with DKD confirmed by biopsy, 224 (55%) had metabolic syndrome (sub-distribution hazard ratio=1.93, 95% CI: 1.34–2.79). Several studies have focused on hyperuricemia as a risk factor for the onset and progression of diabetes mellitus, DKD, and CKD; however, the relationship between IR and hyperuricemia has not attracted much attention, and the causal relationship remains unclear. Moreover, a recent study confirmed that reducing IR can reduce the uricemia level, but reducing the uricemia level is less likely to improve IR [19]. Similarly, studies have shown that hyperuricemia induces endothelial IR by disrupting insulin-stimulated endothelial nitric oxide synthesis [20]. In contrast to previous studies, the present study found no statistical difference in LDL-C levels. This may be related to the enrolled patients in this study taking statins before admission. Drugs that can improve IR, such as metformin and insulin sensitizers (pioglitazone and rosiglitazone), should be selected and used in the treatment of patients with DKD.

Research data on the correlation between the TyG index and DKD are limited. Our study showed that the TyG index was positively correlated with UA, UACR, LDL-C, and FBG (P<0.001) but not with creatinine and eGFR levels in patients with type 2 diabetes. It is speculated that the increase in TyG index occurs in the early stage of type 2 diabetes and is mainly involved in metabolism dysfunction before an increase in serum creatinine and decrease in eGFR occur. Previous research has shown that IR causes an increase in the hydrostatic pressure of the glomerulus and permeability of the renal vessels, leading to glomerular hyperfiltration [21]. The relationship and specific mechanism between the TyG index and eGFR require further study. To further analyze the relationship between TyG and DKD risk, univariate regression analysis showed that TyG index was a risk factor for progression of DKD. The risk of DKD increased 1.521 times (OR=1.521, 95% CI: 1.247–1.854) for every 1 unit increase in TyG, while the risk of DKD increased 1.124 times for every 1 μmol/L increase in triglycerides, and 1.096 times for every 1 mmol/L increase in FBG (OR=1.096 95% CI: 1.049–1.145). The results showed that an increased TyG index was significantly associated with an increased risk of developing DKD and was useful for the early identification of DKD.

Epidemiological studies have shown that inflammatory reactions can lead to the occurrence of type 2 diabetes mellitus by participating in IR and further aggravating it in the presence of hyperglycemia, thus promoting multiple long-term diabetes complications [22]. The TyG index is also considered a reliable marker of systemic inflammation, which can be an inflammatory factor in the progression of DKD [23]. The multivariable analysis adjusts for potential confounding factors and establishes the fitting equation, while the ROC curve clarifies the sensitivity and specificity of the model, indicating that a high TyG index plays an important role in the pathogenesis of DKD. The TyG index can be easily calculated using TG and FPG; therefore, the TyG index is expected to be widely used in clinical practice to identify patients at high risk of DKD and promptly intervene.

There are different factors influencing the TyG index on eGFR, in view of the decrease in eGFR with age. In the present study, the subgroup analysis of eGFR and age showed that when the TyG index was >9.25, differences in eGFR and age were statistically significant between the 2 groups. This suggests that the TyG index increases as the risk of DKD increases and eGFR decreases. Another study showed that there was a significant correlation between the TyG index and DKD in patients with an eGFR ≥90 mL/min/1.73 m2; however, in patients with an eGFR <90 mL/min/1.73m2, this correlation was not significant [24]. A large-scale cohort study to elucidate the exact impact of the TyG index on eGFR is necessary [25].

This study had several limitations. First, this was a cross-sectional observational study; therefore, a causal relationship could not be obtained. Second, we collected the data of all inpatients in the Endocrinology Department of the third-level grade-A traditional Chinese medicine hospital in the past 22 months, including those hospitalized for type 2 diabetes. Moreover, most patients had multiple complications of diabetes at the same time, which may have caused data bias.


We found that there was a significant correlation between the increase in TyG index and DKD. TyG is an important factor in the pathophysiology of DKD and may be an important target for treatment and prevention. Prospective research is needed to explore the effect of TyG on DKD progression.


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