Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Open Access

Peer-reviewed

Research Article

Associations of the TyG index with albuminuria and chronic kidney disease in patients with type 2 diabetes

  • Xiaonan Li,

    Roles Data curation, Formal analysis, Methodology, Software, Writing – original draft

    Affiliation Department of Geriatrics, Jilin Geriatrics Clinical Research Center, The First Hospital of Jilin University, Changchun, China

    ⨯
  • Yuehui Wang

    Roles Conceptualization, Project administration, Supervision, Visualization, Writing – review & editing

    yuehuiwang300@jlu.edu.cn

    Affiliation Department of Geriatrics, Jilin Geriatrics Clinical Research Center, The First Hospital of Jilin University, Changchun, China

    ⨯

Abstract

Objective

Diabetes-related kidney disease reduces patients’ quality of life, increases the risk of death, and is associated with insulin resistance (IR). The triglyceride-glucose (TyG) index is a simple and inexpensive alternative to IR measurement. Furthermore, the relationship between albuminuria and chronic kidney disease (CKD) in type 2 diabetes mellitus (T2DM) remains unclear. Therefore, we aimed to investigate the association of TyG index with albuminuria and CKD in patients with T2DM.

Methods

Data from 01/2013-12/2017 period were obtained from the Population Health Data Archive’s Diabetes Complications Data Set. A total of 1048 patients with T2DM were included in this study. CKD is defined as an estimated glomerular filtration rate < 60 ml/min-1.1.73 m-2 or a urinary albumin-to-creatinine ratio (UACR) ≥ 30 mg/g. Albuminuria is defined as a UACR ≥ 30 mg/g. The TyG index is calculated by measuring the triglyceride and fasting blood glucose levels. Logistic regression models were used to analyze the association between albuminuria, CKD with T2DM and TyG index.

Results

We identified 1048 subjects, 63.03% of whom were men. The mean age was 46.21 years, and the mean body mass index was 26.742 kg/m2. CKD and albuminuria detection rates showed an increasing trend in the different TyG subgroups. (p = 0.008, p = 0.006). Using the Q1 group as a baseline, the risk of albuminuria and CKD was significantly greater in the group Q3 (OR = 1.514, 95% CI 1.121–2.047 P = 0.05), and the same result was obtained after adjusting for covariates (OR = 2.241, 95% CI 1.245–4.034, P = 0.007). Subgroup analyses revealed a significant increase in the incidence of albuminuria and CKD in the group Q3 compared to that in the Q1 group.

Conclusions

The TyG index is positively associated with albuminuria and CKD in patients with T2DM and may be a marker for predicting the occurrence of early kidney injury in patients with T2DM. Clinicians should test this indicator early to detect lesions and improve patient prognosis.

Citation: Li X, Wang Y (2024) Associations of the TyG index with albuminuria and chronic kidney disease in patients with type 2 diabetes. PLoS ONE 19(10): e0312374. https://doi.org/10.1371/journal.pone.0312374

Editor: Amirmohammad Khalaji, Tehran University of Medical Sciences, ISLAMIC REPUBLIC OF IRAN

Received: March 17, 2024; Accepted: October 6, 2024; Published: October 28, 2024

Copyright: © 2024 Li, Wang. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Our data came from the Diabetes Complications Data Set which were collected by the General Hospital of the People's Liberation Army of China from 01/2013 to 12/2017 and it was stored in the Population Health Data Archive, the website is https://www.ncmi.cn/phda/dataDetails.do?id=CSTR:A0006.11.A0005.201905.000282 (Language is Chinese) The data set contains 87 variables for 3,000 patients with T2DM. The example data from the Diabetes Complications Data Set can be acquired and was uploaded as S1_Table.Once the application has been approved, the data can be viewed on the virtual platform, with specific operational usage uploaded as a support information file named S3_file (Language in Chinese). The username is nmdcfx049 and password is fx0514.lxn.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Type 2 diabetes mellitus (T2DM) is the most common metabolic disorder that leads to numerous microvascular and macrovascular complications [1]. Chronic kidney disease (CKD), a major microvascular complication of T2DM, affects nearly half of the patients with T2DM, and approximately 30% of these patients eventually progress to end-stage renal disease (ESRD) [2–5]. CKD is defined as an estimated glomerular filtration rate (eGFR) < 60 ml/min-1.1.73 m-2 or increased levels of urinary albumin-to-creatinine ratio (UACR ≥ 30 mg/g) [6, 7]. T2DM is a major cause of progression to end-stage renal disease (ESRD). Diabetes-related ESRD severely affects the quality of life and treatment of patients, imposing a significant economic burden [8]. Therefore, delaying progression to ESRD is a major challenge in the management of patients with T2DM.

Insulin resistance (IR), which is characterized by the inability of cells to respond to the action of insulin, is a prominent feature of T2DM and is significantly associated with various diseases, including cardiovascular disease, CKD, and cognitive impairment [9–11]. Studies have shown that IR may be related to increased glomerular hydrostatic pressure, resulting in increased renal vascular permeability, eventually leading to glomerular hyperfiltration [8]. Another hypothesis is that metabolic changes associated with IR cause glomerulosclerosis, glomerular hypertrophy, tubulointerstitial inflammation, and fibrosis, ultimately leading to albuminuria and accelerated CKD progression [12, 13]. Therefore, assessing the IR is crucial for understanding the occurrence and progression of T2DM-related kidney diseases.

Studies have indicated that the TyG index, calculated by combining fasting glucose and triglycerides, has a strong correlation with IR, as evaluated by the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) and the Hyperinsulinemic-Euglycemic Clamp (HIEC) test [14–16]. Unlike the traditional IR assessment methods (HOMA-IR and HIEC), the TyG index is cost-effective and readily accessible. Multiple studies have demonstrated that the performance of the TyG index in the assessment of IR is consistent with or superior to that of HOMA-IR [17–19]. Thus, the TyG index is emerging as a novel and promising alternative marker for insulin resistance.

Numerous studies have demonstrated a close relationship between the TyG index, T2DM, and vascular lesions [20–24]. Wang L et al. reported that the TyG index, which is independent of known cardiovascular risk factors, can serve as an independent risk factor for predicting the occurrence of major adverse cardiovascular events in patients with T2DM [22]. Moreover, IR has been consistently associated with increased urinary albumin excretion. A cross-sectional study involving 9,872 participants revealed that the TyG index was closely related to albuminuria in the American population [25]. A large observational population-based cohort study conducted by Fritz et al. revealed that the TyG index was independently associated with an increased risk of end-stage renal disease (ESRD), and nearly half of the association between BMI and ESRD risk of ESRD is mediated by the TyG index [26]. Similarly, in a recent cohort study on middle-aged white men conducted by KUNUTSOR S K et al., a higher TyG index was associated with an increased risk of CKD [27]. However, there are few studies on the relationship between the TyG index, albuminuria, and CKD in individuals with T2DM. Therefore, in patients with a high incidence of T2DM, CKD, and albuminuria, clinicians should perform early TyG assessments to detect early kidney damage so that proactive interventions can be performed. This may reduce the incidence of chronic kidney disease and albuminuria, improve the prognosis, and minimize the risk of chronic complications associated with diabetes. Therefore, we aimed to analyze the relationship between the TyG index and the risk of albuminuria and CKD in patients with T2DM.

Materials and methods

Study population

The data come from the Diabetes Complications Data Set which were collected by the General Hospital of the People’s Liberation Army of China from 01/2013 to 12/2017 and it was stored in the Population Health Data Archive, The website is https://www.ncmi.cn/phda/dataDetails.do?id=CSTR:A0006.11.A0005.201905.000282 (Language is Chinese) The dataset contains 87 variables for 3,000 patients with T2DM [28]. The example data from the Diabetes Complications Data Set can be acquired and was uploaded as S1 Table. Once the application has been approved, the data can be viewed on the virtual platform, with specific operational usage uploaded as a support information file named S3 File. A secondary analysis of the data from this data set was performed. The study was a retrospective study and conducted using a publicly available database, the requirement of ethical approval for this was waived by the Institutional Review Board of the First Hospital of Jilin University. The need for written informed consent was waived by the Institutional Review Board of the First Hospital of Jilin University due to retrospective nature of the study.

A total of 3000 participants were included in the dataset. After identifying the diagnostic and exclusion criteria, 1048 participants were included. A flowchart of the study population screening is shown in Fig 1.

thumbnail
Download:
Fig 1. The flowchart of the study population screening.

https://doi.org/10.1371/journal.pone.0312374.g001

Data collection and definitions

The demographic information of the patients, including age, sex, height, weight, blood pressure, history, and medical history, was obtained from the acquired dataset. Laboratory tests: Laboratory indices collected in this study included fasting blood glucose (FPG), glycosylated hemoglobin (HbA1c), total protein (TP), albumin (Alb), total cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), creatinine, gamma-glutamyl transferase (GGT), uric acid, alanine aminotransferase (ALT), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), alkaline phosphatase (ALP) levels, and the urine albumin/creatinine ratio (UACR).

The TyG index formula is as follows: ln [FPG (mg/dl) × TG (mg/dl]). According to TyG tertiles, TyG ≤ 8.849 was the Q1 (350) group, TyG 8.849–9.538 was the Q2 (349) group, and TyG > 9.538 was the Q3 (349) group.

eGFR is calculated using the simplified Modification of Diet for Renal Disease (MDRM) formula: eGFR (ml/min-1.1.73 m-2) = 186 × serum creatinine-1.154×age-0.203(×0.724 for women).

The definition of albuminuria is a urinary albumin/creatinine ratio ≥ 30 mg/g.

DKD is defined as a DKD diagnosis when the study subject meets one or both of the following criteria: (1) an eGFR < 60 ml/min-1.1.73 m-2 or a UACR ≥ 30 mg/g.

Definition of glycemic control: Based on HbA1c, the participants were divided into groups with good glycemic control (< 7%) and groups with poor glycemic control (≥ 7%).

Statistical analysis

Data were analyzed using SPSS 25.0. Variables with an approximately normal distribution are expressed as the mean ± standard deviation. Variables with a non-normal distribution were presented as medians (interquartile ranges). The t-test or Mann–Whitney U test was used for group comparisons of continuous variables. Categorical variables were described as frequencies or percentages, and the chi-square test was used for group comparisons. Multivariate logistic regression models were used to calculate the odds ratio (OR) and 95% confidence intervals (95% CIs) to clarify the correlation between the TyG index and T2DM with albuminuria and CKD. Further adjustments for covariates and stratified analyses of variables were performed to validate the results. Differences were considered statistically significant at a two-sided significance level of P< 0.05.

Results

Baseline characteristics of participants

A total of 1048 participants met the inclusion criteria and were included in the analysis. The mean age was 46.21 years, mean body mass index (BMI) was 26.742 kg/m2, and 63.03% of the participants were men. Baseline participant characteristics stratified by the TyG index are shown in Table 1. Compared to those in group Q1, albuminuria, CKD, poor glycemic control, retinopathy, hyperuricemia, and fatty liver were more common in group Q3 (P < 0.05). Significant differences were detected in the levels of FPG, HBA1c, albumin, TG, TC, HDL, BUN, creatinine, UA, GGT, and ALP between the three groups (P < 0.05). Pairwise comparisons among groups Q3, Q1, and Q2 revealed statistically significant differences in FPG and HBA1c.

thumbnail
Download:
Table 1. Baseline characteristics of the participants with different TyG subtypes.

https://doi.org/10.1371/journal.pone.0312374.t001

Comparison of the proportions of patients with albuminuria and CKD detected in TyG index tertile subgroups

The detection rates of CKD and albuminuria in the included population were 42.8% (449/1048), and 41.2% (432/1048). Compared to the incidence rates in Group Q1, the incidence of CKD and albuminuria in Group Q3 significantly increased (χ2 = 9.669, P < 0.05; χ2 = 10.345, P < 0.05). The results are presented both in Fig 2 and Table 2.

thumbnail
Download:
Fig 2. The proportions of albuminuria and CKD detected in TyG index tertile subgroups.

Compared to the incidence rates in Group Q1, the incidence of CKD and albuminuria in Group Q3 significantly increased. ALB: albuminuria; CKD: chronic kidney disease.

https://doi.org/10.1371/journal.pone.0312374.g002

thumbnail
Download:
Table 2. Comparison of the prevalence of albuminuria and CKD among TYG tertile subgroups.

https://doi.org/10.1371/journal.pone.0312374.t002

Association of the risk of albuminuria in different TyG subgroups

In this study, we applied two models to investigate the relationship between TyG index and albuminuria. To analyze the results, we used the TyG tertiles as subgroups and albuminuria as the dependent variable. From Table 3 we can see that according to the unadjusted covariate factors of Model 1, the risk of albuminuria was significantly greater in Group Q3 than in Group Q1 (OR = 1.514, 95% CI 1.121–2.047; p = 0.007). According to Model 2, after adjusting for the covariates of hypertension, fatty liver, hyperlipidemia, coronary heart disease, HbA1c, sex, age, FPG level, retinopathy, and eGFR, the incidence of albuminuria was 2.241 times greater in group Q3 than in group Q1 (OR = 2.241, 95% CI 1.245–4.034; P = 0.007). Stratified analysis of the variables was conducted to verify the reliability of the results. Table 4 shows the results presented after stratification. After adjusting for other covariate factors, compared to incidence rates in Group Q1, the incidence of albuminuria in Group Q3 significantly increased across different categories, including sex (male, female), age (≤ 60 years, > 60 years), HbA1c (< 7%, ≥ 7%), hypertension (yes or no), and fatty liver (yes or no) (Table 4). In summary, TyG index is an independent predictor of albuminuria in patients with T2DM.

thumbnail
Download:
Table 3. Logistic regression analysis of albuminuria risk in different TyG subgroups.

https://doi.org/10.1371/journal.pone.0312374.t003

thumbnail
Download:
Table 4. Association of the TyG index with albuminuria in populations stratified by different variables.

https://doi.org/10.1371/journal.pone.0312374.t004

Associations between the risk of CKD and different TyG subgroups

To further investigate the relationship between TyG index and CKD, we used two models. In Model 1, which was not adjusted for covariates and was consistent with the urinary albumin results, compared with that in the Q1 group, the risk of CKD was significantly higher in the group Q3 (OR = 1.510, 95% CI 1.119–2.038, P = 0.007). Model 2 was adjusted for covariates, and after adjustment for hypertension, fatty liver, hyperlipidemia, coronary heart disease, HbA1c, sex, age, FPG, retinopathy, and eGFR, a 1.947-fold greater frequency of CKD was detected in the group Q3 than in the Q1 group (OR = 1.947, 95% CI 1.068–3.548, P = 0.030), we can see the results in Table 5. To validate the relationship between the TyG index and CKD, we conducted stratified analyses of the variables. After adjusting for covariates, the results showed that among patients categorized by sex (male, female), age (≤ 60 years, > 60 years), HbA1c (< 7%, ≥ 7%), hypertension (yes or no), and fatty liver (yes or no), the incidence of CKD in group Q3 was significantly greater than that in group Q1, Table 6 describes this result. Similarly, TyG index is an independent predictor of CKD in patients with T2DM.

thumbnail
Download:
Table 5. Logistic regression analysis of CKD risk in different TyG subgroups.

https://doi.org/10.1371/journal.pone.0312374.t005

thumbnail
Download:
Table 6. Association of the TyG index with CKD in populations stratified by different variables.

https://doi.org/10.1371/journal.pone.0312374.t006

Discussion

This study investigated the potential correlation between TyG index and the presence of albuminuria and CKD in patients with T2DM. In this single-center retrospective cohort study, we found a strong positive correlation between the TyG index, albuminuria, and CKD with T2DM. After adjusting for multiple covariates, the positive correlation remained stable, suggesting that baseline TyG index is an independent predictor of albuminuria and CKD in patients with T2DM. This association remained consistent across subgroups of sex, age, HbA1c level, hypertension, fatty liver, and hyperuricemia.

IR, an important feature of T2DM, is particularly associated with nephropathy and a decline in kidney function and is present in the early stages of CKD [29–31]. Albuminuria, a marker of early kidney damage, is closely associated with IR [32, 33]. PAN Y et al. reported that patients with an elevated TyG index have a greater risk of developing albuminuria, which is consistent with our results and is more pronounced in patients with poor glycemic control [34]. There are several possible mechanisms through which IR may be associated with kidney damage. The kidney contains a variety of insulin-sensitive cells that express insulin receptors [35]. When IR occurs, insulin signaling is impaired and glomerular filtration pressure is increased, leading to glomerular hyperfiltration [36], cytoskeletal rearrangement [37], mitochondrial dysfunction [38] and inflammation [35], resulting in renal hemodynamics, disruption of podocyte viability, and accelerated progression of CKD by facilitating glomerular capillary dilation and tubular fibrosis [12, 13] Therefore, early detection of IR is crucial for the prevention of kidney disease.

Currently, the gold standard for assessing IR is the HIEC test; however, it is invasive and expensive, used only for academic research, and not applied in clinical practice [39]. The widely used HOMA-IR index, which assesses β-cell function and insulin resistance, has limited value in patients undergoing insulin therapy or those without functional β-cells, and serum insulin measurement is not widely used in clinical practice [39]. The TyG index, calculated using FPG and TG levels, assesses IR in both diabetic and non-diabetic individuals and is equivalent to or even better than HOMA-IR [14–16, 40]. This alternative method is simple, convenient, and low-cost, does not require insulin quantification, and can be used in all patients regardless of their insulin treatment status [41]. Therefore, in our study, we adopted the TyG index as an alternative biomarker for IR because it is readily accessible in routine clinical practice and widely used.

The TyG index, a new surrogate for insulin resistance measurement, is increasingly supported by evidence of its important role in predicting vascular diseases [42, 43] A meta-analysis indicated that the TyG index is positively associated with the incidence rates of heart failure and adverse outcomes. Similarly, a study also suggests that the prevalence of other common cardiovascular and cerebrovascular diseases, including arteriosclerosis, coronary artery calcification, coronary artery disease, myocardial infarction, and atrial fibrillation, is significantly related to a high TyG index [44–50]. However, the association between the TyG index and renal microvascular damage was low. Our study showed that an elevated TyG index is significantly associated with the risk of albumin deficiency and CKD in patients with T2DM. A high TyG index has been shown to exacerbate renal dysfunction by promoting insulin resistance and related metabolic disturbances [50] and it is a reliable and convenient prognostic marker for adverse outcomes in patients with kidney disease [51]. A cohort study based on CHARLS data revealed an increased risk of CKD in the highest quartile compared to that in the lowest quartile [52], which is the same as that found in our study. Interestingly, Viñas et al. conducted basic experiments to demonstrate that female mice were more resistant to kidney injury than male mice [50, 53]. Saliba et al. reported the same result in humans, which may be related to the estrogen-AMPK pathway [54]. However, in our study, we did not find any difference in albumin and CKD levels by sex, even after adjusting for risk factors and sex for stratification; this may be related to the ethnicity of the included population, geographic region, and the number of patients included, which needs to be further explored.

Our study had several limitations. First, it was retrospective, precluding the establishment of a causal relationship between our variables of interest and outcomes, and there may have been reporting and selection biases. Second, although potential confounders were controlled as covariates in the multivariate regression model, it is important to acknowledge that the impact of unrecorded confounders cannot be categorically excluded. Finally, our study was a single-center study with a moderate sample size; future research should include large-scale multicenter longitudinal and prospective studies to further investigate the effects and mechanisms of the TyG index in T2DM patients with albuminuria and CKD.

Conclusions

In conclusion, this study revealed that the TyG index was positively associated with CKD and albuminuria in patients with T2DM. Our findings have clinical and public health significance because of the increasing prevalence of diabetes and growing population of patients with diabetes-related kidney injuries. Clinicians should perform early testing of the TyG index to identify early kidney damage, aggressively intervene to reduce proteinuria and CKD, and improve prognosis, thereby reducing the risk of chronic complications of diabetes mellitus.

Supporting information

S1 Table. The example data from the diabetes complications data set.

https://doi.org/10.1371/journal.pone.0312374.s001

(XLSX)

S1 File. Human participants research checklist.

https://doi.org/10.1371/journal.pone.0312374.s002

(PDF)

S2 File. Certificate_of_editing.

https://doi.org/10.1371/journal.pone.0312374.s003

(PDF)

S3 File. Virtual desktop user instructions.

https://doi.org/10.1371/journal.pone.0312374.s004

(PDF)

Acknowledgments

We are thankful for data from the Population Health Data Archive.

References

  1. 1. DeFronzo RA, Ferrannini E, Groop L, Henry RR, Herman WH, Holst JJ, et al. Type 2 diabetes mellitus. Nat Rev Dis Primers. 2015;1:15019. pmid:27189025
  2. 2. Polemiti E, Baudry J, Kuxhaus O, Jäger S, Bergmann MM, Weikert C, et al. BMI and BMI change following incident type 2 diabetes and risk of microvascular and macrovascular complications: the EPIC-Potsdam study. Diabetologia. 2021;64(4):814–25. pmid:33452586
  3. 3. Dal Canto E, Ceriello A, Rydén L, Ferrini M, Hansen TB, Schnell O, et al. Diabetes as a cardiovascular risk factor: An overview of global trends of macro and micro vascular complications. Eur J Prev Cardiol. 2019;26(2_suppl):25–32. pmid:31722562
  4. 4. Pugliese G, Penno G, Natali A, Barutta F, Di Paolo S, Reboldi G, et al. Diabetic kidney disease: new clinical and therapeutic issues. Joint position statement of the Italian Diabetes Society and the Italian Society of Nephrology on "The natural history of diabetic kidney disease and treatment of hyperglycemia in patients with type 2 diabetes and impaired renal function". J Nephrol. 2020;33(1):9–35. pmid:31576500
  5. 5. Bakris GL. Update on reducing the development of diabetic kidney disease and cardiovascular death in diabetes. Kidney Int Suppl (2011). 2018;8(1):1.
  6. 6. Levey AS, Becker C, Inker LA. Glomerular filtration rate and albuminuria for detection and staging of acute and chronic kidney disease in adults: a systematic review. Jama. 2015;313(8):837–46. pmid:25710660
  7. 7. Cui C, Liu L, Zhang T, Fang L, Mo Z, Qi Y, et al. Triglyceride-glucose index, renal function and cardiovascular disease: a national cohort study. Cardiovasc Diabetol. 2023;22(1):325. pmid:38017519
  8. 8. Smeijer JD, Kohan DE, Rossing P, Correa-Rotter R, Liew A, Tang SCW, et al. Insulin resistance, kidney outcomes and effects of the endothelin receptor antagonist atrasentan in patients with type 2 diabetes and chronic kidney disease. Cardiovasc Diabetol. 2023;22(1):251. pmid:37716952
  9. 9. Hill MA, Yang Y, Zhang L, Sun Z, Jia G, Parrish AR, et al. Insulin resistance, cardiovascular stiffening and cardiovascular disease. Metabolism. 2021;119:154766. pmid:33766485
  10. 10. Leyking S, Fliser D. Insulin resistance in CKD. Clin J Am Soc Nephrol. 2014;9(4):638–40. pmid:24677558
  11. 11. Biessels GJ, Reagan LP. Hippocampal insulin resistance and cognitive dysfunction. Nat Rev Neurosci. 2015;16(11):660–71. pmid:26462756
  12. 12. Sasson AN, Cherney DZ. Renal hyperfiltration related to diabetes mellitus and obesity in human disease. World J Diabetes. 2012;3(1):1–6. pmid:22253940
  13. 13. Alicic RZ, Rooney MT, Tuttle KR. Diabetic Kidney Disease: Challenges, Progress, and Possibilities. Clin J Am Soc Nephrol. 2017;12(12):2032–45. pmid:28522654
  14. 14. Xue Y, Xu J, Li M, Gao Y. Potential screening indicators for early diagnosis of NAFLD/MAFLD and liver fibrosis: Triglyceride glucose index-related parameters. Front Endocrinol (Lausanne). 2022;13:951689. pmid:36120429
  15. 15. 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. pmid:38419039
  16. 16. Sánchez-García A, Rodríguez-Gutiérrez R, Mancillas-Adame L, González-Nava V, Díaz González-Colmenero A, Solis RC, et al. Diagnostic Accuracy of the Triglyceride and Glucose Index for Insulin Resistance: A Systematic Review. Int J Endocrinol. 2020;2020:4678526. pmid:32256572
  17. 17. Wang S, Shi J, Peng Y, Fang Q, Mu Q, Gu W, et al. Stronger association of triglyceride glucose index than the HOMA-IR with arterial stiffness in patients with type 2 diabetes: a real-world single-centre study. Cardiovasc Diabetol. 2021;20(1):82. pmid:33888131
  18. 18. Park HM, Lee HS, Lee YJ, Lee JH. The triglyceride-glucose index is a more powerful surrogate marker for predicting the prevalence and incidence of type 2 diabetes mellitus than the homeostatic model assessment of insulin resistance. Diabetes Res Clin Pract. 2021;180:109042. pmid:34506839
  19. 19. Kim KS, Hong S, Hwang YC, Ahn HY, Park CY. Evaluating Triglyceride and Glucose Index as a Simple and Easy-to-Calculate Marker for All-Cause and Cardiovascular Mortality. J Gen Intern Med. 2022;37(16):4153–9. pmid:35676587
  20. 20. 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. pmid:38281973
  21. 21. Wang M, Zhou L, Su W, Dang W, Li H, Chen H. Independent and joint associations between the triglyceride-glucose index and NT-proBNP with the risk of adverse cardiovascular events in patients with diabetes and acute coronary syndrome: a prospective cohort study. Cardiovasc Diabetol. 2023;22(1):149. pmid:37365593
  22. 22. Wang L, Cong HL, Zhang JX, Hu YC, Wei A, Zhang YY, et al. Triglyceride-glucose index predicts adverse cardiovascular events in patients with diabetes and acute coronary syndrome. Cardiovasc Diabetol. 2020;19(1):80. pmid:32534586
  23. 23. Lin Z, He J, Yuan S, Song C, Bian X, Yang M, et al. Glycemic control and cardiovascular outcomes in patients with diabetes and coronary artery disease according to triglyceride-glucose index: a large-scale cohort study. Cardiovasc Diabetol. 2024;23(1):11. pmid:38184572
  24. 24. Zhang Y, Wu Z, Li X, Wei J, Zhang Q, Wang J. Association between the triglyceride-glucose index and carotid plaque incidence: a longitudinal study. Cardiovasc Diabetol. 2022;21(1):244. pmid:36380351
  25. 25. Wang Z, Qian H, Zhong S, Gu T, Xu M, Yang Q. The relationship between triglyceride-glucose index and albuminuria in United States adults. Front Endocrinol (Lausanne). 2023;14:1215055. pmid:37680883
  26. 26. Fritz J, Brozek W, Concin H, Nagel G, Kerschbaum J, Lhotta K, et al. The Triglyceride-Glucose Index and Obesity-Related Risk of End-Stage Kidney Disease in Austrian Adults. JAMA Netw Open. 2021;4(3):e212612. pmid:33787913
  27. 27. Kunutsor SK, Seidu S, Kurl S, Laukkanen JA. Baseline and usual triglyceride-glucose index and the risk of chronic kidney disease: a prospective cohort study. Geroscience. 2024;46(3):3035–46. pmid:38180700
  28. 28. [Chinese People’s Liberation Army General Hospital. Diabetes Complications Data Set]. 2022.[Chinese]
  29. 29. de Boer IH, Mehrotra R. Insulin resistance in chronic kidney disease: a step closer to effective evaluation and treatment. Kidney Int. 2014;86(2):243–5. pmid:25079023
  30. 30. Demir S, Nawroth PP, Herzig S, Ekim Üstünel B. Emerging Targets in Type 2 Diabetes and Diabetic Complications. Adv Sci (Weinh). 2021;8(18):e2100275. pmid:34319011
  31. 31. Bjornstad P, Choi YJ, Platnick C, Gross S, Narongkiatikhun P, Melena I, et al. Insulin Secretion, Sensitivity, and Kidney Function in Young Individuals With Type 2 Diabetes. Diabetes Care. 2024;47(3):409–17. pmid:38153805
  32. 32. Krok KL, Milwalla F, Maheshwari A, Rankin R, Thuluvath PJ. Insulin resistance and microalbuminuria are associated with microvascular disease in patients with cirrhosis. Liver Transpl. 2009;15(9):1036–42. pmid:19718631
  33. 33. Dong J, Yang H, Zhang Y, Chen L, Hu Q. A high triglyceride glucose index is associated with early renal impairment in the hypertensive patients. Front Endocrinol (Lausanne). 2022;13:1038758. pmid:36589813
  34. 34. Pan Y, Zhong S, Zhou K, Tian Z, Chen F, Liu Z, et al. Association between Diabetes Complications and the Triglyceride-Glucose Index in Hospitalized Patients with Type 2 Diabetes. Journal of Diabetes Research. 2021;2021:8757996. pmid:34671683
  35. 35. Coward RJ, Welsh GI, Yang J, Tasman C, Lennon R, Koziell A, et al. The human glomerular podocyte is a novel target for insulin action. Diabetes. 2005;54(11):3095–102. pmid:16249431
  36. 36. Tiwari S, Sharma N, Gill PS, Igarashi P, Kahn CR, Wade JB, et al. Impaired sodium excretion and increased blood pressure in mice with targeted deletion of renal epithelial insulin receptor. Proc Natl Acad Sci U S A. 2008;105(17):6469–74. pmid:18424559
  37. 37. Welsh GI, Hale LJ, Eremina V, Jeansson M, Maezawa Y, Lennon R, et al. Insulin signaling to the glomerular podocyte is critical for normal kidney function. Cell Metab. 2010;12(4):329–40. pmid:20889126
  38. 38. Sharma K. Mitochondrial hormesis and diabetic complications. Diabetes. 2015;64(3):663–72. pmid:25713188
  39. 39. Minh HV, Tien HA, Sinh CT, Thang DC, Chen CH, Tay JC, et al. Assessment of preferred methods to measure insulin resistance in Asian patients with hypertension. J Clin Hypertens (Greenwich). 2021;23(3):529–37. pmid:33415834
  40. 40. Simental-Mendía LE, Rodríguez-Morán M, Guerrero-Romero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab Syndr Relat Disord. 2008;6(4):299–304. pmid:19067533
  41. 41. Lee SH, Kwon HS, Park YM, Ha HS, Jeong SH, Yang HK, et al. Predicting the development of diabetes using the product of triglycerides and glucose: the Chungju Metabolic Disease Cohort (CMC) study. PLoS One. 2014;9(2):e90430. pmid:24587359
  42. 42. Lee EY, Yang HK, Lee J, Kang B, Yang Y, Lee SH, et al. Triglyceride glucose index, a marker of insulin resistance, is associated with coronary artery stenosis in asymptomatic subjects with type 2 diabetes. Lipids Health Dis. 2016;15(1):155. pmid:27633375
  43. 43. Jin JL, Sun D, Cao YX, Guo YL, Wu NQ, Zhu CG, et al. Triglyceride glucose and haemoglobin glycation index for predicting outcomes in diabetes patients with new-onset, stable coronary artery disease: a nested case-control study. Ann Med. 2018;50(7):576–86. pmid:30207490
  44. 44. Liang S, Wang C, Zhang J, Liu Z, Bai Y, Chen Z, et al. Triglyceride-glucose index and coronary artery disease: a systematic review and meta-analysis of risk, severity, and prognosis. Cardiovascular Diabetology. 2023;22(1):170. pmid:37415168
  45. 45. Khalaji A, Behnoush AH, Khanmohammadi S, Ghanbari Mardasi K, Sharifkashani S, Sahebkar A, et al. Triglyceride-glucose index and heart failure: a systematic review and meta-analysis. Cardiovasc Diabetol. 2023;22(1):244. pmid:37679763
  46. 46. 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. pmid:35778731
  47. 47. Azarboo A, Behnoush AH, Vaziri Z, Daneshvar MS, Taghvaei A, Jalali A, et al. Assessing the association between triglyceride-glucose index and atrial fibrillation: a systematic review and meta-analysis. Eur J Med Res. 2024;29(1):118. pmid:38347644
  48. 48. Liu F, Ling Q, Xie S, Xu Y, Liu M, Hu Q, et al. Association between triglyceride glucose index and arterial stiffness and coronary artery calcification: a systematic review and exposure-effect meta-analysis. Cardiovasc Diabetol. 2023;22(1):111. pmid:37179288
  49. 49. Yang Y, Huang X, Wang Y, Leng L, Xu J, Feng L, et al. The impact of triglyceride-glucose index on ischemic stroke: a systematic review and meta-analysis. Cardiovasc Diabetol. 2023;22(1):2. pmid:36609319
  50. 50. Fritz J, Brozek W, Concin H, Nagel G, Kerschbaum J, Lhotta K, et al. The Association of Excess Body Weight with Risk of ESKD Is Mediated Through Insulin Resistance, Hypertension, and Hyperuricemia. J Am Soc Nephrol. 2022;33(7):1377–89. pmid:35500938
  51. 51. Yildirim F, Yildiz AB, Kanbay M. A promising tool: triglyceride-glucose index to stratify the risk of cardiovascular events in chronic kidney disease. Clin Kidney J. 2022;15(9):1653–6. pmid:36003667
  52. 52. Ren X, Jiang M, Han L, Zheng X. Association between triglyceride-glucose index and chronic kidney disease: A cohort study and meta-analysis. Nutrition, Metabolism and Cardiovascular Diseases. 2023;33(6):1121–8. pmid:37088649
  53. 53. Viñas JL, Porter CJ, Douvris A, Spence M, Gutsol A, Zimpelmann JA, et al. Sex diversity in proximal tubule and endothelial gene expression in mice with ischemic acute kidney injury. Clin Sci (Lond). 2020;134(14):1887–909. pmid:32662516
  54. 54. Lee HJ, Min L, Gao J, Matta S, Drel V, Saliba A, et al. Female protection against diabetic kidney disease is regulated by kidney-specific AMPK activity. Diabetes. 2024. pmid:38656940