
==== Front
Diabetes Metab Syndr Obes
Diabetes Metab Syndr Obes
dmso
Diabetes, Metabolic Syndrome and Obesity
1178-7007
Dove

477836
10.2147/DMSO.S477836
Original Research
The Association Between Triglyceride Glucose-Body Mass Index and Kidney Impairment in Patients with Type 2 Diabetes Mellitus
Huang et al
Huang et al
http://orcid.org/0009-0000-7168-0929
Huang Nan 1 2 3 *
Lu Bing 4 *
Zhu Zhuan-Zhuan 5 *
Zhu Xiang-Yun 1 3
Chen Sheng 1 2 3
Shu Zhi-Yi 1 2 3
Liu Gai-Fang 6
Peng You-Fan 7 *
Li Ling 1 2 3 *
1 Department of Endocrinology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, People’s Republic of China
2 Key Laboratory of Environmental Medicine Engineering of Ministry of Education, Southeast University, Nanjing, Jiangsu, 210009, People’s Republic of China
3 Pancreatic Research Institute, Southeast University, Nanjing, Jiangsu, 210009, People’s Republic of China
4 Department of Endocrinology, Kunshan Hospital Affiliated to Jiangsu University, Kunshan, Jiangsu, 215343, People’s Republic of China
5 Department of Endocrinology, Affiliated People’s Hospital of Jiangsu University, Zhenjiang, Jiangsu, 212002, People’s Republic of China
6 Division of Gastroenterology, Hebei General Hospital, Shijiazhuang, Hebei, 050000, People’s Republic of China
7 Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi, 533000, People’s Republic of China
Correspondence: Ling Li, Department of Endocrinology, Zhongda Hospital, School of Medicine, Southeast University, No. 87 Dingjiaqiao, Nanjing, Jiangsu, 210009, People’s Republic of China, Email lingli@seu.edu.cn
You-Fan Peng, Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Youjiang Medical University for Nationalities, No. 18 Zhongshan 2nd Road, Baise, Guangxi, 533000, People’s Republic of China, Email youfanpeng@hotmail.com
* These authors contributed equally to this work

16 9 2024
2024
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11 5 2024
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© 2024 Huang et al.
2024
Huang et al.
https://creativecommons.org/licenses/by-nc/3.0/ This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
Purpose

Insulin resistance is associated with kidney impairment in patients with type 2 diabetes mellitus (T2DM). The triglyceride glucose-body mass index (TyG-BMI), which combines the TyG index with body mass index (BMI), has received significant attention as a tool for evaluating insulin resistance. Thus, the aim of this study was to explore the association between TyG-BMI and kidney impairment in patients with type 2 diabetes mellitus (T2DM).

Patients and Methods

The cross-sectional analysis included 1080 patients with T2DM, and data were collected retrospectively. TyG-BMI was calculated by fasting blood glucose, triglyceride, and body mass index.

Results

TyG-BMI was significantly higher in T2DM patients with albuminuria than those without albuminuria (232.16 [206.52–268.02] vs 229.83 [206.11–255.64], p =0.023). T2DM patients with chronic kidney disease (CKD) showed a significantly higher value of TyG-BMI compared with those without CKD (232.23 [206.46–268.28] vs 229.73 [206.11–255.49], p=0.014). Correlation analysis showed a significantly positive association between TyG-BMI and metabolic parameters including BMI (r = 0.866, p < 0.001), TG (r = 0.630, p < 0.001), TC (r = 0.119, p < 0.001), HDL-C (r = −0.374, p < 0.001), FBG (r = 0.297, p < 0.001), and HbA1c (r = 0.116, p < 0.001) in patients with T2DM. The binary logistic regression analysis found that TyG-BMI was an independent factor for albuminuria (OR = 1.004, 95% CI: 1.001–1.008, p = 0.010) and CKD (OR = 1.005, 95% CI: 1.001–1.008, p = 0.005) in patients with T2DM respectively.

Conclusion

The study suggests that TyG-BMI is associated with kidney impairment in patients with T2DM. Given that TyG-BMI is a novel parameter of insulin resistance, the study results indicates that clinicians should pay close attention to screening for kidney impairment in T2DM patients with insulin resistance.

Keywords

type 2 diabetes mellitus
TyG-BMI
insulin resistance
kidney impairment
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pmcIntroduction

Type 2 diabetes mellitus (T2DM) is a common chronic disease characterized by hyperglycemia.1 T2DM often leads to various complications, including diabetic retinopathy, diabetic nephropathy, and diabetic neuropathy.2 Diabetic nephropathy is the most prevalent complication of T2DM, and it is a main cause of end-stage kidney disease worldwide.3 However, due to its insidious onset, lack of early symptoms, and insufficient patient awareness, diabetic nephropathy is often unnoticed, leading to the underestimation of its morbidity.4 Moreover, early kidney damage in patients with T2DM may gradually progress to chronic kidney disease (CKD) which poses a serious threat to their health.5 Clinically, the assessments of early diabetic nephropathy mainly depend on monitoring urinary protein, with microalbuminuria (MAU) being a sensitive marker for early kidney impairment in patients with T2DM.6 Nevertheless, the measurement of urinary protein can be affected by factors such as exercise, menstruation, infection, and fever, making it challenging to ensure the stability and specificity of detection.7 Estimation of glomerular filtration rate (eGFR), as a calculated value based on age and serum creatinine (Cr) levels, may not detect early kidney impairment due to the high compensatory capacity of the kidneys and the lack of changes in serum Cr levels during the early stages of kidney damage.8 Therefore, exploring additional markers are important for identifying early-stage of kidney damage, which has practical clinical significance for early warning and prevention of kidney damagein patients with T2DM.

The triglyceride glucose (TyG), a calculated index derived from fasting triglycerides (TG) and fasting blood glucose (FBG), has been shown to be a reliable marker for insulin resistance.9 Several studies have demonstrated a close correlation between TyG and kidney impairment in patients with T2DM, indicating that TyG serves as a biomarker for diabetic nephropathy. It has reported that TyG is progressively associated with the risk of diabetic nephropathy.10–12 In recent years, the triglyceride glucose-body mass index (TyG-BMI), a combination of TyG index and body mass index (BMI), has been received more attention. TyG-BMI has been reported to be a better marker for reflecting insulin resistance.13 And insulin resistance has been demonstrated to be associated with kidney impairment in patients with T2DM.14,15 Thus, the purpose of this study was to evaluate the association between TyG-BMI and kidney impairment in patients with T2DM.

Materials and Methods

Patients

The cross-sectional analysis included 1080 patients with T2DM who visited the National Metabolic Management Center of the Zhongda Hospital, Southeast University (Nanjing, China) between January 2021 and December 2022. The diagnosis of T2DM was based on the American Diabetes Association criteria.16 T2DM patients with following conditions were excluded: systemic autoimmune disease, urinary tract infection, and cancer. The study was approved by the Ethics Committees at Zhongda Hospital, Southeast University and was in compliance with the Helsinki Declaration. Patients’ informed consent was waived by Ethics Committees at Zhongda Hospital, Southeast University, because the study data were collected retrospectively. The study confirmed that participants’ privacy was strictly protected.

Data

The study data were obtained retrospectively from electronic information records. Demographic and clinical information comprised gender, age, height, weight, and medical history. The biochemical data comprised triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), fasting blood glucose (FBG), hemoglobin A1c (HbA1c), blood creatinine (Cr), urine Cr, and urine albumin. The results of biochemical tests were from fasting overnight blood and random urine samples in this study.

Definition of Index

The body mass index (BMI)=weight (kg)/height (m)2. TyG-BMI = BMI × TyG index, TyG index = Ln [1/2FBG (mg/dL) × TG (mg/dL)].13 Urine albumin to urine Cr ratio (ACR) = urine albumin (mg/L)/ urine Cr (mg/dL). The patients with T2DM were classified as patients with albuminuria and those without albuminuria according to the laboratory reference range of ACR (0–30 mg/g). The patients without albuminuria were defined as ACR ≤ 30 mg/g, and the patients with albuminuria were defined as ACR > 30 mg/g. The estimated glomerular filtration rate (eGFR) was calculated by modified glomerular filtration rate estimating equation with adjustment for Chinese, and the chronic kidney diseases (CKD) in T2DM patients was defined as eGFR < 60 mL/min/1.73m2 and (or) ACR > 30 mg/g.17

Statistical Analyses

Continuous variables of non-normal distribution are presented as median (interquartile range), and categorical variables are presented as numbers (percentages). Mann–Whitney U-test or Chi-square test was used to compare the statistical significance between the two groups. Correlations were assessed by Spearman’s test. Binary logistic regression analysis was performed to examine the factors associated dependent variable. A p < 0.05 was considered statistically significant. The statistical analyses were performed with SPSS 25.0 software version.

Results

The Comparison of Characteristics in T2DM Patients with and those without Early Kidney Impairment

The characteristics were compared in T2DM patients with albuminuria and those without albuminuria, as shown in Table 1. The TyG-BMI was significantly elevated in T2DM patients with albuminuria compared with those without albuminuria (232.16 [206.52–268.02] vs 229.83 [206.11–255.64], p =0.023). There were significantly differences in age (p=0.048), hypertension history (p < 0.001), TG (p < 0.001), HDL-C (p = 0.015), LDL-C (p = 0.007), FBG (p = 0.016), and HbA1c (p = 0.025) between T2DM patients with albuminuria and those without albuminuria.Table 1 Characteristics of Type 2 Diabetes Mellitus Patients with Albuminuria and those Without Albuminuria

	T2DM with Albuminuria (N =384)	T2DM without Albuminuria (N =696)	p value	
Male (n, %)	298 (77.60)	507 (72.84)	0.086	
Age (years)	58 (50–65)	57 (48–64)	0.048	
Hypertension history (n, %)	224 (58.33)	293 (42.10)	< 0.001	
BMI (kg/m2)	25.05 (22.85–27.75)	25.00 (23.17–26.00)	0.757	
TG (mmol/L)	1.55 (1.10–2.42)	1.36 (2.03–0.96)	< 0.001	
TC (mmol/L)	4.38 (3.60–5.25)	4.43 (3.74–5.18)	0.823	
HDL-C (mmol/L)	1.06 (0.88–1.25)	1.11 (0.92–1.30)	0.015	
LDL-C (mmol/L)	2.35 (1.80–2.99)	2.58 (2.00–3.05)	0.007	
FBG (mmol/L)	8.38 (6.33–11.97)	7.69 (6.15–10.67)	0.016	
HbA1c (%)	8.88 (7.56–10.57)	8.64 (7.09–10.14)	0.025	
TyG-BMI	232.16 (206.52–268.02)	229.83 (206.11–255.64)	0.023	
Abbreviations: BMI, Body Mass Index; TG, Triglycerides; TC, Total Cholesterol; HDL-C, High-Density Lipoprotein Cholesterol; LDL-C, Low-Density Lipoprotein Cholesterol; FBG, Fasting Blood Glucose; HbA1c, Hemoglobin A1c; TyG-BMI, Triglyceride Glucose-Body Mass Index.

The Comparison of Characteristics in T2DM Patients with CKD and those Without CKD

The characteristics were compared in T2DM patients with CKD and those without CKD, as shown in Table 2. Compared with T2DM patients without CKD, the TyG-BMI was significantly elevated in T2DM patients with CKD (232.23 [206.46–268.28] vs 229.73 [206.11–255.49], p = 0.014). There were significantly differences in age (p = 0.013), hypertension history (p < 0.001), TG (p < 0.001), HDL-C (p = 0.010), LDL-C (p = 0.005), FBG (p = 0.019), and HbA1c (p = 0.021) between T2DM patients with CKD and those without CKD.Table 2 Characteristics of Type 2 Diabetes Mellitus Patients with and those without Chronic Kidney Disease

	T2DM with CKD (N =391)	T2DM without CKD (N =689)	p value	
Male (n, %)	302 (77.24)	503 (73.00)	0.125	
Age (years)	58 (50–66)	57 (48–64)	0.013	
Hypertension history (n, %)	230 (58.82)	287(41.65)	< 0.001	
BMI (kg/m2)	25.06 (22.85–27.76)	24.98 (23.17–26.99)	0.648	
TG (mmol/L)	1.55 (1.10–2.43)	1.36 (0.96–2.03)	< 0.001	
TC (mmol/L)	4.38 (3.62–5.26)	4.43 (3.73–5.16)	0.883	
HDL-C (mmol/L)	1.06 (0.88–1.25)	1.11 (0.92–1.30)	0.010	
LDL-C (mmol/L)	2.35 (1.80–2.98)	2.59 (2.00–3.06)	0.005	
FBG (mmol/L)	8.36 (6.33–11.92)	7.69 (6.15–10.70)	0.019	
HbA1c (%)	8.89 (7.55–10.56)	8.63 (7.08–10.13)	0.021	
TyG-BMI	232.23 (206.46–268.28)	229.73 (206.11–255.49)	0.014	
Abbreviations: BMI, Body Mass Index; TG, Triglycerides; TC, Total Cholesterol; HDL-C, High-Density Lipoprotein Cholesterol; LDL-C, Low-Density Lipoprotein Cholesterol; FBG, Fasting Blood Glucose; HbA1c, Hemoglobin A1c; TyG-BMI, Triglyceride Glucose-Body Mass Index.

The Correlation Between TyG-BMI and Metabolic Parameters in Patients with T2DM

TyG-BMI showed a significant correlation with metabolic parameters in Table 3, including BMI (r = 0.866, p < 0.001), TG (r = 0.630, p < 0.001), TC (r = 0.119, p < 0.001), HDL-C (r = −0.374, p < 0.001), FBG (r = 0.297, p < 0.001), and HbA1c (r = 0.116, p < 0.001) in patients with T2DM. However, there was no correlation between TyG-BMI and LDL-C (r = −0.003, p = 0.912) in these patients.Table 3 The Correlation Between TyG-BMI and Metabolic Parameters in Patients with Type 2 Diabetes Mellitus

Variables	r	p value	
BMI	0.866	< 0.001	
TG	0.630	< 0.001	
TC	0.119	< 0.001	
HDL-C	−0.374	< 0.001	
LDL-C	−0.003	0.912	
FBG	0.297	< 0.001	
HbA1c	0.116	< 0.001	
Abbreviations: BMI, Body Mass Index; TG, Triglycerides; TC, Total Cholesterol; HDL-C, High-Density Lipoprotein Cholesterol; LDL-C, Low-Density Lipoprotein Cholesterol; FBG, Fasting Blood Glucose; HbA1c, Hemoglobin A1c; TyG-BMI, Triglyceride Glucose-Body Mass Index.

Multivariable Analysis of Risk Factors Associated with Albuminuria and CKD in Patients with T2DM

Two multivariable analyses were constructed to assess whether TyG-BMI was associated with albuminuria and CKD in patients with T2DM, as shown in Tables 4 and 5. Sex, age, hypertension history, HDL-C, LDL-C, HbA1c, and TyG-BMI were included as independent variables, while albuminuria and CKD were considered as dependent variables in binary logistic regression analysis, respectively. The results showed that TyG-BMI was significantly associated with presence of albuminuria (OR = 1.004, 95% CI: 1.001–1.008, p = 0.010) and CKD (OR = 1.005, 95% CI: 1.001–1.008, p = 0.005) in patients with T2DM, respectively. Furthermore, both hypertension history and HbA1c were significantly associated with albuminuria (OR = 1.819, 95% CI: 1.380–2.398, p = < 0.001; OR = 1.100, 95% CI: 1.033–1.172, p = 0.003, respectively) and CKD (OR = 1.848, 95% CI: 1.403–2.435, p < 0.001; OR = 1.105, 95% CI: 1.037–1.177, p = 0.002, respectively) in patients with T2DM.Table 4 The Presence of Albuminuria as Dependent Variable in Binary Logistic Regression Analysis

Variables	B	SE	Wald	OR (95% CI)	p value	
Male	0.328	0.159	4.272	1.388 (1.017–1.894)	0.039	
Age	0.011	0.006	2.795	1.011 (0.998–1.024)	0.095	
Hypertension history	0.598	0.141	18.036	1.819 (1.380–2.398)	< 0.001	
HDL-C	0.103	0.241	0.184	1.109 (0.692–1.777)	0.668	
LDL-C	−0.155	0.083	3.425	0.857 (0.728–1.009)	0.064	
HbA1c	0.095	0.032	8.778	1.100 (1.033–1.172)	0.003	
TyG-BMI	0.004	0.002	6.633	1.004 (1.001–1.008)	0.010	
Abbreviations: SE, Standard Error; HDL-C, High-Density Lipoprotein Cholesterol; LDL-C, Low-Density Lipoprotein Cholesterol; HbA1c, Hemoglobin A1c; TyG-BMI, Triglyceride Glucose-Body Mass Index.

Table 5 The Presence of Chronic Kidney Disease as Dependent Variable in Binary Logistic Regression Analysis

Variables	B	SE	Wald	OR (95% CI)	p value	
Male	0.302	0.158	3.654	1.353 (0.992–1.844)	0.056	
Age	0.014	0.006	4.569	1.014 (1.001–1.027)	0.033	
Hypertension history	0.614	0.141	19.109	1.848 (1.403–2.435)	< 0.001	
HDL-C	0.062	0.241	0.067	1.064 (0.664–1.706)	0.796	
LDL-C	−0.152	0.083	3.331	0.859 (0.729–1.011)	0.068	
HbA1c	0.099	0.032	9.537	1.105 (1.037–1.177)	0.002	
TyG-BMI	0.005	0.002	7.938	1.005 (1.001–1.008)	0.005	
Abbreviations: SE, Standard Error; HDL-C, High-Density Lipoprotein Cholesterol; LDL-C, Low-Density Lipoprotein Cholesterol; HbA1c, Hemoglobin A1c; TyG-BMI, Triglyceride Glucose-Body Mass Index.

Discussion

The current study examined the relationship between the TyG-BMI and kidney impairment in patients with T2DM. A significantly higher TyG-BMI was observed in T2DM patients with kidney impairment compared to those without kidney impairment, and TyG-BMI was found to be substantially associated with albuminuria and CKD in these patients.

Insulin resistance is the basis for many metabolic disorders, including T2DM, where impairments in insulin signaling disrupt the entry of glucose into the adipocytes and skeletal muscle cells.18 Insulin resistance contributes to the development of glomerular hypertrophy and renal interstitial fibrosis, which can potentially accelerate the progression of CKD.19 Insulin resistance is a major cause of diabetes complications, and plays a crucial role in the occurrence and progression of diabetic nephropathy.20,21 Insulin resistance is also evident in the early of renal impairment of T2DM and may accelerate the decline of kidney function.22 A bidirectional association between insulin resistance and CKD has been suggested in T2DM, and insulin resistance is a predictor of CKD progression, while the progressive CKD with uremia is associated with the deterioration of insulin resistance.14,23 Therefore, evaluating insulin resistance is clinically important, especially in patients with T2DM. The assessment criteria for insulin resistance includes hyper-insulinemic euglycemic clamp (HIEC) test and homeostasis model Assessment of IR (HOMA-IR).24,25 However, recent studies have demonstrated a significant association between the TyG index and insulin resistance.9,26,27 Compared to traditional methods for assessing insulin resistance, the TyG index is considered a simple and promising new biomarker.9 Clinical studies have indicated that the TyG index is linked to the development of both macrovascular and microvascular complications in patients with T2DM, particularly in diabetic nephropathy.28–30 Furthermore, TyG-BMI is calculated by TyG index and BMI index, which represents a combination of FBG, TG, and BMI. As a combined parameter, the TyG-BMI can more comprehensively reflect the insulin resistance levels compared to individual markers.13,31,32 Our study suggested that the TyG-BMI was associated with MAU and CKD in patients with T2DM, indicating that TyG-BMI is related to kidney impairment in T2DM.

Hypertension is a well-established risk factor for kidney impairment in patients with T2DM.33–35 In addition to the TyG-BMI, we found that a history of hypertension is significantly associated with kidney impairment in these patients. Elevated levels of HbA1c have been shown to be related with both microvascular and macrovascular events.36,37 Lin CC et al have observed that HbA1c is associated with the presence of diabetic nephropathy in patients with T2DM.38 Consistent with these findings, we also observed a significant association between HbA1c levels and kidney impairment in patients with T2DM.

There are several limitations to this study. First, due to its cross-sectional design, it is unclear whether TyG-BMI is a predictor of kidney impairment in patients with T2DM. Second, although the main confounders were considered, other factors such as genetic predispositions, medication use, and lifestyle habitsmay also influence the results. Third, the study only used eGFR and ACR to assess kidney impairment, and only single measurement of eGFR and ACR may not completely reflect the kidney injury in patients with T2DM. Based on the limitations mentioned above, a multicenter cohort study needs to be conducted in the future to investigate whether TyG-BMI could be a potential predictor of kidney impairment in patients with T2DM. Additionally, the comprehensive assessments of lifestyle habits, medication use, and laboratory indicators should be taken into account as potential confounding factors to explore the predictive capacity of the TyG-BMI for the onset and progression of kidney impairment in patients with T2DM.

Conclusion

The study observed that increased TyG-BMI was significantly associated with MAU and CKD, suggesting that TyG-BMI is associated with kidney impairment in patients with T2DM. Give that TyG-BMI is a novel parameter of insulin resistance, in clinical practice, the study results indicate that clinicians should pay more attention to screening for kidney impairment in T2DM patients with insulin resistance.

Data Sharing Statement

The data underlying this article is available from the corresponding author under reasonable request.

Ethics Statement

The study was reviewed and approved by the Ethics Committees at Zhongda Hospital, Southeast University and was in compliance with the Helsinki Declaration. Patients’ informed consent was waived by Ethics Committees at Zhongda Hospital Southeast University, because the study data were collected retrospectively. The study confirmed that participants’ privacy was strictly protected.

Disclosure

The authors declare that they have no competing interests.
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References

1. Brunton S. Pathophysiology of type 2 diabetes: the evolution of our understanding. J Fam Pract. 2016;65 (4 Suppl ).
2. Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol. 2018;14 (2 ):88–98. doi:10.1038/nrendo.2017.151 29219149
3. Sagoo MK, Gnudi L. Diabetic Nephropathy: an Overview. Methods Mol Biol. 2020;2067 :3–7.31701441
4. Alicic RZ, Rooney MT, Tuttle KR. Diabetic Kidney Disease: challenges, Progress, and Possibilities. Clin J Am Soc Nephrol. 2017;12 (12 ):2032–2045. doi:10.2215/CJN.11491116 28522654
5. Navaneethan SD, Zoungas S, Caramori ML, et al. Diabetes management in chronic kidney disease: synopsis of the 2020 KDIGO clinical practice guideline. Ann Intern Med. 2021;174 (3 ):385–394. doi:10.7326/M20-5938 33166222
6. Thipsawat S. Early detection of diabetic nephropathy in patient with type 2 diabetes mellitus: a review of the literature. Diab Vasc Dis Res. 2021;18 (6 ):14791641211058856. doi:10.1177/14791641211058856 34791910
7. National Kidney F. KDOQI clinical practice guideline for diabetes and CKD: 2012 update. Am J Kidney Dis. 2012;60 (5 ):850–886.23067652
8. Macisaac RJ, Ekinci EI, Jerums G. Markers of and risk factors for the development and progression of diabetic kidney disease. Am J Kidney Dis. 2014;63 (2 Suppl 2 ):S39–62. doi:10.1053/j.ajkd.2013.10.048 24461729
9. 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 ):986–988. doi:10.47391/JPMA.22-63 35713073
10. Wang H, Chen G, Sun D, Ma Y. The threshold effect of triglyceride glucose index on diabetic kidney disease risk in patients with type 2 diabetes: unveiling a non-linear association. Front Endocrinol. 2024;15 :1411486. doi:10.3389/fendo.2024.1411486
11. Yan H, Zhou Q, Wang Y, et al. Associations between cardiometabolic indices and the risk of diabetic kidney disease in patients with type 2 diabetes. Cardiovasc Diabetol. 2024;23 (1 ):142. doi:10.1186/s12933-024-02228-9 38664793
12. Nabipoorashrafi SA, Adeli A, Seyedi SA, et al. Comparison of insulin resistance indices in predicting albuminuria among patients with type 2 diabetes. Eur J Med Res. 2023;28 (1 ):166. doi:10.1186/s40001-023-01134-2 37161502
13. Er LK, Wu S, Chou HH, et al. Triglyceride glucose-body mass index is a simple and clinically useful surrogate marker for insulin resistance in nondiabetic individuals. PLoS One. 2016;11 (3 ):e0149731. doi:10.1371/journal.pone.0149731 26930652
14. Alqallaf A, Swan P, Docherty NG. Renal insulin resistance in type 2 diabetes mellitus and progression of chronic kidney disease: potential pathogenic mechanisms. Expert Rev Endocrinol Metab. 2022;17 (6 ):523–532. doi:10.1080/17446651.2022.2131534 36203374
15. Penno G, Solini A, Orsi E, et al. Insulin resistance, diabetic kidney disease, and all-cause mortality in individuals with type 2 diabetes: a prospective cohort study. BMC Med. 2021;19 (1 ):66. doi:10.1186/s12916-021-01936-3 33715620
16. American Diabetes A. Classification and diagnosis of diabetes: standards of medical care in diabetes-2020. Diabetes Care. 2020;43 (Suppl 1 ):S14–S31.31862745
17. Webster AC, Nagler EV, Morton RL, Masson P. Chronic kidney disease. Lancet. 2017;389 (10075 ):1238–1252. doi:10.1016/S0140-6736(16)32064-5 27887750
18. Yaribeygi H, Farrokhi FR, Butler AE, Sahebkar A. Insulin resistance: review of the underlying molecular mechanisms. J Cell Physiol. 2019;234 (6 ):8152–8161. doi:10.1002/jcp.27603 30317615
19. Whaley-Connell A, Sowers JR. Insulin resistance in kidney disease: is there a distinct role separate from that of diabetes or obesity? Cardiorenal Med. 2017;8 (1 ):41–49. doi:10.1159/000479801 29344025
20. Svensson M, Eriksson JW. Insulin resistance in diabetic nephropathy--cause or consequence? Diabetes Metab Res Rev. 2006;22 (5 ):401–410. doi:10.1002/dmrr.648 16703644
21. Li S, Cui M, Liu Y, et al. Metabolic profiles of type 2 diabetes and their association with renal complications. J Clin Endocrinol Metab. 2024;109 (4 ):1051–1059. doi:10.1210/clinem/dgad643 37933705
22. Mu X, Wu A, Hu H, Yang M, Zhou H. Correlation between alternative insulin resistance indexes and diabetic kidney disease: a retrospective study. Endocrine. 2024;84 (1 ):136–147. doi:10.1007/s12020-023-03574-6 37906402
23. Scurt FG, Ganz MJ, Herzog C, Bose K, Mertens PR, Chatzikyrkou C. Association of metabolic syndrome and chronic kidney disease. Obes Rev. 2024;25 (1 ):e13649. doi:10.1111/obr.13649 37783465
24. Mohd Nor NS, Lee S, Bacha F, Tfayli H, Arslanian S. Triglyceride glucose index as a surrogate measure of insulin sensitivity in obese adolescents with normoglycemia, prediabetes, and type 2 diabetes mellitus: comparison with the hyperinsulinemic-euglycemic clamp. Pediatr Diabetes. 2016;17 (6 ):458–465. doi:10.1111/pedi.12303 26251318
25. Guerrero-Romero F, Simental-Mendia LE, Gonzalez-Ortiz M, et al. The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp. J Clin Endocrinol Metab. 2010;95 (7 ):3347–3351. doi:10.1210/jc.2010-0288 20484475
26. Salvatori B, Linder T, Eppel D, et al. TyGIS: improved triglyceride-glucose index for the assessment of insulin sensitivity during pregnancy. Cardiovasc Diabetol. 2022;21 (1 ):215. doi:10.1186/s12933-022-01649-8 36258194
27. Huang R, Cheng Z, Jin X, et al. Usefulness of four surrogate indexes of insulin resistance in middle-aged population in Hefei, China. Ann Med. 2022;54 (1 ):622–632. doi:10.1080/07853890.2022.2039956 35175162
28. Park B, Lee HS, Lee YJ. Triglyceride glucose (TyG) index as a predictor of incident type 2 diabetes among nonobese adults: a 12-year longitudinal study of the Korean genome and epidemiology study cohort. Transl Res. 2021;228 :42–51. doi:10.1016/j.trsl.2020.08.003 32827706
29. Liu L, Xia R, Song X, et al. Association between the triglyceride-glucose index and diabetic nephropathy in patients with type 2 diabetes: a cross-sectional study. J Diabetes Investig. 2021;12 (4 ):557–565. doi:10.1111/jdi.13371
30. Gao YM, Chen WJ, Deng ZL, Shang Z, Wang Y. Association between triglyceride-glucose index and risk of end-stage renal disease in patients with type 2 diabetes mellitus and chronic kidney disease. Front Endocrinol. 2023;14 :1150980. doi:10.3389/fendo.2023.1150980
31. Taniguchi A, Fukushima M, Sakai M, et al. The role of the body mass index and triglyceride levels in identifying insulin-sensitive and insulin-resistant variants in Japanese non-insulin-dependent diabetic patients. Metabolism. 2000;49 (8 ):1001–1005. doi:10.1053/meta.2000.7735 10954017
32. Jiang Y, Lai X. Clinical features of early-onset type 2 diabetes and its association with triglyceride glucose-body mass index: a cross-sectional study. Front Endocrinol. 2024;15 :1356942. doi:10.3389/fendo.2024.1356942
33. Nosadini R. Hypertension and renal complications in type 2 diabetes. Semin Vasc Med. 2002;2 (1 ):109–119. doi:10.1055/s-2002-23101 16222601
34. Handelsman Y. Diabetes and hypertension: a comprehensive report on management and the prevention of cardiovascular and renal complications. J Clin Hypertens. 2011;13 (4 ):221–223. doi:10.1111/j.1751-7176.2011.00453.x
35. Schnell O, Barnard-Kelly K, Battelino T, et al. CVOT summit report 2023: new cardiovascular, kidney, and metabolic outcomes. Cardiovasc Diabetol. 2024;23 (1 ):104. doi:10.1186/s12933-024-02180-8 38504284
36. Laiteerapong N, Ham SA, Gao Y, et al. The legacy effect in type 2 diabetes: impact of early glycemic control on future complications (The Diabetes & Aging Study). Diabetes Care. 2019;42 (3 ):416–426. doi:10.2337/dc17-1144 30104301
37. Kim KJ, Choi J, Bae JH, et al. Time to reach target glycosylated hemoglobin is associated with long-term durable glycemic control and risk of diabetic complications in patients with newly diagnosed type 2 diabetes mellitus: a 6-year observational study. Diabetes Metab J. 2021;45 (3 ):368–378. doi:10.4093/dmj.2020.0046 33081424
38. Lin CC, Chen CC, Chen FN, et al. Risks of diabetic nephropathy with variation in hemoglobin A1c and fasting plasma glucose. Am J Med. 2013;126 (11 ):1017e1011–1010. doi:10.1016/j.amjmed.2013.04.015
