TyG Index Performs Better Than HOMA-IR in Chinese Type 2 Diabetes Mellitus with a BMI < 35 kg/m2: A Hyperglycemic Clamp Validated Study
by
, and
Ping Luo
†
,
Yaoquan Cao
†,
Pengzhou Li
,
Weizheng Li
,
Zhi Song
,
Zhibing Fu
,
Hui Zhou
,
Xianhao Yi
,
Liyong Zhu
* and
Shaihong Zhu
* Department of General Surgery, Third Xiangya Hospital, Central South University, Changsha 410013, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Medicina 2022, 58(7), 876; https://doi.org/10.3390/medicina58070876
Submission received: 16 April 2022
/
Revised: 30 May 2022
/
Accepted: 25 June 2022
/
Published: 30 June 2022
(This article belongs to the Special Issue Advance in Type 2 Diabetes and Insulin Resistance)
Abstract
:Background and objectives: Chinese type 2 diabetes mellitus (T2DM) patients are characterized by a low body mass index (BMI), and significant insulin resistance (IR). The triglyceride glucose (TyG) index has not been studied as a means of assessing IR in Chinese T2DM patients with a BMI < 35 kg/m2. Materials and Methods: An open-label cross-sectional study recruited 102 Chinese T2DM patients with a BMI < 35 kg/m2. The hyper-insulinemic euglycemic clamp, homeostatic model assessment of IR (HOMA-IR), and TyG index were used to determine the level of IR. Based on Pearson’s correlations, glucose disposal rate (GDR), TyG index, and HOMA-IR were analyzed. HOMA-IR and TyG index for IR were evaluated using multiple linear regression and multivariate logistic regression analyses. On the basis of the receiver operating characteristic (ROC) curve, the sensitivity, specificity, and optimal cut-off value of HOMA-IR and the TyG index were determined. Results: The mean values of GDR, HOMA-IR, and TyG index were 4.25 ± 1.81, 8.05 ± 7.98, and 8.12 ± 0.86 mg/kg/min, respectively. Pearson’s correlation coefficient was −0.418 between GDR and TyG index and −0.324 between GDR and HOMA-IR. ROC curve analysis showed that, among both sexes, the TyG index was a better discriminator of IR than HOMA-IR. The area under the ROC curve (AUC) of the TyG index (0.785, 0.691–0.879) was higher than that of HOMA-IR (0.73, 0.588–0.873) in all genders. The optimal cut-off values of the TyG index and HOMA-IR were 7.99 and 3.39, respectively. Conclusions: The TyG index showed more effectiveness in identifying IR in Chinese T2DM patients with a BMI < 35 kg/m2 compared to HOMA-IR.
1. Introduction
The incidence of T2DM has increased dramatically across the globe [1]. Diabetes and related comorbidities were the leading cause of death in China in 2019 [2]. Chinese T2DM patients are characterized by a relatively young age, a low BMI, and significant IR [3,4]. It is well documented that the two major pathological features of T2DM are IR and β-cell dysfunction [5,6]. The incidence of T2DM is related more to IR than to β-cell dysfunction in Chinese adults [7]. Therefore, it is very important to find a simple and reliable index to assess the IR of patients with T2DM.
HOMA-IR, based on fasting glucose and insulin, is the most popular index for evaluating IR in diabetes populations [8]. Unfortunately, HOMA-IR is the least accurate and varies partly due to the lack of standardization of insulin immunoassays [9]. Kang et al. reported that HOMA-IR had limitations to evaluate IR in low BMI T2DM patients with β-cell malfunction and insulin secretory defects [10]. A hyper-insulinemic euglycemic clamp is considered the gold standard to assess IR, but its disadvantages are obvious and fatal because it is laborious, complex, and expensive [11]. The TyG index, an emerging and reliable indicator, has been used to identify IR in different populations and diseases [12,13,14].
To the best of our knowledge, the value of the TyG index needed to assess IR in Chinese T2DM patients with a low BMI has not been studied. Therefore, we prospectively evaluated the potential of using the TyG index to assess IR in Chinese patients with T2DM and a BMI < 35 kg/m2.
2. Materials and Methods
2.1. Study Population
A cross-sectional study was conducted in the Department of General Surgery, Third Xiangya Hospital, Central South University (Changsha, China). One hundred and two T2DM patients with a BMI < 35 kg/m2 were prospectively consecutively recruited. The detailed steps for the search are presented in Figure 1. With the approval of the protocol by the Ethical Committee of our hospital (R19025), informed consent was signed before the study.
The inclusion criteria were as follows: (1) Conforming to the criteria of American Diabetes Association (2014) [15]; (2) a BMI < 35 kg/m2; (3) aged 18–65 years; (4) glycosylated hemoglobin A1c (HbA1c) level between 7% and 11%. The exclusion criteria were: (1) Unable to complete blood sampling or anthropometric measurements; (2) usage of insulin and medication that affects lipid metabolism; (3) the values of fasting glucose and triglyceride are more than three standard deviations from the mean; (4) severe organic diseases, such as myocardial infarction, renal failure, or stroke; (5) alcohol or medicine addiction; and (6) uncontrolled psychiatric disease.
2.2. Study Protocol
All participants were required to maintain their usual diet containing at least 250 g of carbohydrates and avoid strenuous exercise at least 3 days before the procedure. Medical treatment of T2DM including sulfonylureas and/or biguanide was required to continue as usual before the study.
2.3. Assays
Anthropometric parameters and fasting blood profile were performed after at least 8 h of overnight fast. Plasma glucose was determined using the glucose oxidase method (Beckman Coulter Inc, Brea, CA, America). Triglycerides were enzymatically analyzed by using the spectrophotometric method (Beckman Coulter Inc, Brea, CA, America). Plasma insulin was detected by radioimmunoassay (Roche, Basel, Switzerland). HbA1c was analyzed by using a high-performance liquid chromatograph (Hitachi, Tokyo, Japan). The level of IR was evaluated by a hyper-insulinemic euglycemic clamp, HOMA-IR, and the TyG index. The TyG index was calculated as ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2] [16]. HOMA-IR was calculated as fasting glucose (mmol/L) × fasting insulin (mU/mL)/22.5 [8].
The operation procedure of the hyper-insulinemic euglycemic clamp was performed as in our previous study [17]. A clamping test was performed after a 12 h overnight fast. Catheters were inserted into the antecubital and dorsal vein for infusion and blood sampling, respectively. Insulin (Humulin R, Eli Lilly, Indianapolis, IN, USA) was infused at a constant rate of 40 mU/kg/min for 150 min. Serum glucose level was measured every 5 min with a glucose analyzer. Dextrose 20% was administered intravenously at variable rates to maintain a steady glucose level of 5.0 mmol/L. Glucose disposal rate (GDR) was calculated at steady-state intervals.
2.4. Statistical Analysis
The data analysis was performed using SPSS software version 26 (SPSS Inc. Chicago, USA). Continuous variables are expressed as mean ± standard deviation. Categorical variables are expressed as frequencies and percentages. Pearson’s correlations between GDR, TyG index, and HOMA-IR were analyzed. The sensitivity, specificity, and the optimal cut-off value of the TyG index and HOMR-IR to evaluate IR were calculated using an ROC curve. The AUC, as a criterion of diagnostic accuracy, was evaluated. After GDR was log-transformed (natural logarithm) to approximate normal distributions, linear regression analyses with log GDR as the dependent variable were conducted to assess associations between TyG as well as HOMA-IR with GDR. The predicted value for each index was calculated as the R2 of the entire regression model minus the R2 of the underlying model excluding each index. The GDR was divided based on the quintile into grades I–III before the multivariate logistic regression analysis was performed to calculate the odds ratio and 95% confidence interval (CI) for IR. Regression analysis took gender specificity into account. Statistical significance was defined as p values < 0.05.
3. Results
A total of 102 T2DM patients with a BMI < 35 kg/m2 were enrolled. According to the exclusion criteria (3), four patients were excluded. The average age and BMI were 42.34 ± 12.86 years and 29.20 ± 3.52 kg/m2, respectively. The mean duration of T2DM was 5.84 ± 4.33 years. Sixty-six subjects were treated with metformin to control blood glucose. The mean duration of metformin was 4.16 ± 4.57 years. Totals of 53.1 and 50 percent of patients had hypertension and nonalcoholic fatty liver disease, respectively.
The average fasting glucose and triglyceride were 167.09 ± 63.39 mg/dL and 56.29 ± 53.71 mg/dL, respectively. The mean values of GDR, HOMA-IR, and TyG index were 4.25 ± 1.81, 8.05 ± 7.98, and 8.12 ± 0.86 mg/kg/min, respectively. The clinical characteristics of subjects are shown in Table 1. All clinical characteristics did not show significant differences between both sexes.
All of the following analyses were stratified by sex because of significant differences in lipid profiles between men and women [18]. As shown in Table 2 and Figure 2, the Pearson’s correlations coefficient between GDR and the TyG index was −0.418 (−0.412 in men and −0.431 in women). On the other hand, the correlation between GDR and HOMA-IR was −0.324 (−0.291 in men and −0.364 in women). Compared with HOMA-IR, the TyG index showed a better performance in identifying diabetes patients with IR in both sexes.
As shown in Table 3, both the TyG index and HOMA-IR made a significant incremental additive contribution to the prediction of IR in both sexes. However, the additional percentage of variation in IR explained by the TyG index was stronger than by HOMA-IR in men (0.134 versus 0.044) and women (0.81 versus 0.003). The direct comparative ORs and 95% CIs for the TyG index and HOMA-IR are presented in Table 4. Compared with HOMA-IR (1.08 in men and 1.11 in women), among both sexes, the OR for IR was highest in the TyG index (2.22 in men and 3.07 in women).
As shown in Table 5 and Figure 3, the AUC of the TyG index (0.785, 0.691–0.879) was higher than that of HOMA-IR (0.73, 0.588–0.873) among both sexes, as well as in men (0.774 versus 0.709) and women (0.794 versus 0.769). The AUC analysis indicated that the TyG index was a better surrogate maker to evaluate IR than HOMA-IR. The optimal cut-off values of the TyG index and HOMA-IR were 7.99 and 3.39 with a sensitivity of 59.5% and 76.2% and a specificity of 100% and 64.3% respectively.
4. Discussion
T2DM is characterized by impaired glucose and lipid metabolism associated with IR [6,19]. There is strong evidence that IR is closely linked to the pathophysiology of T2DM [20,21]. It is vital to identify IR with a reliable and simple indicator.
HOMA-IR, the TyG index, and a hyper-insulinemic euglycemic clamp were used in our study to investigate IR in T2DM patients with a BMI < 35 kg/m2. Our results indicate that the TyG index can be used to identify IR among Chinese adults with T2DM who have a low BMI, as it closely matches the hyper-insulinemic euglycemic clamp. HOMA-IR was a validated and widely used method for identifying IR, but laboratory measurements of plasma insulin have not been standardized [22]. Moreover, a study has shown that the majority of commercial assays lack precision and cross-reactivity [9]. According to Muniyappa et al. [23], HOMA-IR has no linear correlation with the clamp test. Similar results were found in a Korean population with T2DM and a lower BMI [10]. HOMA-IR and GDR showed a correlation based on our results, but it was lower than the correlation for the TyG index in both sexes. Meanwhile, multiple linear regression showed that the additional percentage of variation in IR explained by the TyG index is stronger than by HOMA-IR in all subjects. In addition, it indicated that the TyG index may be a better maker to assess IR than HOMA-IR.
This simple lipid-glucose index, based on fasting triglyceride and glucose levels, is affordable and readily available in most clinical laboratories [13,24]. The TyG index was demonstrated to be a simple and reliable measure of IR by Guerrero-Romero et al. [12]. For assessing IR in the Brazilian population, Vasques et al. showed that the TyG index outperformed HOMA-IR [11]. In our study, Pearson’s correlation coefficient between the TyG index and GDR (−0.418) was lower than that of HOMA-IR (−0.324) in all genders. Multivariate logistic regression showed that the value of OR in the TyG index was higher than that of HOMA-IR. In ROC analysis, the AUC of the TyG index (0.785, 0.691–0.879) was higher than that of HOMA-IR (0.73, 0.588–0.873), supporting that the TyG index was a better indicator than HOMA-IR in identifying IR in Chinese T2DM patients with a BMI < 35 kg/m2.
The main advantage of this study was comparing the gold standard clamp test with the TyG index and HOMA-IR. Using the TyG index, we also calculated the optimal cut-off value for IR identification in our study population. The present study is without a doubt limited. To begin with, the sample size was small. Additionally, we did not take into account the variability in fasting triglyceride levels when calculating the TyG index.
5. Conclusions
The TyG index can be regarded as an accurate and reliable indicator of IR and outperformed HOMA-IR in Chinese T2DM patients with a BMI < 35 kg/m2.
Author Contributions
Study design and writing, P.L. (Ping Luo) and Y.C.; Surgery, P.L. (Pengzhou Li), Z.S. and W.L.; Data collection and analysis, H.Z., X.Y. and Z.F.; Guide, L.Z. and S.Z. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the Wisdom Accumulation and Talent Cultivation Project of the Third Xiangya Hospital of Central South University, grant number YX202102.
Institutional Review Board Statement
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Third Xiangya Hospital, Central South University (protocol code R19025 and date of approval 19 June 2019).
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patient(s) to publish this paper.
Data Availability Statement
The data that support the findings of this study are available from Third Xiangya Hospital, Central South University, but restricted for research use only. The data are not publicly available. Data are available from the authors upon reasonable request and with permission of Third Xiangya Hospital, Central South University.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Chen, L.; Magliano, D.J.; Zimmet, P.Z. The worldwide epidemiology of type 2 diabetes mellitus--present and future perspectives. Nat. Rev. Endocrinol. 2011, 8, 228–236. [Google Scholar] [CrossRef]
- Saeedi, P.; Petersohn, I.; Salpea, P.; Malanda, B.; Karuranga, S.; Unwin, N.; Colagiuri, S.; Guariguata, L.; Motala, A.A.; Ogurtsova, K.; et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9(th) edition. Diabetes Res. Clin. Pract. 2019, 157, 107843. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hu, C.; Jia, W. Diabetes in China: Epidemiology and Genetic Risk Factors and Their Clinical Utility in Personalized Medication. Diabetes 2018, 67, 3–11. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gao, L.; Zhang, P.; Weng, J.; Lu, J.; Guo, X.; Jia, W.; Yang, W.; Zou, D.; Zhou, Z.; Pan, C.; et al. Patient characteristics and 6-month dose of basal insulin associated with HbA1c achievement <7.0% in Chinese people with type 2 diabetes: Results from the Observational Registry of Basal Insulin Treatment (ORBIT). J. Diabetes 2020, 12, 668–676. [Google Scholar] [CrossRef] [PubMed]
- Porte, D., Jr.; Kahn, S.E. beta-cell dysfunction and failure in type 2 diabetes: Potential mechanisms. Diabetes 2001, 50 (Suppl S1), S160–S163. [Google Scholar] [CrossRef] [Green Version]
- Galicia-Garcia, U.; Benito-Vicente, A.; Jebari, S.; Larrea-Sebal, A.; Siddiqi, H.; Uribe, K.B.; Ostolaza, H.; Martín, C. Pathophysiology of Type 2 Diabetes Mellitus. Int. J. Mol. Sci. 2020, 21, 6275. [Google Scholar] [CrossRef]
- Yabe, D.; Seino, Y.; Fukushima, M.; Seino, S. β cell dysfunction versus insulin resistance in the pathogenesis of type 2 diabetes in East Asians. Curr. Diab. Rep. 2015, 15, 602. [Google Scholar] [CrossRef] [Green Version]
- Matthews, D.R.; Hosker, J.P.; Rudenski, A.S.; Naylor, B.A.; Treacher, D.F.; Turner, R.C. Homeostasis model assessment: Insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985, 28, 412–419. [Google Scholar] [CrossRef] [Green Version]
- Marcovina, S.; Bowsher, R.R.; Miller, W.G.; Staten, M.; Myers, G.; Caudill, S.P.; Campbell, S.E.; Steffes, M.W. Standardization of insulin immunoassays: Report of the American Diabetes Association Workgroup. Clin. Chem. 2007, 53, 711–716. [Google Scholar] [CrossRef]
- Kang, E.S.; Yun, Y.S.; Park, S.W.; Kim, H.J.; Ahn, C.W.; Song, Y.D.; Cha, B.S.; Lim, S.K.; Kim, K.R.; Lee, H.C. Limitation of the validity of the homeostasis model assessment as an index of insulin resistance in Korea. Metabolism 2005, 54, 206–211. [Google Scholar] [CrossRef]
- Bonora, E.; Targher, G.; Alberiche, M.; Bonadonna, R.C.; Saggiani, F.; Zenere, M.B.; Monauni, T.; Muggeo, M. Homeostasis model assessment closely mirrors the glucose clamp technique in the assessment of insulin sensitivity: Studies in subjects with various degrees of glucose tolerance and insulin sensitivity. Diabetes Care 2000, 23, 57–63. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Guerrero-Romero, F.; Simental-Mendía, L.E.; González-Ortiz, M.; Martínez-Abundis, E.; Ramos-Zavala, M.G.; Hernández-González, S.O.; Jacques-Camarena, O.; Rodríguez-Morán, M. The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp. J. Clin. Endocrinol. Metab. 2010, 95, 3347–3351. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhang, M.; Wang, B.; Liu, Y.; Sun, X.; Luo, X.; Wang, C.; Li, L.; Zhang, L.; Ren, Y.; Zhao, Y.; et al. Cumulative increased risk of incident type 2 diabetes mellitus with increasing triglyceride glucose index in normal-weight people: The Rural Chinese Cohort Study. Cardiovasc. Diabetol. 2017, 16, 30. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Thai, P.V.; Tien, H.A.; Van Minh, H.; Valensi, P. Triglyceride glucose index for the detection of asymptomatic coronary artery stenosis in patients with type 2 diabetes. Cardiovasc. Diabetol. 2020, 19, 137. [Google Scholar] [CrossRef] [PubMed]
- American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care 2014, 37 (Suppl S1), S81–S90. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Park, B.; Lee, H.S.; Lee, Y.J. 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. [Google Scholar] [CrossRef]
- Luo, P.; Cao, Y.; Li, P.; Wang, G.; Song, Z.; Li, W.; Su, Z.; Zhou, H.; Yi, X.; Fu, Z.; et al. Insulin Resistance Remission Following Laparoscopic Roux-en-Y Gastric Bypass and Laparoscopic Sleeve Gastrectomy in Chinese Type 2 Diabetes Mellitus Patients With a Body Mass Index of 27.5–32.5 kg/m2. Front. Physiol. 2021, 12, 772577. [Google Scholar] [CrossRef]
- Du, T.; Yuan, G.; Zhang, M.; Zhou, X.; Sun, X.; Yu, X. Clinical usefulness of lipid ratios, visceral adiposity indicators, and the triglycerides and glucose index as risk markers of insulin resistance. Cardiovasc. Diabetol. 2014, 13, 146. [Google Scholar] [CrossRef]
- Kumar, A.S.; Maiya, A.G.; Shastry, B.A.; Vaishali, K.; Ravishankar, N.; Hazari, A.; Gundmi, S.; Jadhav, R. Exercise and insulin resistance in type 2 diabetes mellitus: A systematic review and meta-analysis. Ann. Phys. Rehabil. Med. 2019, 62, 98–103. [Google Scholar] [CrossRef]
- DeFronzo, R.A.; Ferrannini, E.; Groop, L.; Henry, R.R.; Herman, W.H.; Holst, J.J.; Hu, F.B.; Kahn, C.R.; Raz, I.; Shulman, G.I.; et al. Type 2 diabetes mellitus. Nat. Rev. Dis. Primers 2015, 1, 15019. [Google Scholar] [CrossRef]
- Kautzky-Willer, A.; Harreiter, J.; Pacini, G. Sex and Gender Differences in Risk, Pathophysiology and Complications of Type 2 Diabetes Mellitus. Endocr. Rev. 2016, 37, 278–316. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wallace, T.M.; Levy, J.C.; Matthews, D.R. Use and abuse of HOMA modeling. Diabetes Care 2004, 27, 1487–1495. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Muniyappa, R.; Irving, B.A.; Unni, U.S.; Briggs, W.M.; Nair, K.S.; Quon, M.J.; Kurpad, A.V. Limited predictive ability of surrogate indices of insulin sensitivity/resistance in Asian-Indian men. Am. J. Physiol. Endocrinol. Metab. 2010, 299, E1106–E1112. [Google Scholar] [CrossRef] [PubMed]
- Su, W.Y.; Chen, S.C.; Huang, Y.T.; Huang, J.C.; Wu, P.Y.; Hsu, W.H.; Lee, M.Y. Comparison of the Effects of Fasting Glucose, Hemoglobin A(1c), and Triglyceride-Glucose Index on Cardiovascular Events in Type 2 Diabetes Mellitus. Nutrients 2019, 11, 2838. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Figure 1.
Schematic of the research methods.
Figure 2.
(A): Correlations between TyG index and GDR; (B): correlations between HOMA-IR and GDR.
Figure 3.
Receiver operating characteristic (ROC) analysis for TyG index and HOMA-IR in the identification of insulin resistance.
Figure 3.
Receiver operating characteristic (ROC) analysis for TyG index and HOMA-IR in the identification of insulin resistance.
Table 1.
Clinical characteristics of patients (n = 98).
| Characteristics | Total (n = 98) | Men (n = 57) | Women (n = 41) | p Value | |||
|---|---|---|---|---|---|---|---|
| Mean ± SD | Median (Min, Max) | Mean ± SD | Median (Min, Max) | Mean ± SD | Median (Min, Max) | ||
| Age (years) | 42.34 ± 12.86 | 42 (18, 67) | 42.46 ± 12.43 | 43 (18, 64) | 42.17 ± 13.58 | 41 (18, 67) | 0.914 |
| Body mass index (kg/m2) | 29.20 ± 3.52 | 29.75 (21.88, 34.95) | 28.94 ± 3.58 | 28.72 (21.88, 34.77) | 29.56 ± 3.46 | 29.95 (22.19, 34.95) | 0.394 |
| Waist circumference (cm) | 99.92 ± 9.88 | 98 (61, 131) | 99.93 ± 10.52 | 97.5 (61, 131) | 99.92 ± 9.03 | 99 (80, 118) | 0.994 |
| Duration of T2DM (years) | 5.84 ± 4.33 | 5 (1, 15) | 6.07 ± 4.18 | 5 (1, 15) | 5.51 ± 4.55 | 4 (1, 15) | 0.532 |
| Metformin (n) | 66 (67.35%) | 37 (64.91%) | 29 (70.73%) | 0.549 | |||
| Duration of metformin (years) | 4.16 ± 4.57 | 2 (1, 15) | 4.04 ± 4.51 | 2 (1, 15) | 4.34 ± 4.69 | 2 (1, 15) | 0.745 |
| Fasting glucose (mg/dL) | 167.09 ± 63.39 | 155.88 (68.04, 392.04) | 161.22 ± 62.88 | 151.2 (68.04, 392.04) | 175.25 ± 63.97 | 165.06 (83.7, 297.72) | 0.282 |
| HbA1c (%) HbA1c (mmol/mol) | 8.50 ± 1.87 62.65 ± 17.79 | 8.3 (5.6, 16.8) 59.56 (37.71, 106.56) | 8.55 ± 1.58 68.33 ± 17.77 | 8.3 (6.2, 12.3) 66.12 (44.26, 110.93) | 8.44 ± 2.24 68.79 ± 24.48 | 8.1 (5.6, 16.8) 65.03 (37.71, 160.11) | 0.793 0.503 |
| Triglyceride (mg/dL) | 56.29 ± 53.71 | 39.78 (8.82, 313.92) | 60.42 ± 60.41 | 41.58 (8.82, 313.92) | 50.55 ± 42.78 | 39.24 (17.1, 219.78) | 0.372 |
| GDR (mg/kg/min) | 4.25 ± 1.81 | 3.88 (1.61, 8.91) | 4.34 ± 1.76 | 4.13 (1.72, 8.91) | 4.13 ± 1.89 | 3.64 (1.61, 8.58) | 0.581 |
| HOMA-IR | 8.05 ± 7.98 | 5.36 (0.59, 46.37) | 7.28 ± 8.23 | 5.11 (0.59, 46.37) | 8.96 ± 7.79 | 6.99 (1.1, 40.84) | 0.312 |
| TyG index | 8.12 ± 0.86 | 7.99 (6.38, 11.03) | 8.13 ± 0.88 | 8.01 (6.38, 11.03) | 8.11 ± 0.83 | 7.97 (6.94, 10.38) | 0.897 |
T2DM, type 2 diabetes mellitus; HbA1c, glycosylated hemoglobin A1c; GDR, glucose disposal rate; HOMA-IR, homeostatic model assessment of insulin resistance; TyG index, triglyceride glucose index.
Table 2.
Pearson’s correlations analysis between TyG index, HOMA-IR, and GDR.
| Men | Women | Total | ||||
|---|---|---|---|---|---|---|
| Pearson’s Correlations Coefficient | p Value | Pearson’s Correlations Coefficient | p Value | Pearson’s Correlations Coefficient | p Value | |
| TyG index and GDR | −0.412 | 0.001 ** | −0.431 | 0.005 ** | −0.418 | 0.0000 **** |
| HOMA-IR and GDR | −0.291 | 0.028 * | −0.364 | 0.019 * | −0.324 | 0.0011 ** |
* p < 0.05; ** p < 0.01; **** p < 0.0001.
Table 3.
Multiple linear regression model for predicting IR.
| Men | Women | |||||
|---|---|---|---|---|---|---|
| Additional R2 | β | p Value | Additional R2 | β | p Value | |
| TyG index | 0.134 | −0.78 | 0.002 ** | 0.81 | −0.69 | 0.02 * |
| HOMA-IR | 0.044 | −0.061 | 0.014 * | 0.003 | −0.034 | 0.369 |
The model was adjusted for age, duration of T2DM, metformin, BMI and waist circumference. * p < 0.05; ** p < 0.01.
Table 4.
Odds ratios of multivariate logistic regression model for predicting IR.
| Men | Women | |||
|---|---|---|---|---|
| Odds ratio (95% Confidence Interval) | p Value | Odds ratio (95% Confidence Interval) | p Value | |
| TyG index | 2.22(1.12–4.39) | 0.022 * | 3.07(1.20–7.83) | 0.019 * |
| HOMA-IR | 1.08(1.00–1.15) | 0.044 * | 1.11(0.98–1.24) | 0.092 |
The model was adjusted for age, duration of T2DM, metformin, BMI and waist circumference. * p < 0.05.
Table 5.
Receiver operating characteristic (ROC) analysis for HOMA-IR and TyG index in the identification of insulin resistance.
Table 5.
Receiver operating characteristic (ROC) analysis for HOMA-IR and TyG index in the identification of insulin resistance.
| Men | Women | Total | ||||
|---|---|---|---|---|---|---|
| AUC (95% Confidence Interval) | p Value | AUC (95% Confidence Interval) | p Value | AUC (95% Confidence Interval) | p Value | |
| TyG index | 0.774 (0.648–0.901) | 0.02 * | 0.794 (0.643–0.945) | 0.015 * | 0.785 (0.691–0.879) | 0.001 ** |
| HOMA-IR | 0.709 (0.534–0.884) | 0.076 | 0.769 (0.53–1.00) | 0.027 * | 0.73 (0.588–0.873) | 0.006 ** |
* p < 0.05; ** p < 0.01.
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Luo, P.; Cao, Y.; Li, P.; Li, W.; Song, Z.; Fu, Z.; Zhou, H.; Yi, X.; Zhu, L.; Zhu, S. TyG Index Performs Better Than HOMA-IR in Chinese Type 2 Diabetes Mellitus with a BMI < 35 kg/m2: A Hyperglycemic Clamp Validated Study. Medicina 2022, 58, 876. https://doi.org/10.3390/medicina58070876
AMA Style
Luo P, Cao Y, Li P, Li W, Song Z, Fu Z, Zhou H, Yi X, Zhu L, Zhu S. TyG Index Performs Better Than HOMA-IR in Chinese Type 2 Diabetes Mellitus with a BMI < 35 kg/m2: A Hyperglycemic Clamp Validated Study. Medicina. 2022; 58(7):876. https://doi.org/10.3390/medicina58070876
Chicago/Turabian StyleLuo, Ping, Yaoquan Cao, Pengzhou Li, Weizheng Li, Zhi Song, Zhibing Fu, Hui Zhou, Xianhao Yi, Liyong Zhu, and Shaihong Zhu. 2022. "TyG Index Performs Better Than HOMA-IR in Chinese Type 2 Diabetes Mellitus with a BMI < 35 kg/m2: A Hyperglycemic Clamp Validated Study" Medicina 58, no. 7: 876. https://doi.org/10.3390/medicina58070876
