Association Between Glycated Hemoglobin Index and Triglycerides-Glucose Index in a Sample of Non-Diabetic Individuals and Its Relation With Insulin Resistance

doi:10.21203/rs.3.rs-2883442/v1

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Association Between Glycated Hemoglobin Index and Triglycerides-Glucose Index in a Sample of Non-Diabetic Individuals and Its Relation With Insulin Resistance

https://doi.org/10.21203/rs.3.rs-2883442/v1

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published 03 Jun, 2024

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Introduction

Studies have shown that elevations of the triglyceride/glucose index (TyG) as well as the glycation of hemoglobin index (HGI) are associated with several complications related to insulin resistance (IR).

Objective

To evaluate the association between HGI and TyG index in a sample of non-diabetic individuals and it’s relation with IR.

Method

A cross-sectional study was conducted with 32 non-diabetic individuals. The variables analyzed included age, gender, body mass index, and laboratory data (triglycerides, glucose, HbA1c, and insulin). We calculated the TyG index, HOMA-IR, and HGI. Simple and multivariate linear regression analyses were also performed, in addition to ANOVA and Pearson's correlation between variables.

Results

The multivariate linear regression analysis of the analyzed sample revealed a significant correlation between the TyG index and HGI, which was confirmed by the T-test. The results indicated a strong positive correlation between the TyG index and HGI, with a Pearson correlation coefficient of 0.98.

Conclusion

The HGI, TyG index and IR showed a significant association in the analyzed sample. This suggests that both indices are highly reliable in measuring IR and glucose metabolism and may be markers of risk independent of FPG, and the other variables evaluated in this study.

Endocrinology & Metabolism

Glycation hemoglobin index

Triglyceride-glucose index

Insulin resistance.

The hemoglobin A1c (HbA1c) test is used to determine a person's average blood glucose concentration over the past 2–3 months. However, research shows that the HbA1c levels in different populations cannot be fully explained by differences in blood glucose levels alone (1). Other factors, such as pH and erythrocyte turnover, can also impact HbA1c levels (2). Furthermore, some individuals and racial groups consistently have higher or lower HbA1c levels than expected based on their blood glucose levels (3).

Overall, the searches suggest that factors beyond blood glucose levels, such as inflammation and oxidative stress, can impact HbA1c levels (4). This can make it more difficult to use HbA1c as a tool for diagnosing and controlling diabetes.

The hemoglobin glycation index (HGI) applies a population-based linear regression equation to originate a forecast HbA1c as of plasma glucose values. Thus, the measured HbA1c is subtracted from the predicted HbA1c and HGI is calculated: HGI = measured HbA1c - predicted HbA1c (5).The HGI was created to calculate inter-individual divergence in HbA1c at the expense of various conditions than blood glucose level. The HGI evaluation in the Action to Control Cardiovascular Risk in Diabetes study has shown that HGI is a risk factor for cardiovascular disease and may be associated with other risk factors such as inflammation (6).

The TyG index has been established as a dependable indicator of insulin resistance (IR) alongside the hyperinsulinemic-euglycemic clamp test (7) and Homeostatic Model Assessment for IR (HOMA-IR) (8). A manuscript titled "The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with hyperinsulinemic-euglycemic clamp" was published by Guerrero Romero F, et al. in 2010, confirming the credibility of the TyG index as a marker of IR through its alignment with the hyperinsulinemic clamp and HOMA-IR (9). The TyG index was initially suggested by Simental-Mendia, et al. (10) in 2008, who had assessed a sample of healthy individuals in a cross-sectional study.

The TyG index has been utilized as an indicator of IR, a marker of atherosclerosis, and hepatic fatty infiltration in healthy individuals (11, 12).

The objective of this study was to evaluate the association between TyG index and HGI in a sample of non-diabetic individuals and it’s relation with insulin resistance.

A cross-sectional study was conducted with 32 non-diabetic individuals. The variables analyzed included age, gender, height, body mass index (BMI), abdominal waist, laboratory data (triglycerides, glucose, HbA1c, and insulin), TyG index, HOMA-IR, and HGI.

The study was conducted according to the Declaration of Helsinki, and participants signed informed consent. The CONEP approved the project (registry number: 2.464.513).

Biochemical Tests

Plasma insulin, triglyceride and fasting glucose levels were determined using VITROS® 5600 Integrated System. The HOMA-IR was evaluated using the fasting plasma glucose (FPG) level and fasting insulin level. The HbA1c was measured using turbidimetric inhibition immunoassay using a Roche Co-bas c501 automatic analyzer, according to the manufacturers’ instructions.

Calculating the HOMA-IR

HOMA-IR was calculated by ad formula: fasting insulin (microU/L) x fasting glucose (nmol/L)/22.5.

Generally, optimal insulin sensitivity is achieved when your HOMA-IR is less than 1. Levels above 1.9 indicate early IR, while levels above 2.9 indicate significant IR (7).

Calculating the TyG index

The TyG index was calculated using the lipid profile and FPG data, based on the formula TyG = Ln [Tg (mg/dL) x Glycemia (mg/dL) / 2], where Ln is the neperian logarithm (8).

Individuals with an index of 3.7 (3.5, 3.9) probably do not have IR (10).

Calculating the HGI

The data obtained from the sample were utilized to determine the linear correlation between FPG and HbA1c in the study populace. This was represented by the equation: HbA1c = (0.0183 × FPG (mg/dL)) + 4.6. Further, HGI was calculated by deducting the predicted HbA1c value from the observed HbA1c value (Fig. 1). (5).

Each individual was then assigned to low, moderate, or high HGI subgroups based on weighted HGI tertile (33.3%) cut points (low HGI < − 1.5; n = 22; moderate HGI, − 0.6–1.5; n = 6; high HGI > 0.6; n = 4).

Statistical Analysis

The data were analyzed using R 3.1.1, and normality was determined based on parameters such as mean, median, standard deviation. Simple and multivariate linear regression analysis was also performed, in addition to ANOVA and Pearson's correlation between variables.

A significance level of P < 0.05 was adopted for all variables.

Data from 72 patients were included, 48.60% female and 51.40% male; mean age of 45.31 ± 13.72 years; weight 88.52 ± 18.27 Kg, BMI 32.25 ± 5.73 Kg/m², and waist circumference 99.68 ± 14.52cm.

Correlation between TyG index and HGI

The multivariate linear regression analysis of the sample analyzed revealed a noteworthy correlation between the TyG index and HGI, which was confirmed by the T-test. The results are displayed in Table 1, providing a clear illustration of this correlation.

Table 1

Correlation between TyG index, HGI and HOMA-IR
	Coefficients	Standard error	Stat t	valor-P
HGI	1,404951392	2,365706072	0,015938825	0,005719531
TyG	0,028110626	0,516966214	0,054376138	0,007008372
HOMA	-0,029796592	0,035798059	-0,83235217	0,004120124

The results indicated a strong positive correlation between the TyG index and HGI, with a Pearson correlation coefficient of 0.98.

We used the HGI to test the hypothesis that the change in HbA1c associated with the TyG index in a non-diabetic adult population are indices are highly reliable in assessing IR and glucose metabolism and may be markers of risk independent of FPG and the other variables assessed in this study.

Since 2010 the American Diabetes Association has approved HbA1c for the diagnosis of diabetes and the detection of pre-diabetes (13). The HGI calculates fluctuation of HbA1c as a function of variables other than just the blood glucose concentration (14). Advanced glycation end products (AGEs) lead to IR, and increased HGI is associated with elevated levels of AGEs (15). A study has shown that there is less insulin sensitivity in subjects with high HGI when compared to subjects with normal or low HGI regardless of BMI, sex, or age (16). Furthermore, studies including non-diabetic individuals who had elevated HGI were at increased risk for atherosclerosis, coronary heart disease, and chronic kidney disease (17–19), and the ACCORD study (6) demonstrated that HGI could predict cardiovascular risk. Our study showed a statistically significant correlation of the association between the elevation of HGI and HOMA-IR.

The TyG index is a commonly used substitute indicator for evaluate IR being considered very reliable (20). Its usefulness as an easily measurable marker for both metabolic syndrome and the IR has been established by early research carried out by Abbasi et al. and Simental-Mendía et al. (21, 22), and its computation entails only basic laboratory parameters such as glucose and triglycerides. Moreover, as compared to surrogate markers such as HOMA-IR, the TyG index has been found to be more effective in anticipating IR, when matched with a direct measurement method like the hyperglycemic clamp method (23). However, a recent systematic review found evidence of moderate to low quality concerning the TyG marker's effectiveness as a surrogate biomarker for IR diagnosis due to the variation among studies, considering additional validation and standardized cut-off values are necessary to employ this marker in clinical practice (24). Despite the cited systematic review and in agreement with several published manuscripts, our study found a statistically significant result of the association between elevations of TyG with elevation of HOMA-IR.

Our study on the HGI, TYG index and its relation to IR showed a significant association between these three parameters in the analyzed sample. This suggests that both indices are highly reliable in measuring IR and glucose metabolism, and both can be used interchangeably in clinical practice. Furthermore, the study findings provide valuable insights into the pathophysiology of metabolic disorders such as diabetes, obesity, and cardiovascular diseases, where IR is a central feature. This study supports the notion that the HGI and TyG index are reliable indicators for IR diagnosis, and its use can facilitate the early identification of individuals at risk of developing metabolic disorders. Therefore, the findings of this study have significant implications for clinical practice, public health, and research in the field of metabolic disorders.

The HGI, TyG index and IR showed a significant association in the analyzed sample. This suggests that both indices are highly reliable in measuring IR and glucose metabolism and may be markers of risk independent of FPG, and the other variables evaluated in this study.

Declaration of conflicting interests

The authors declare no potential conflicts of interest.

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Download PDF

Journal Publication

published 03 Jun, 2024

Read the published version in Revista de Ciências Médicas e Biológicas →

Version 1

posted

You are reading this latest preprint version

Association Between Glycated Hemoglobin Index and Triglycerides-Glucose Index in a Sample of Non-Diabetic Individuals and Its Relation With Insulin Resistance

Status:

Journal Publication

Version 1

Abstract

Introduction

Objective

Method

Results

Conclusion

Figures

INTRODUCTION

METHODS

Biochemical Tests

Calculating the HOMA-IR

Calculating the TyG index

Calculating the HGI

Statistical Analysis

RESULTS

Correlation between TyG index and HGI

DISCUSSION

CONCLUSION

Declarations

References

Status:

Journal Publication

Version 1