The Triglycerides–Glucose Index Shows a Stronger Correlation with Serum Adiponectin Levels than Homeostasis Model Assessment of Insulin Resistance and Quantitative Insulin Sensitivity Check Index

Abstract

Purpose:

To evaluate the association between diverse surrogate markers of insulin resistance and adiponectin concentrations.

Methods:

Four hundred healthy participants were included. Two different cohorts were formed according to the body mass index (BMI) values. Group 1 (n = 200) consisted of individuals with normal BMI values (18.50–24.99 kg/m²), whereas in Group 2 (n = 200) there were overweight or obese individuals (BMI ≥25.00 kg/m²). Homeostasis model assessment of insulin resistance (HOMA-IR), quantitative insulin sensitivity check index (QUICKI), and triglycerides–glucose index (TyG) were calculated. Serum adiponectin levels were measured by ELISA. A correlation analysis was performed to assess the association between serum adiponectin and HOMA-IR, QUICKI, and TyG.

Results:

Participants in Group 2 were older (age in years: Group 1, 33.3 ± 6.8 vs. Group 2, 36.4 ± 7.0, P < 0.001). There was no gender difference between groups. Overweight or obese participants had higher BMI, waist circumference, fat mass, fat ratio, fasting plasma glucose, fasting plasma insulin, triglycerides, total cholesterol, and low-density lipoprotein cholesterol values, whereas high-density lipoprotein cholesterol was higher in participants with normal BMI measures. Overweight or obese subjects were more insulin resistant (higher TyG index and HOMA-IR) and less insulin sensitive (lower QUICKI), P < 0.001 for all. Serum adiponectin levels were lower in Group 2 (serum adiponectin in ng/mL: Group 1, 11,880 ± 6838 vs. Group 2, 9115 ± 5766, P < 0.001). The correlation between TyG index and adiponectin was stronger than the correlation between QUICKI and adiponectin, and HOMA-IR and adiponectin (r for TyG and adiponectin −0.408, r for QUICKI and adiponectin 0.394, r for HOMA-IR and adiponectin −0.268, respectively, P < 0.001 for all correlations).

Conclusions:

TyG has a stronger association with adiponectin than HOMA-IR and QUICKI.

Introduction

Insulin resistance (IR) is characterized by a defect in insulin action on target tissues and it plays a major pathophysiological role in a constellation of human disorders associated with major public health problems, including type 2 diabetes mellitus, obesity, hypertension, coronary artery disease, and dyslipidemias.^1,2 Developing practical tools for measuring IR that can be used to evaluate the frequency and pathophysiological mechanisms of IR in large populations is substantial since such an indicator would help to (i) determine patients who require interventions, (ii) assist treatment decisions and assess outcomes of treatments, and (iii) understand the clinical course of patients with IR.

Thus, the development of practical and effective tools to quantify IR has been the main focus of several research. Although it has been the gold standard, the hyperinsulinemic–euglycemic clamp test is a labor-intensive, time-consuming, operator-dependent, expertise-requiring, and expensive test.³ These factors make its use limited to researches, including small number of subjects and it is not convenient for clinical and epidemiological studies. Therefore, surrogate markers that have a strong correlation with clamp studies and can be used in large populations (because they are easy to obtain) have been proposed to assess IR.⁴ The homeostasis model assessment of insulin resistance (HOMA-IR) index and the quantitative insulin sensitivity check index (QUICKI) are the two most frequently employed single-sample indicators of IR and they have been shown to correlate well with glucose clamp studies.^5,6

Both methods require only measuring plasma glucose and plasma insulin concentrations in a morning fasting sample. However, the quantification of serum insulin levels is not standardized yet and serum insulin measurements have a high biological variability; for instance, insulin values may vary by about 20% in the same individual. In addition, serum insulin measurement is expensive and not available in most laboratories.⁷

To address these limitations, in 2008, a new indicator has been proposed by Simental-Mendia et al., which does not require insulin measurement, and is based on a single assessment of fasting serum triglycerides and glucose levels that are widely available in most countries and laboratories. The product of fasting glucose and triglycerides, namely triglycerides–glucose index (TyG), has been shown to have a good correlation with total glucose metabolism rates and has high sensitivity and specificity to diagnose IR.⁸ Previous studies have demonstrated that the TyG index is a useful marker to predict IR-related health outcomes such as diabetes mellitus, hypertension, and cardiovascular disease.^9,10 However, currently, no evidence exists regarding the correlation between various surrogate markers of IR and adipokines. In this study, our objective was to evaluate and compare the power of the TyG index, HOMA-IR, and QUICKI for estimating adiponectin alterations in normal-weight and overweight–obese individuals.

Materials and Methods

Study design and study population

The study was designed as cross-sectional research and conducted at the Division of Endocrinology and Metabolism outpatient clinic, Department of Internal Medicine of Hacettepe University Hospital, Ankara, Turkey. Individuals who visited the outpatient endocrinology clinic, were 18 to 50 years of age, had a body mass index (BMI) value higher than 18.5 kg/m² and had a consent to participate in the study were enrolled. Participants who had the following conditions were excluded: (i) having specific health problems, including diabetes mellitus, liver and kidney diseases, mental and psychological disorders, history of cancer, and serious endocrine disorders (hypothyroidism, hyperthyroidism or hypopituitarism); (ii) history of bariatric surgery; (iii) being pregnant or lactating; (iv) using drugs that affect body weight, serum glucose, and insulin concentrations.

Two different cohorts were formed according to the participants' BMI values. Group 1 (n = 200) consisted of individuals with normal BMI values (18.50–24.99 kg/m²) whereas in Group 2 (n = 200) there were overweight or obese individuals (BMI ≥25.00 kg/m²).

The study was approved by the Ethics Committee of Hacettepe University with the project number GO 15/612-11, and all the participants gave their informed consent.

Study variables

The following information was recorded for each individual: age and gender, BMI, waist circumference, fat mass, fat ratio, fasting plasma glucose level, fasting plasma insulin level, fasting lipid profile, including triglyceride, total cholesterol, low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) measures, serum adiponectin level, HOMA-IR, QUICKI, and TyG index values.

Body weight and height were measured by standard methods using a calibrated digital scale (Seca 220 Scale, Germany). BMI was calculated using the formula weight (kg) divided by height squared (m²). Waist circumference (cm) was measured midpoint between the top of the iliac crest and the lower margin of the last palpable rib in the midaxillary line at the end of expiration. Bioelectrical impedance was employed for body composition analysis (TANITA BC418-MA^®, Japan).

Serum lipid and glucose levels were assessed by AU5800 Clinical Chemistry Analyzer (Beckman Coulter^®, USA) with special kits and reagents employed for each biochemical parameter. Serum insulin levels were analyzed by UniCel DxI 800 Access Immunoassay System according to the manufacturer's instructions by using dedicated kits and reagents (Beckman Coulter). Determination of serum adiponectin levels were performed by ELISA kits (Ebioscience, Austria). HOMA-IR, QUICKI, and TyG index were calculated according to the following formulas:

Statistical analyses

The continuous variables were investigated using visual (histograms, probability plots) and analytical methods (Kolmogorov–Smirnov/Shapiro–Wilk's test) to determine whether or not they are normally distributed. Descriptive analyses were presented using frequencies, median–interquartile range (IQR), or mean–standard deviation, where appropriate. The chi-square or Fisher's exact test, Mann–Whitney U test, or Student's t-test were used to compare groups. While investigating the associations between variables, the correlation coefficients and their significance were calculated using the Pearson's test. A P value ≤0.05 was accepted as statistically significant. All analyses were performed with Statistical Package for Social Sciences version 21.0.

Results

Demographic, clinical, and biochemical characteristics of the participants are summarized in Table 1, highlighting that, although both groups comprised middle-aged individuals, overweight or obese participants were older (age in years: Group 1, 33.3 ± 6.8 vs. Group 2, 36.4 ± 7.0, P < 0.001). Gender distribution was similar [F-M, n (%): Group 1, 100–100 (50–50) vs. Group 2, 92–108 (46–54), P = 0.48].

Table 1.

Demographic, Clinical, and Biochemical Characteristics of the Participants

	All cohort (n = 400)	Normal weight (n = 200)	Overweight or obese (n = 200)	P value ^a
Age (years)	35.0 ± 7.0	33.3 ± 6.8	36.4 ± 7.0	<0.001
Gender F-M n (%)	192–208 (48–52)	100–100 (50–50)	92–108 (46–54)	0.48
BMI (kg/m²)	25.8 ± 4.2	22.6 ± 1.8	29.0 ± 3.4	<0.001
Waist circumference (cm)	88.1 ± 11.6	80.7 ± 8.0	95.5 ± 9.8	<0.001
Fat mass (kg)	19.4 ± 7.5	14.5 ± 4.4	24.4 ± 6.7	<0.001
Fat ratio (%)	26.0 ± 7.6	22.5 ± 6.9	29.5 ± 6.6	<0.001
Fasting plasma glucose (mg/dL)	88.5 ± 11.2	86.0 ± 8.1	90.9 ± 12.9	<0.001
Insulin (mIU/mL)	7.8 ± 5.4	6.1 ± 4.3	9.6 ± 5.8	<0.001
Triglyceride (mg/dL)	118.8 ± 72.8	102.2 ± 68.0	135.5 ± 73.8	<0.001
Total cholesterol (mg/dL)	189.4 ± 38.8	185.0 ± 35.4	193.8 ± 41.6	0.02
HDL-C (mg/dL)	48.7 ± 11.5	51.4 ± 11.8	46.0 ± 10.6	<0.001
LDL-C (mg/dL)	123.2 ± 31.0	118.6 ± 27.4	127.7 ± 31.9	0.002
TyG	8.41 ± 0.56	8.25 ± 0.51	8.58 ± 0.57	<0.001
QUICKI	0.36 ± 0.03	0.38 ± 0.03	0.35 ± 0.03	<0.001
HOMA-IR	1.77 ± 1.57	1.33 ± 1.23	2.21 ± 1.75	<0.001
Adiponectin (ng/mL)	10,497 ± 6467	11,880 ± 6838	9115 ± 5766	<0.001

Significant P values are shown in bold.

Comparison of normal weight and overweight–obese individuals.

BMI, body mass index; HDL-C, high-density lipoprotein cholesterol; HOMA-IR, homeostatic model assessment of insulin resistance; LDL-C, low-density lipoprotein cholesterol; QUICKI, quantitative insulin sensitivity check index; TyG, triglycerides–glucose index.

Overweight–obese individuals had higher BMI, waist circumference, fat mass, fat ratio, fasting plasma glucose, fasting plasma insulin, triglycerides, total cholesterol, and LDL-C values, whereas HDL-C was higher in participants with normal BMI measures. Overweight or obese subjects were more insulin resistant (TyG index: Group 1, 8.25 ± 0.51 vs. Group 2, 8.58 ± 0.57, and HOMA-IR: Group 1, 1.33 ± 1.23 vs. Group 2, 2.21 ± 1.75; P < 0.001 for both) and less insulin sensitive (QUICKI: Group 1, 0.38 ± 0.03 vs. Group 2, 0.35 ± 0.03, P < 0.001). Subjects with high BMI displayed an adverse adipokine profile that serum adiponectin levels were lower in overweight–obese individuals (serum adiponectin in ng/mL: Group 1, 11,880 ± 6838 vs. Group 2, 9115 ± 5766, P < 0.001).

As shown in Table 2, the Pearson's correlation between TyG index and adiponectin was stronger than the correlation between QUICKI and adiponectin, and HOMA-IR and adiponectin in all cohort, in subjects with normal BMI and in overweight–obese individuals (r for TyG and adiponectin: −0.408, −0.370, −0.377; r for QUICKI and adiponectin: 0.394, 0.317, 0.370; r for HOMA-IR and adiponectin: −0.268, −0.194, −0.259, in all cohort, in Group 1 and Group 2, respectively; P < 0.001 for all correlations).

Table 2.

r Correlation Values Between Adiponectin and Surrogate Insulin Resistance Markers

	Adiponectin
	All cohort (n = 400)	Normal weight (n = 200)	Overweight or obese (n = 200)
TyG	−0.408	−0.370	−0.377
QUICKI	0.394	0.317	0.370
HOMA-IR	−0.268	−0.194	−0.259

P-value was <0.001 for all correlations.

Discussion

Our study indicates that an easy-to-obtain surrogate marker of IR, the TyG index, demonstrated a stronger correlation with serum adiponectin than QUICKI and HOMA-IR.

Current studies have revealed that adipose tissue is not only involved in fat storage however, several bioactive mediators released by adipocytes, namely adipokines, play particular roles in the regulation of complex metabolic processes.¹¹

Adiponectin, one of the most abundant adipokines in circulation, is primarily derived from adipocytes and the expression is low in other peripheral tissues.¹² This adipokine exerts its effect through adiponectin receptors 1 (AdipoR1) and 2 (AdipoR2) which are highly expressed in skeletal muscle and liver.¹³ The physiological actions of adiponectin have significant impacts on health and disease, and several studies have established the beneficial effects of adiponectin on glucose metabolism. Through the binding to AdipoR1 and AdipoR2, adiponectin activates the AMP kinase pathway and the peroxisome proliferator-activated receptor alpha pathway in the liver which augment insulin action in liver. Moreover, adiponectin prompts AMP kinase in skeletal muscle and stimulates fatty acid oxidation, which further promotes insulin sensitivity in peripheral tissues.¹⁴ In addition, increased glucose transport and GLUT4 translocation in skeletal muscle contribute to the insulin-sensitizing actions of adiponectin.¹⁵

Therefore, adiponectin is an important mediator against IR. Accordingly, multiple studies have shown a strong inverse association between serum adiponectin concentrations and the presence of metabolic syndrome.^16,17 Also, decreased adiponectin serum levels have been observed in patients with obesity, IR, type 2 diabetes mellitus, and cardiovascular disease,¹⁸ and higher serum adiponectin levels have been associated with decreased type 2 diabetes mellitus risk.¹⁹ In our study, we have shown that as one of the surrogate markers of IR, the TyG index displayed a stronger correlation with adiponectin, which plays a crucial and causal role in IR and metabolic syndrome and whose varying levels predict health outcomes associated with IR. Thus, based on our data, we propose that the TyG index better predicts adipokine alterations in both normal-weight and overweight–obese individuals.

Since its first description, the TyG index has been recognized as a convenient alternative biomarker of IR. A considerable number of studies have provided evidence that the TyG index is associated with the grade of IR and IR-related health outcomes. In the first study, which was a large cross-sectional study, including apparently healthy subjects, the TyG index was shown to be a better surrogate of IR than the HOMA-IR.⁸ By comparing the gold standard method, euglycemic–hyperinsulinemic clamp test, Guerrero-Romero et al. have demonstrated that the TyG index has high sensitivity (96.5%) and specificity (85.0%) to identify IR and they have proposed TyG index as a useful marker to identify subjects with decreased insulin sensitivity.²⁰ Furthermore, in a large study enrolling a total of 5.354 middle-aged nondiabetic Koreans with a median follow-up period of 4.6 years, the TyG index was established as a better predictor of the future diabetes risk than HOMA-IR and when compared with the lowest quartile group, the relative risk of diabetes was nearly fourfold higher in the highest quartile.²¹

Moreover, in a cohort in which 5014 patients were followed with a median period of 10 years, researchers specifically evaluated the association between the TyG index and cardiovascular events and higher levels of TyG index have been found to be significantly associated with cardiovascular events, including coronary heart disease, cerebrovascular disease, and peripheral arterial disease.²² Therefore, a considerable number of studies have verified that the TyG index is a practical and reliable surrogate for IR, which can be used to assess IR and predict IR-related health outcomes; and TyG is a better marker for estimating IR than the HOMA-IR and QUICKI. In our study, we evaluated the power of various surrogate IR markers from a different perspective based on adiponectin alterations and we found that the TyG index had a stronger association with adiponectin than HOMA-IR and QUICKI.

The strengths of our study lie in its inclusion of a large number of normal- and overweight participants who were assessed with standardized high-quality clinical characteristics and laboratory measurements, and the exclusion of potential confounders. There were particular limitations of our study. Although adiponectin is the most abundant adipokine in circulation, many other adipokines involve in the regulation of metabolism. Unfortunately, we were not able to assess the association between the TyG index and other adipokines, such as leptin, resistin, and others.¹¹ Confirming a stronger association also between the TyG index and other adipokines would be valuable. In addition, the cross-sectional design of our study limits making further comments regarding the long-term health outcomes. The study population included only Turkish individuals, which may restrict the generalizability of the results. Despite these limitations, our study is the first study that demonstrates the association between the TyG index and adiponectin.

Conclusions

The TyG index is a practical and convenient surrogate marker of IR and can be used to evaluate IR in large populations. It is a good predictor to assess the future risk of diabetes, cardiovascular event, cardiovascular mortality, and all-cause mortality, and it has a better association with long-term consequences of IR than HOMA-IR and QUICKI. Finally, it has a stronger relationship with adiponectin alterations than HOMA-IR and QUICKI. Displaying a better association with adiponectin concentrations supports the idea that TyG is a better predictor of IR than more conventional measures of IR.

Footnotes

Authors' Contributions

S.N.S.: Conceptualization, methodology, formal analysis, investigation, and writing—original draft. K.I.A.: Investigation and resources. B.T.D.: Investigation and resources. I.L.: Resources. Z.B.: Methodology, investigation, resources, and writing—review and editing. T.E.: Conceptualization, methodology, writing—review and editing, visualization, and supervision.

Author Disclosure Statement

The authors declare no conflicts of interest.

Funding Information

This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant 216S272.

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