The triglyceride-glucose index, a predictor of type 2 diabetes development: A retrospective cohort study

ARTICLE IN PRESS

PCD-821; No. of Pages 7

p r i m a r y c a r e d i a b e t e s x x x ( 2 0 1 9 ) xxx–xxx

Contents lists available at ScienceDirect

Primary Care Diabetes journal homepage: http://www.elsevier.com/locate/pcd

Original research

The triglyceride-glucose index, a predictor of type 2 diabetes development: A retrospective cohort study Panya Chamroonkiadtikun ∗ , Thareerat Ananchaisarp, Worawit Wanichanon Department of Family and Preventive Medicine, Prince of Songkla University, Hat Yai, Songkhla, 90110, Thailand

a r t i c l e

i n f o

a b s t r a c t

Article history:

Aims: The triglycerides-glucose (TyG) index, the product of fasting plasma glucose (FPG) and

Received 6 June 2019

triglycerides (TG) is a novel index. Many previous studies have reported that the TyG index

Accepted 8 August 2019

might be a strong predictor of incident type 2 diabetes. We determined whether the TyG

Available online xxx

index could be a useful predictor for diabetes diagnosis and compared it to the FPG and TG as predictors of type 2 diabetes.

Keywords:

Methods: A total of 617 subjects without baseline diabetes were examined and followed up

Type 2 diabetes mellitus

for a median period of 9.2 years. We performed a mixed effect cox regression analysis to

Triglycerides

evaluate the risk of developing diabetes across the quartiles of the TyG index, calculated as

Glucose

ln[triglyceride (mg/dl) × FPG (mg/dl)/2], and plotted a receiver operating characteristic (ROC)

Risk factor

curve to assess discrimination among TyG, FPG and TG.

Incidence

Results: During 4,871.56 person-years of follow-up, there were 163 incident cases of diabetes. The risk of diabetes increased across the quartiles of the TyG index. Those in the highest quartile of TyG had a higher risk of developing diabetes (adjusted HR 3.38 95% CI 2.38–4.8, ptrend < 0.001) than those in the lowest quartile. The area under the curve (AUC) of the ROC plots were 0.79 (95% CI 0.74–0.83) for FPG, 0.64 (95% CI 0.60–0.69) for TyG and 0.59 (95% CI 0.54–0.64) for TG. Conclusion: The TyG index was significantly associated with risk of incident diabetes and could be a valuable biomarker of developing diabetes. However, FPG appeared to be a more robust predictor of diabetes. © 2019 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.

1.

Introduction

Diabetes mellitus (DM) is a serious public health concern. Globally there is a rising trend in the prevalence and incidence of DM due to many factors, such as aging, urbanization and an increasing prevalence of obesity and physical inac-

tivity, leading to a burden on health economics due to its complications, and even reaching epidemic proportions in some countries. The International Diabetes Federation (IDF) [1] reported that the worldwide prevalence of DM was 8.8%; in the western pacific region it was 9.5%. The IDF has predicted that the prevalence of DM in the western pacific region will rise to 10.3% by 2045. Based on recent data from Thailand’s

∗

Corresponding author. E-mail addresses: [email protected] (P. Chamroonkiadtikun), [email protected] (T. Ananchaisarp), [email protected] (W. Wanichanon). https://doi.org/10.1016/j.pcd.2019.08.004 1751-9918/© 2019 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.

Please cite this article in press as: P. Chamroonkiadtikun, et al., The triglyceride-glucose index, a predictor of type 2 diabetes development: A retrospective cohort study, Prim. Care Diab. (2019), https://doi.org/10.1016/j.pcd.2019.08.004

PCD-821; No. of Pages 7

ARTICLE IN PRESS

2

p r i m a r y c a r e d i a b e t e s x x x ( 2 0 1 9 ) xxx–xxx

national health examination survey (NHES) [2], the prevalence of DM in individuals aged 15 and over increased from 6.9% in 2009 to 9.9% in 2014, and women have experienced a higher prevalence than men. The available reports of adult type 2 diabetes incidence in Thailand were 13.6–17.8 per 1000 personyears men and 6.4–9.2 per 1000 person-years women [3,4]. To prevent and overcome the burden of diabetes, identification of individuals at a high risk of developing diabetes is one of the effective interventions. Thus, a simple, effective, reliable and widely applicable tool needs to be developed. In the past few decades, various risk factors, predictive models, and inflammatory biomarkers have been suggested for identifying persons at a high risk of developing type 2 diabetes in the future [5–7]. However, the data regarding the role of inflammatory biomarkers, such as high-sensitivity C-reactive protein, tumor necrosis factor-alpha or interleukin-6, are limited. Furthermore, these novel markers are also expensive and inconvenient practically for use in primary health care [8]. Among the traditional risk factors, levels of fasting plasma glucose (FPG) and triglycerides (TG), it is well established that they are associated with an increased risk of type 2 diabetes [9–11]. The triglycerides-glucose (TyG) index, the product of FPG and triglycerides is a novel index that has been suggested as a surrogate of IR [12–15]. It was correlated with the homeostasis model assessment of IR (HOMA-IR) and total glucose metabolism rate during hyperinsulinemic-euglycemic clamp studies [13,16,17]. Although there were many studies on the TyG index as a predictor of type 2 diabetes in Iranian, Korean and the white European population, it may not be universally applicable among all populations and to date no study has examined type 2 diabetes incidences with the TyG index in a Thai population. In this study, we aimed, first, to determine whether the TyG index could be a useful marker for type 2 diabetes diagnosis and, secondly, to compare the TyG index to FPG and to TG as predictors of type 2 diabetes.

2.

Method

2.1.

Study design and setting

A historical cohort study was conducted in the primary care unit of Songklanagarind Hospital between 1 January 2007 and 31 December 2016. Songklanagarind Hospital is a tertiary care hospital, affiliated to the Faculty of Medicine, Prince of Songkla University, located in Hat Yai, Songkhla Province, Thailand.

2.2.

Subjects

We collected data from the medical records of patients aged ≥20 years who visited the primary care unit (PCU) or general practice (GP) clinic at Songklanagarind Hospital from 1 January 2007 to 31 December 2007 and were followed up until 31 December 2016. The exclusion criteria were previously diagnosed diabetes, never had a fasting blood sugar and serum lipid test between 2007 and 2016, had fasting blood sugar test (at least one time per year) <4 years. The initial number of recruited medical records was 18,977 and 18,351 met the exclusion criteria. Finally, 9 participants were excluded

due to incomplete data, leaving 617 participants available for final analyses. The research was approved by the Ethics Committee of Faculty of Medicine of Prince of Songkla University. Patient confidentiality was protected by codifying the recorded information and all results were reported in overall data.

2.3.

Data collection

The following data regarding anthropometric measurements, clinical history and blood analyses were derived from the medical records at baseline and at each follow-up visit. Anthropometric measurements included weight, height, body mass index (BMI) and blood pressure. BMI was defined as weight divided by the square of height and expressed in units of kg/m2 . Clinical history of date of hypertension, diabetes, dyslipidemia diagnosis and number of follow-up years were recorded. The biochemical data including FPG, uric acid, cholesterol, TG, high-density lipoprotein (HDL), and lowdensity lipoprotein (LDL) were also retrieved. The TyG index was calculated using the following formula: ln[triglyceride (mg/dl) × FPG (mg/dl)/2] [18–20]. For continuous variables, the values from every visit in each year of follow-up were calculated as a mean. For categorical variable, the values were collected by latest value.

2.4.

Data management and analysis

Data analyses were performed using R program (R Core Team 2017, Vienna, Austria). Categorical data were expressed as percentages. Continuous data were presented as mean ± standard deviation (SD) or median and interquartile range (IQR). The Mann–Whitney U, T test and chi-squared test were used to compare the characteristics of the subgroups according to the development of diabetes. For comparing the characteristics of the TyG index quartiles, the ANOVA and Kruskal–Wallis test was used. We conducted the mixed effects cox regression analysis to calculate hazard ratio (HR) and 95% CI for testing the association of the various factors in predicting the development of type 2 diabetes. The area under the curve (AUC) of the receiver operating characteristics (ROC) curve and the 95% CI were calculated to compare the predictive power of the TyG index, FPG and TG. A p-value less than 0.05 was considered statistically significant.

2.5.

Ethic consideration

The authors’ protocol was approved by the Ethics Committee of the Faculty of Medicine of Prince of Songkla University. Patient confidentiality was protected by codifying the recorded information and all results were reported in overall data.

3.

Results

The baseline characteristics of 617 participants are shown in Tables 1 and 2. Two-thirds of them were female and mean age at baseline was 66.88 year (SD 10.18). There were 163 incident cases of type 2 diabetes in 4,871.56 person-years of follow-up. During a median follow-up of 9.28 years, the overall cumulative incidence was 26.42% or incidence density was

Please cite this article in press as: P. Chamroonkiadtikun, et al., The triglyceride-glucose index, a predictor of type 2 diabetes development: A retrospective cohort study, Prim. Care Diab. (2019), https://doi.org/10.1016/j.pcd.2019.08.004

PCD-821; No. of Pages 7

ARTICLE IN PRESS 3

p r i m a r y c a r e d i a b e t e s x x x ( 2 0 1 9 ) xxx–xxx

Table 1 – Baseline characteristics of participants according to type 2 diabetes. Characteristic

Total (n = 617)

Non-diabetes (n = 454)

Diabetes (n = 163)

Gender Male Female Age (year) Weight (IQR) (kg) BMI (IQR) (kg/m2 ) SBP (IQR) (mmHg) DBP (mmHg) Hypertension (%) Dyslipidemia (%) FPG (IQR) (mg/dl) Cholesterol (mg/dl) Triglyceride (IQR) (mg/dl) HDL-cholesterol (IQR) (mg/dl) LDL-cholesterol (mg/dl) Uric acid (IQR) (mg/dl) TyG index

236 (38.2%) 381 (61.8%) 66.88 ± 10.18 64.62 (58.02,71.58) 25.65 (23.76,27.95) 134.5 (122.8,143.5) 79.8 ± 9.61 55.9 66.1 98 (92.67,106) 220.9 ± 35.4 139 (104.4,191.5) 51.11 (44.11,59.85) 130.17 ± 40.3 6.25 (5.18,7.3) 8.84 ± 0.486

176 (38.8%) 278 (61.2%) 67.25 ± 10.45 63.47 (56.8,69.24) 25.24 (23.44,27.35) 133.42 (120.78,142.19) 78.98 ± 9.62 53.7 66.3 96 (91.12,102) 221.71 ± 35.5 132.8 (100,185.7) 52.04 (44.88,60.69) 128.52 ± 40.78 6.3 (5.2,7.3) 8.78 ± 0.49

60 (36.8%) 103 (63.2%) 65.85 ± 9.35 68.83 (62.56,76.19) 27.43 (25.02,29.48) 137.4 (127.2,147.3) 82.13 ± 9.21 62 65.6 107 (101,114.8) 222.74 ± 35.18 156 (124,194.2) 48.98 (43.25,56.36) 134.9 ± 38.62 6.1 (5,7.16) 9.02 ± 0.42

p-Value

0.72 0.13 <0.01 <0.01 <0.01 <0.01 0.08 0.96 <0.01 0.44 <0.01 <0.01 0.09 0.76 <0.01

Data are expressed as means ± SD, % or median (25th–75th percentiles). BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TyG, triglyceride-glucose.

Table 2 – Baseline characteristics of participants according to the TyG index quartiles. Characteristic

Q1 (n = 142)

Q2 (n = 149)

Q3 (n = 151)

Q4 (n = 175)

p-Value

Gender Male Female Age (year) Weight (IQR) (kg) BMI (IQR) (kg/m2 ) SBP (IQR) (mmHg) DBP (mmHg) Hypertension (%) Dyslipidemia (%) FPG (IQR) (mg/dl) Cholesterol (mg/dl) Triglyceride (IQR) (mg/dl) HDL-cholesterol (IQR) (mg/dl) LDL-cholesterol (mg/dl) Uric acid (IQR) (mg/dl)

38 (26.8%) 104 (73.2%) 63.98 ± 11.09 62.05 (53.51,67.44) 24.83 (22.29,26.73) 130.2 (117.8,140.4) 80 (72.74,85.62) 64 (45.1%) 68 (47.9%) 93.25 (89,99) 212.6 ± 37.53 79 (66,90.92) 61.9 (53.99,71.4) 126.95 ± 41.25 5.38 (4.53,6.18)

50 (33.6%) 99 (66.4%) 67.02 ± 10.05 63.41 (57.53,69.06) 25.72 (23.79,27.92) 137.2 (123.5,145.8) 79 (73,86.43) 89 (59.7%) 92 (61.7%) 97.50 (92,102.5) 220.03 ± 32.74 121 (111,130) 54.4 (48.38,62.96) 133.05 ± 35.25 6.4 (5.2,7.1)

59 (39.1%) 92 (60.9%) 68.76 ± 10.03 65.9 (59.9,73.69) 25.86 (24.34,28.27) 135 (125.5,143.3) 79 (72.58,84.88) 94 (62.3%) 107 (70.9%) 100 (94.58,108.63) 222.98 ± 34.35 156 (142.5,170.5) 49.01 (43.55,55.2) 133.96 ± 41.58 6.5 (5.4,7.5)

89 (50.9%) 86 (49.1%) 67.51 ± 9.19 66.85 (60.67,74.73) 26.27 (24.2,28.31) 135.5 (124.3,144.8) 81.33 (73.9,88.45) 98 (56%) 141 (80.6%) 102 (96,109.8) 226.66 ± 35.67 225.5 (197.5,283.1) 44.98 (39.54,50.2) 126.83 ± 42.35 6.78 (5.83,7.55)

<0.01 <0.01 <0.01 <0.01 <0.01 0.01 0.26 0.06 <0.01 <0.01 <0.01 <0.01 <0.01 0.27 <0.01

Data are expressed as means ± SD, % or median (25th–75th percentiles). BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; HDL, high-density lipoprotein; LDL, low-density lipoprotein.

33.46 cases/1000 person-years. The baseline characteristics were compared according to diabetes status and TyG index quartile groups. Those who developed diabetes had a higher weight, BMI, SBP, DBP, FPG, triglyceride, HDL-cholesterol and TyG index. Participants in the higher quartile groups had a higher age, weight, BMI, FPG, cholesterol, triglyceride, uric acid, proportion of men and dyslipidemia while the level of HDL-cholesterol decreased in proportion to the TyG index quartiles. The incidence and risk of type 2 diabetes according to the quartiles of the TyG index are shown in Table 3. The incidence density of diabetes increased across the quartiles of the TyG index. The incidence was 17.28 per 1000 person-years for participants in the first quartile and 47 per 1000 personyears for participants in the fourth quartile. Compared with the first quartile, the hazard ratio (HR) of diabetes after age and sex were adjusted and increased across the quartiles of the TyG index. The risk of diabetes in the fourth, third and

second quartile were 3.4 times higher (95% CI 2.43–4.75), 2.25 times higher (95% CI 1.62–3.13) and 1.92 times higher (95% CI 1.4–2.65), respectively. After using the multivariate adjusted analysis, the HR of diabetes also increased across the quartiles of the TyG index. The HR of diabetes in the fourth, third and second quartile was 3.38 times higher (95% CI 2.38–4.8), 1.95 times higher (95% CI 1.4–2.71) and 1.55 times higher (95% CI 1.13–2.12), respectively, when compared with the first quartile (p for trend < 0.001). We further assessed the association between the quartiles of the TyG index and the risk of diabetes among normal fasting glucose (NFG) and impaired fasting glucose (IFG) subgroups. In participants with NFG, the risk of incident diabetes increased with the rising quartiles of the TyG index with corresponding HR as follows: second quartile HR 1.59 (0.81–3.15), third quartile HR 1.64 (0.78–3.47) and fourth quartile 5.51 (2.68–11.30) (p for trend < 0.001). This association still persisted among the IFG subgroup, the HR of diabetes in the fourth, and the third and second quartile were 2.18

Please cite this article in press as: P. Chamroonkiadtikun, et al., The triglyceride-glucose index, a predictor of type 2 diabetes development: A retrospective cohort study, Prim. Care Diab. (2019), https://doi.org/10.1016/j.pcd.2019.08.004

PCD-821; No. of Pages 7

ARTICLE IN PRESS

4

p r i m a r y c a r e d i a b e t e s x x x ( 2 0 1 9 ) xxx–xxx

Table 3 – Incidence density and risk of type 2 diabetes by the quartile of triglycerides-glucose index.

Number of cases Person-years Incidence/1000 person-years Unadjusted (crude) Sex and age adjusted Multivariate adjusteda a

TyG Q1

TyG Q2

TyG Q3

TyG Q4

p-Value

21 1215.6 17.28 1 1 1

22 1220.5 18.03 1.85 (1.35–2.55) 1.92 (1.4–2.65) 1.55 (1.13–2.12)

59 1137.6 51.86 2.14 (1.54–2.97) 2.25 (1.62–3.13) 1.95 (1.4–2.71)

61 1297.9 47 3.25 (2.33–4.54) 3.4 (2.43–4.75) 3.38 (2.38–4.8)

<0.01 <0.01 <0.001

Included variables were history of hypertension, history of dyslipidemia, cholesterol, LDL, BMI, diastolic blood pressure and age.

(1.44–3.31), 1.52 (1.04–2.22) and 1.24 (0.86–1.77), respectively (p for trend < 0.001).

3.1. The association between diabetes and other parameters The mixed effects cox regression analysis was performed to evaluate the association of the various parameters in predicting the development of type 2 diabetes as shown in Table 4. In the univariate model, developing diabetes was significantly correlated with age, hypertension, dyslipidemia, cholesterol, LDL, FPG, BMI, DBP and TyG index. Furthermore, the risk of future diabetes was significantly associated with age, hypertension, dyslipidemia, cholesterol, LDL, FPG, BMI and TyG index in the multivariate model.

3.2.

The ROC curves for the incidence of diabetes

The ROC curves of type 2 diabetes for the TyG index, FPG and TG are shown in Fig. 1. The area under the ROC curves for the TyG index was 0.64 (95% CI 0.60–0.69), for FPG it was 0.79 (95% CI 0.74–0.83), and for TG it was 0.59 (95% CI 0.54–0.64). The TyG index and FPG were significant predictors for future diabetes (p < 0.001). The area under the curve for FPG was higher than the TyG index and TG (p < 0.001). Accordingly, the FPG was better than the TyG index and TG in predicting future diabetes. After re-analyzing the data with impaired fasting glucose subjects, the ROC curves for TyG was 0.58 (95% CI 0.52–0.65), for FPG it was 0.70 (95% CI 0.64–0.77), and for TG it was 0.56 (95% CI 0.49–0.63). In the impaired fasting glucose subgroup, the area under the curve for FPG was higher than the TyG index and TG (p < 0.05).

4.

Discussion

In this retrospective cohort study, we found that the TyG index is a strong predictor of developing type 2 diabetes. The risk of diabetes was increased across the quartiles of the TyG index. Among the participants without diabetes at baseline, those in the highest quartile of the TyG index had a 3.4 times higher risk of developing diabetes than those in the lowest quartile. Although the TyG index could be a predictive test for type 2 diabetes, the FPG seemed to be more a potent predictor of type 2 diabetes. Our observed prevalence and incidence of diabetes were higher than in other studies. This may be due to most of these studies being population based [2,21], whereas ours was a hospital-based study in which participants had more risk fac-

tors of diabetes, including diagnosed hypertension, diagnosed dyslipidemia and obesity. Many studies have assessed the TyG index as a surrogate for insulin resistance (IR). Guerrero-Romero et al. [13] proposed that the TyG index could be the predictor for the identification of individuals at risk of decreased insulin sensitivity because their study results showed a moderate correlation between the TyG index and the homeostatic model assessment of insulin resistance (HOMA-IR). Abbasi et al. [14] reported that the TyG index has been significantly associated with insulin-stimulated glucose uptake measured as the steady-state plasma glucose concentration during insulin suppression testing in subjects without diabetes. In a Canadian population based study, Bastard et al. [22] showed that the TyG index and European group for the study of insulin resistance (EGIR) insulin sensitivity/resistance (IS/R) indices were only modestly related to the hyperinsulinaemic-euglycaemic clamp in postmenopausal women, but fasting- and OGTTderived IS/R surrogate indices appeared to be more accurate in estimating IS/R. Vasques et al. [23] concluded that the TyG index correlated with adiposity, metabolic and atherosclerosis markers related to the IR and TyG index which represents an accessible tool for the assessment of IR in clinical practice in Brazilian participants. Furthermore, Du et al. [15] showed that the TyG index was the most robust marker for the early identification of IR individuals. However, there are many studies that have assessed the association between the risk of developing diabetes and the TyG index. In the Chungju Metabolic Disease Cohort Study, Lee et al. [20] reported that the TyG index was a better predictor of developing diabetes than other IR indices, including HOMA-IR or TG/HDL-C. Lee et al. [24] reported similar results in a study conducted among Korean subjects that showed that the TyG index was significantly correlated with developing diabetes. In a population-based cohort study among a white European population, Navarro-Gonzalez et al. [18] showed that the increment in the TyG index per 1-SD was significantly associated with a 54% increased incident of diabetes. From the same populations [18], they also found that the TyG index is a more useful tool for predicting future diabetes than the FPG or TG. In a Chinese population based study, Zhang et al. [25] showed that an increase in the TyG index was associated with an increased risk of diabetes, and suggested that the TyG index may be a useful tool for predicting diabetes among people with a normal weight. In contrast to the above studies, our data analysis showed that FPG appeared to be a more robust predictor of diabetes than the TyG index. Our results are in concordance with previous studies. Janghorbani et al. [26] conducted a study among an Iranian population with nondiabetes first-degree relatives. They reported that the TyG

Please cite this article in press as: P. Chamroonkiadtikun, et al., The triglyceride-glucose index, a predictor of type 2 diabetes development: A retrospective cohort study, Prim. Care Diab. (2019), https://doi.org/10.1016/j.pcd.2019.08.004

PCD-821; No. of Pages 7

ARTICLE IN PRESS 5

p r i m a r y c a r e d i a b e t e s x x x ( 2 0 1 9 ) xxx–xxx

Table 4 – Associated parameters with type 2 diabetes. Multivariate analysisa

Univariate analysis

Hypertension Dyslipidemia Cholesterol HDL LDL Triglyceride Fasting plasma glucose Body mass index Systolic blood pressure Diastolic blood pressure Age Sex Uric acid TyG index

Hazard ratio (HR) [95% CI]

p-Value

Hazard ratio (HR) [95% CI]

p-Value

3.21 [2.35–4.38] 5.86 [3.73–9.20] 0.99 [0.98–0.99] 1.00 [0.99–1.01] 0.99 [0.98–0.99] 0.998 [0.99–1.00] 1.03 [1.02–1.03] 1.11 [1.07–1.15] 1.00 [0.99–1.01] 0.99 [0.98–0.99] 0.97 [0.96–0.99] 1.12 [0.79–1.61] 0.98 [0.88–1.09] 2.72 [2.19–3.38]

<0.001 <0.001 <0.001 0.56 <0.001 0.18 <0.001 <0.001 0.16 <0.05 <0.01 0.52 0.69 <0.001

2.09 [1.38–3.16] 2.51 [1.50–4.21] 0.97 [0.96–0.98]

<0.001 <0.001 <0.001

1.02 [1.01–1.03]

<0.001

1.02 [1.01–1.03] 1.10 [1.04–1.15]

<0.001 <0.001

0.99 [0.98–1.00] 0.96 [0.94–0.99]

0.21 <0.01

2.03 [1.38–3.00]

<0.001

HR, hazard ratio; CI, confidence interval; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TyG, triglyceride-glucose. a Included variables were history of hypertension, history of dyslipidemia, cholesterol, LDL, BMI, diastolic blood pressure and age.

Fig. 1 – Receiver operative characteristic curves for fasting plasma glucose (FPG), triglyceride-glucose (TyG) index and triglyceride (TG) for prediction of type 2 diabetes.

index is a predictive tool for type 2 diabetes, but FPG seemed to be a more robust predictor of type 2 diabetes than the TyG index. In addition, Tohidi et al. [27] also showed that FPG was the strongest predictor of diabetes compared to the other surrogates of IR, including the TyG index, TG/HDL and HOMA-IR indices among an Iranian population. Regarding the discriminatory powers of diagnostic ability, our ROC curve analysis showed that the highest AUC was attributed to FPG. The AUC for FPG, TyG index and TG were 0.79, 0.64 and 0.59, respectively. The superiority of FPG over the TyG index was also reported in the studies conducted by Janghorbani et al. [26], where it was shown that the AUC for FPG was 0.76, for TyG it was 0.65, and for TG it was 0.59. Tohidi et al. [27] results: AUC for FPG was 0.75 and for TyG 0.69.

Among the risk factors of diabetes using multivariate analysis, our results showed that the risk of diabetes increased across the rising quartiles of the TyG index. Our results are in concordance with previous studies, including NavarroGonzalez et al. [18], Janghorbani et al. [26], Lee et al. [20] and Low et al. [28]. The strength of this study: It was a long-term follow-up cohort study and had sufficient participants for precise analysis. Moreover, we collected data in a clinical setting and updated the data at every follow-up visit. Therefore, we could observe the change of risk factors of diabetes over the followup time. Our study has several limitations: First, the study design was retrospective. Second, we did not assess other biomarkers, such as the 2-h oral glucose tolerance test or glycosylated hemoglobin in order to confirm the diagnosis of

Please cite this article in press as: P. Chamroonkiadtikun, et al., The triglyceride-glucose index, a predictor of type 2 diabetes development: A retrospective cohort study, Prim. Care Diab. (2019), https://doi.org/10.1016/j.pcd.2019.08.004

PCD-821; No. of Pages 7

ARTICLE IN PRESS

6

p r i m a r y c a r e d i a b e t e s x x x ( 2 0 1 9 ) xxx–xxx

diabetes. We also did not have data on insulin levels for supporting the role of the TyG index in predicting future diabetes. Third, we could not assess the HOMA-IR. Fourth, we did not collect data on dietary habits. Even though we had no data on this factor, we used other parameters indirectly related to dietary habits, such as BMI, cholesterol and LDL in the multivariate analysis for the development of diabetes. Finally, we lacked information on antihypertensive and lipid lowering agents, which might have affected the results.

[3]

[4]

[5]

5.

Conclusion

The TyG index was significantly associated with the risk of incident diabetes and could be a valuable biomarker of developing diabetes. Although FPG appeared to be a more robust predictor of diabetes, the TyG index may help identify normal blood sugar individuals who have an early risk of future diabetes. The TyG index can be derived from routine clinical measurements and it is inexpensive; thus, the TyG index can be of value for applying this index in diabetes risk assessment in clinical practice. Therefore, it may be necessary to attach great importance to the assessment and management of risk for developing diabetes in patients with high TG.

Author contributions P.C. contributed to the generation of the database and data research, extracted the data, performed data analysis, drafted and revised the manuscript. T.A. contributed to the generation of the database and data research, extracted the data and drafted the manuscript. W.W. contributed to the generation of the database and data research and extracted the data. All authors read and approved the final manuscript.

Funding

[6]

[7]

[8]

[9]

[10]

[11]

[12]

[13]

This research did not receive a specific grant from any funding agency in the public, commercial, or not-for-profit sectors. [14]

Conflict of interest The authors state that they have no conflict of interest.

Acknowledgements

[15]

We acknowledge the assistance of Mr. Trevor Pearson for his assistance in editing the English language of this paper. [16]

references [17] [1] International Diabetes Federation, IDF Diabetes Atlas, 8th edn., International Diabetes Federation, Brussels, Belgium, 2017. [2] W. Aekplakorn, S. Chariyalertsak, P. Kessomboon, et al., Prevalence of diabetes and relationship with socioeconomic status in the thai population: national health examination

[18]

survey, 2004-2014, J. Diabetes Res. 2018 (2018), 1654530, http://dx.doi.org/10.1155/2018/1654530. W. Jiamjarasrangsi, W. Aekplakorn, Incidence and predictors of type 2 diabetes among professional and office workers in Bangkok, Thailand, J. Med. Assoc. Thai. 88 (2005) 1896–1904. W. Jiamjarasrangsi, V. Lohsoonthorn, S. Lertmaharit, S. Sangwatanaroj, Incidence and predictors of abnormal fasting plasma glucose among the university hospital employees in Thailand, Diabetes Res. Clin. Pract. 79 (2008) 343–349. A. Abbasi, L.M. Peelen, E. Corpeleijn, et al., Prediction models for risk of developing type 2 diabetes: systematic literature search and independent external validation study, BMJ 18 (345) (2012) e5900, http://dx.doi.org/10.1136/bmj.e5900. B. Buijsse, R.K. Simmons, S.J. Griffin, M.B. Schulze, Risk assessment tools for identifying individuals at risk of developing type 2 diabetes, Epidemiol. Rev. 33 (2011) 46–62, http://dx.doi.org/10.1093/epirev/mxq019. D. Noble, R. Mathur, T. Dent, et al., Risk models and scores for type 2 diabetes: systematic review, BMJ 343 (2011) d7163, http://dx.doi.org/10.1136/bmj.d7163. S. Liu, L. Tinker, Y. Song, et al., A prospective study of inflammatory cytokines and diabetes mellitus in a multiethnic cohort of postmenopausal women, Arch. Intern. Med. 167 (2007) 1676–1685, http://dx.doi.org/10.1186/s12933-014-0146-3. A. Tirosh, I. Shai, D. Tekes-Manova, et al., Normal fasting plasma glucose levels and type 2 diabetes in young men, N. Engl. J. Med. 353 (2005) 1454–1462. S.Y. Rhee, J.T. Woo, The prediabetic period: review of clinical aspects, Diabetes Metab. J. 35 (2011) 107–116, http://dx.doi.org/10.4093/dmj.2011.35.2.107. M. Janghorbani, M. Amini, Normal fasting plasma glucose and risk of prediabetes and type 2 diabetes: the Isfahan Diabetes Prevention Study, Rev. Diabet. Stud. 8 (2011) 490, http://dx.doi.org/10.1900/RDS.2011.8.490. L.E. Simental-Mendía, M. Rodríguez-Morán, F. Guerrero-Romero, The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects, Metab. Syndr. Relat. Disord. 6 (2008) 299–304, http://dx.doi.org/10.1089/met.2008.0034. F. Guerrero-Romero, L.E. Simental-Mendía, M. González-Ortiz, et al., The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemichyperinsulinemic clamp, J. Clin. Endocrinol. Metab. 95 (2010) 3347–3351, http://dx.doi.org/10.1210/jc.2010-0288. F. Abbasi, G.M. Reaven, Comparison of two methods using plasma triglyceride concentration as a surrogate estimate of insulin action in nondiabetic subjects: triglycerides 9 glucose versus triglyceride/high-density lipoprotein cholesterol, Metabolism 60 (2011) 1673–1676, http://dx.doi.org/10.1016/j.metabol.2011.04.006. T. Du, G. Yuan, M. Zhang, et al., Clinical usefulness of lipid ratios, visceral adiposity indicators, and the triglycerides and glucose index as risk markers of insulin resistance, Cardiovasc. Diabetol. 13 (2014) 146, http://dx.doi.org/10.1186/s12933-014-0146-3. J.H. Warram, B.C. Martin, A.S. Krolewski, et al., Slow glucose removal rate and hyperinsulinemia precede the development of type II diabetes in the offspring of diabetic parents, Ann. Intern. Med. 113 (1990) 909–915. R.A. DeFronzo, From the triumvirate to the ominous octet: a new paradigm for the treatment of type 2 diabetes mellitus, Diabetes 58 (2009) 773–795. ˜ D. Navarro-González, L. Sánchez-Ínigo, J. Pastrana-Delgado, et al., Triglyceride-glucose index (TyG index) in comparison with fasting plasma glucose improved diabetes prediction in

Please cite this article in press as: P. Chamroonkiadtikun, et al., The triglyceride-glucose index, a predictor of type 2 diabetes development: A retrospective cohort study, Prim. Care Diab. (2019), https://doi.org/10.1016/j.pcd.2019.08.004

PCD-821; No. of Pages 7

ARTICLE IN PRESS p r i m a r y c a r e d i a b e t e s x x x ( 2 0 1 9 ) xxx–xxx

[19]

[20]

[21]

[22]

[23]

patients with normal fasting glucose: the vascular-metabolic CUN cohort, Prev. Med. 86 (2016) 99–105, http://dx.doi.org/10.1016/j.ypmed.2016.01.022. S. Zheng, S. Shi, X. Ren, et al., Triglyceride glucose-waist circumference, a novel and effective predictor of diabetes in first-degree relatives of type 2 diabetes patients: cross-sectional and prospective cohort study, J. Transl. Med. 14 (2016) 260, http://dx.doi.org/10.1186/s12967-016-1020-8. S.H. Lee, H.S. Kwon, Y.M. Park, et al., Predicting the development of diabetes using the product of triglycerides and glucose: the Chungju Metabolic Disease Cohort (CMC) study, PLoS One 9 (2014), e90430, http://dx.doi.org/10.1371/journal.pone.0090430. C. Deerochanawong, A. Ferrario, Diabetes management in Thailand: a literature review of the burden, costs, and outcomes, Glob. Health 9 (2013) 11, http://dx.doi.org/10.1186/1744-8603-9-11. J.P. Bastard, M.E. Lavoie, V. Messier, et al., Evaluation of two new surrogate indices including parameters not using insulin to assess insulin sensitivity/resistance in non-diabetic postmenopausal women: a MONET group study, Diabetes Metab. 38 (2012) 258–263, http://dx.doi.org/10.1016/j.diabet.2012.01.004. A.C. Vasques, F.S. Novaes, S. de Oliveira Mda, et al., TyG index performs better than HOMA in a Brazilian population: a hyperglycemic clamp validated study, Diabetes Res. Clin. Pract. 93 (2011) e98–e100, http://dx.doi.org/10.1016/j.diabres.2011.05.030.

7

[24] D.Y. Lee, E.S. Lee, J.H. Kim, et al., Predictive value of triglyceride glucose index for the risk of incident diabetes: a 4-year retrospective longitudinal study, PLoS One 11 (2016), e0163465, http://dx.doi.org/10.1371/journal.pone.0163465. [25] M. Zhang, B. Wang, Y. Liu, 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. 16 (2017) 30, http://dx.doi.org/10.1186/s12933-017-0514-x. [26] M. Janghorbani, S.Z. Almasi, M. Amini, The product of triglycerides and glucose in comparison with fasting plasma glucose did not improve diabetes prediction, Acta Diabetol. 52 (2015) 781–788, http://dx.doi.org/10.1007/s00592-014-0709-5. [27] M. Tohidi, A. Baghbani-Oskouei, N.S. Ahanchi, et al., Fasting plasma glucose is a stronger predictor of diabetes than triglyceride-glucose index, triglycerides/high-density lipoprotein cholesterol, and homeostasis model assessment of insulin resistance: Tehran lipid and glucose study, Acta Diabetol. 55 (2018) 1067–1074, http://dx.doi.org/10.1007/s00592-018-1195-y. [28] S. Low, K.C.J. Khoo, B. Irwan, et al., The role of triglyceride glucose index in development of Type 2 diabetes mellitus, Diabetes Res. Clin. Pract. 143 (2018) 43–49, http://dx.doi.org/10.1016/j.diabres.2018.06.006.

Please cite this article in press as: P. Chamroonkiadtikun, et al., The triglyceride-glucose index, a predictor of type 2 diabetes development: A retrospective cohort study, Prim. Care Diab. (2019), https://doi.org/10.1016/j.pcd.2019.08.004