The product of fasting plasma glucose and triglycerides improves risk prediction of type 2 diabetes in middle-aged Koreans

Type 2 diabetes mellitus (T2DM) is one of the most prevalent non-communicable diseases in the middle-aged population worldwide, largely because of recent changes in diet and lifestyle [1, 2]. In the Korean National Health and Nutrition Examination Surveys (KNHANES), the prevalence of diabetes among adults aged 30 and over was found to have slightly increased, from 12.4% in 2011 to 13.0% in 2014 [3, 4]. According to an estimate by the International Diabetes Federation, 46.3% of cases of diabetes in Koreans aged 20–79 were undiagnosed in 2015 [5]. However, lifestyle intervention can reduce the risk of incident T2DM and mortality in individuals at high risk of diabetes [6, 7]. Therefore, it is important to screen high-risk individuals for T2DM regularly to ensure early diagnosis. For this reason, risk models for diabetes have been proposed in previous studies, and, recently, T2DM risk prediction models have been reported in Korea [8‐14].

Obesity is the most significant risk factor for incident T2DM [15, 16]. Body mass index (BMI) has been used as a surrogate marker for obesity and included as one of the variables in most risk models for T2DM [9, 11‐14]. However, BMI does not reflect central obesity. Lee et al. selected waist circumference (WC) instead of BMI in their diabetes risk model considering its association with diabetes [10]. In the systematic review, more than 30% of the diabetes risk models stating its components included both BMI and WC [8]. To improve the prediction ability of a risk model for incident T2DM, blood parameters are also frequently included [8].

Also, the increase of fasting plasma glucose (FPG) in the normal range is associated with increased incident T2DM [17]. Of these, serum triglyceride (TG) has been used to identify people at high risk for T2DM, alongside obesity [18]. In addition, lipid accumulation product (LAP) and the product of fasting plasma glucose and triglyceride (TyG index), composite indices including TG, have been proposed as predictors of T2DM [19, 20]. In particular, TyG index has been used as a marker of insulin resistance [19]. Although the simple diabetes risk model is convenient for self-assessment, more accurate prediction models that include blood parameters are also required to facilitate more accurate clinical consultations [8]. In Korea, most people are registered with the National Health Insurance (NHI), which provides biannual medical check-ups for middle-aged people, including the measurement of key blood parameters [21]. Therefore, risk models for incident T2DM that are based on the data obtained from these medical check-ups would be of great use for the prediction of risk of future T2DM. To date, few studies have been undertaken in Korea that compare the predictive ability for incident T2DM of the simple model and composite models, which include blood test results [11, 22]. Recently, a risk model for T2DM that included blood test results was proposed based on cohort data, and its reclassification ability was significantly improved when glycated hemoglobin (HbA_1c) was included [11]. However, HbA_1c is not assessed in the routine health examination and this study did not consider reclassification ability when other blood test results apart from HbA_1c were included in the T2DM risk model [23].

Therefore, in the present study, we aimed to identify which of the TG-related indices that can be derived from general check-up data would improve the prediction ability of the simple T2DM risk model in middle-aged Koreans.

We showed that the predictive ability of the simple T2DM risk model was increased when TG-related indices or FPG was added. Recently, several diabetes risk models have been proposed to screen high-risk groups, some of which included blood parameters. WC was included as one of the components in the model because increased WC increases T2DM risk [8]. However, WC is not easily used in practice because of measurement inaccuracy [34]. Among the TG-related indices analyzed in the present study, the AROC and reclassification ability of TyG index were higher than for TG or LAP. Although the inclusion of LAP also improved the predictive ability of the risk model for incident diabetes, this required the additional inclusion of WC, while this was not required to demonstrate improvements in the risk model by the inclusion of TG or TyG index [20]. Most studies that used TyG index to predict incident T2DM used it as a surrogate for insulin resistance, and reported that its predictive ability is better than that of the homeostasis model of assessment (HOMA-IR) [19, 35, 36]. However, Abbasi and Reaven showed that the correlation between insulin-mediated glucose uptake (IMGU) and TyG index is not better than that between IMGU and TG or HOMA-IR [37, 38]. Nevertheless, it was noted that TyG index is a practical measure for use in T2DM prediction because of its cost-efficiency [37]. Moreover, the use of HbA_1c has been recommended for diagnosis and screening of patients. Lim et al. added HbA_1c to a diabetes risk model and demonstrated an increase in predictive ability for T2DM in a middle-aged Korean cohort, while Ahn et al. also demonstrated through longitudinal validation analysis that adding FPG or HbA_1c to the simple diabetes risk model also improves its predictive ability [11, 22]. Thus, in general, the predictive ability of a diabetes risk model that includes laboratory parameters such as TG, FPG, or HbA_1c is better than that of the simple diabetes risk model [11, 14, 22].

The advantages of the simple diabetes risk model are that it is inexpensive and patients can use it for self-assessment [8]. Weighed against the improved predictive accuracy obtained by the addition of HbA_1c and the Oral Glucose Tolerance Test (OGTT) to the T2DM risk model, there are substantial practical constraints on screening the whole population using such measurements [22]. As an effective alternative, a two-pronged approach to screening has been proposed [22, 39]. This approach consists of using the non-laboratory score for the general population and using the laboratory score for patients in a higher-risk group for T2DM. For screening as part of the Korean NHI program, TG-related indices or FPG are more suitable as one of the components of the T2DM risk model than OGTT or HbA_1c, given that these measurements are not currently included in primary screening measurements in Korea [23]. To the best of our knowledge, there is no TyG index criterion yet. On the other hand, several studies conducted in Europe and Asian have shown that the risk of incident T2DM was increased with increasing TyG index [40‐42]. Some researchers have proposed a cut-off value of TyG index of 8.8 for incident T2DM and insulin resistance [41, 42]. The subjects in the present study were classified by the cut-off value of TyG index of 8.8. The adjusted odds ratio (95% CI) of incident T2DM in the subjects with TyG index ≥8.8 was 1.95 (1.73–2.20) compared to the counterparts. We also have proposed sex-specific cut-off points of TyG index (≥ 8.86 in men, ≥ 8.52 in women) as predictors of T2DM based on the maximized Youden index. Compared to the counterparts, the adjusted odds ratio (95% CI) of incident T2DM in the men with TyG index ≥8.86 and in the women with TyG index ≥8.52 were 2.01 (1.69–2.39) and 2.16 (1.82–2.56), respectively (Appendix 1). Considering NIH’s universe coverage, the health promotion that offered the general health check-ups for 17.6 million Koreans in 2016, and its following examination rate (77.7%), there should be less burden caused by blood tests than other countries [43]. Therefore, TyG index is recommended as a screening tool for the prediction of T2DM. Although TyG index predicts incident T2DM well, its usefulness is inconsistent, when compared to fasting glucose [38, 44]. For predicting T2DM risk, TyG index was not better than FPG or OGTT in Isfahan Diabetes Prevention Study [38]. Wang et al. reported that TyG index, LAP, and visceral adiposity were not superior to FPG or WC alone as diabetes predictors among Chinese [44]. In the Vascular-Metabolic CUN cohort, Navarro-Gonzalez et al. compared the prediction ability of TyG index and FPG for onset T2DM [35]. Association between indexes and their discrimination for onset T2DM were different depending on the fasting glucose subgroup. When the highest quartile for each index was compared to the lowest, the hazard ratios (HRs) of TyG index and FPG were 3.0 and 7.3 in the impaired fasting glucose group. On the other hand, TyG index showed stronger association with onset DM than FPG in the normal fasting glucose group (HRs: 6.8 vs. 4.6). The discrimination of TyG index for onset T2DM was also better than that of FPG in the normal fasting glucose group (AROC: 0.75 vs. 0.66). In the present study, we found the association between some metabolic syndrome (MetS) components and incident T2DM (Appendix 2). Therefore, we compared the discrimination of each index for incident T2DM in the stratified MetS components (Appendix 3). In the elevated FPG group, the discrimination of FPG (AROC: 0.506) for incident T2DM was inferior to that of other indices. On the other hand, TyG index was a more robust discrimination index than other indices in the group stratified by sex and MetS components. Our findings indicate that TyG index is not only a better predictor for incident diabetes than WC, LAP, and TG, but it also has a better reclassification ability. On the other hand, association between FPG and its(their) reclassification ability for incident T2DM were different depending on sex. The present study used community-based long-term prospective cohort data. Therefore, the temporal relationship between each measure and incident diabetes is clear. Our findings indicate that TyG index is not only a better predictor for incident diabetes than WC, LAP, and TG, but it also has a better reclassification ability. It was also noted that association between FPG and its reclassification ability for incident T2DM were different depending on sex. There were, however, a few limitations to this study. Firstly, even though KoGES is designed to include subjects who live both in rural and urban areas, the data are not representative of the entire Korean population [45]. Secondly, dietary intake was not included in the analysis. Moreover, Baik et al. had reported that the usefulness of dietary information in cardiovascular disease risk prediction models [46]. However, dietary information has been excluded from the questionnaire in medical check since 2009. Finally, our results were not validated using a separate Korean dataset. Therefore, additional studies are required to validate our data.

In conclusion, TG-related indices and FPG were more accurate than WC in the prediction of incident T2DM. In the subgroup categorized by sex and fasting glucose level, TyG index was a more robust predictor for onset T2DM than other indexes. Therefore, TyG index can be a useful screening tool for incident T2DM in middle-aged Koreans.