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

The product of fasting plasma glucose and triglycerides improves risk prediction of type 2... Background: Screening for risk of type 2 diabetes mellitus (T2DM) is an important public health issue. Previous studies report that fasting plasma glucose (FPG) and triglyceride (TG)-related indices, such as lipid accumulation product (LAP) and the product of fasting glucose and triglyceride (TyG index), are associated with incident T2DM. We aimed to evaluate whether FPG or TG-related indices can improve the predictive ability of a diabetes risk model for middle-aged Koreans. Methods: 7708 Koreans aged 40–69 years without diabetes at baseline were eligible from the Korean Genome and Epidemiology Study. The overall cumulative incidence of T2DM was 21.1% (766 cases) in men and 19.6% (797 cases) in women. Therefore, the overall cumulative incidence of T2DM was 20.3% (1563 cases). Multiple logistic regression analysis was conducted to compare the odds ratios (ORs) for incident T2DM for each index. The area under the receiver operating characteristic curve (AROC), continuous net reclassification improvement (cNRI), and integrated discrimination improvement (IDI) were calculated when each measure was added to the basic risk model for diabetes. Results: All the TG-related indices and FPG were more strongly associated with incident T2DM than WC in our study population. The adjusted ORs for the highest quartiles of WC, TG, FPG, LAP, and TyG index compared to the lowest, were 1.64 (95% CI, 1.13–2.38), 2.03 (1.59–2.61), 3.85 (2.99–4.97), 2.47 (1.82–3.34), and 2.79 (2.16–3.60) in men, and 1.17 (0.83–1.65), 2.42 (1.90–3.08), 2.15 (1.71–2.71), 2.44 (1.82–3.26), and 2.85 (2.22–3.66) in women, respectively. The addition of TG-related parameters or FPG, but not WC, to the basic risk model for T2DM (including age, body mass index, family history of diabetes, hypertension, current smoking, current drinking, and regular exercise) significantly increased cNRI, IDI, and AROC in both sexes. Conclusions: Adding either TyG index or FPG into the basic risk model for T2DM increases its prediction and reclassification ability. Compared to FPG, TyG index was a more robust T2DM predictor in the stratified sex and fasting glucose level. Therefore, TyG index should be considered as a screening tool for identification of people at high risk for T2DM in practice. Keywords: TyG index, Type 2 diabetes mellitus, Risk model * Correspondence: mdhypark@gmail.com Division of Cardiovascular Diseases, Center for Biomedical Sciences, Korea National Institute of Health, 187 Osongsaengmyeng 2-ro, Osong-eup, Heungdeok-gu, Cheongju-si, Chungcheongbuk-do 361-951, South Korea Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Lee et al. BMC Endocrine Disorders (2018) 18:33 Page 2 of 10 Background the prediction of risk of future T2DM. To date, few Type 2 diabetes mellitus (T2DM) is one of the most studies have been undertaken in Korea that compare prevalent non-communicable diseases in the middle-aged thepredictiveability for incidentT2DM ofthe sim- population worldwide, largely because of recent ple model and composite models, which include changes in diet and lifestyle [1, 2]. In the Korean blood test results [11, 22]. Recently, a risk model for National Health and Nutrition Examination Surveys T2DM that included blood test results was proposed (KNHANES), the prevalence of diabetes among adults based on cohort data, and its reclassification ability aged 30 and over was found to have slightly in- was significantly improved when glycated creased, from 12.4% in 2011 to 13.0% in 2014 [3, 4]. hemoglobin (HbA ) was included [11]. However, 1c According to an estimate by the International Dia- HbA is not assessed in the routine health examin- 1c betes Federation, 46.3% of cases of diabetes in Ko- ation and this study did not consider reclassification reans aged 20–79 were undiagnosed in 2015 [5]. ability when other blood test results apart from However, lifestyle intervention can reduce the risk of HbA were included in the T2DM risk model [23]. 1c incident T2DM and mortality in individuals at high Therefore, in the present study, we aimed to iden- risk of diabetes [6, 7]. Therefore, it is important to tify which of the TG-related indices that can be de- screen high-risk individuals for T2DM regularly to rived from general check-up data would improve the ensure early diagnosis. For this reason, risk models prediction ability of the simple T2DM risk model in for diabetes have been proposed in previous studies, middle-aged Koreans. and, recently, T2DM risk prediction models have been reported in Korea [8–14]. Methods Obesity is the most significant risk factor for inci- Study population dent T2DM [15, 16]. Body mass index (BMI) has The Korean Genome and Epidemiology Study been used as a surrogate marker for obesity and in- (KoGES) consists of a gene-environment model and cluded as one of the variables in most risk models for population-based studies [24]. KoGES: Ansan and T2DM [9, 11–14]. However, BMI does not reflect Ansung study is an ongoing prospective cohort study central obesity. Lee et al. selected waist circumference conducted in urban (Ansan) and rural (Ansung) (WC) instead of BMI in their diabetes risk model areas in Korea with biennial follow-ups, which considering its association with diabetes [10]. In the started in 2001. 10038 people underwent an initial systematic review, more than 30% of the diabetes risk examination, and 9001 subjects were included after models stating its components included both BMI exclusion of 1037 who refused to participate or died. and WC [8]. To improve the prediction ability of a Thirty-five participants were not suitable for the risk model for incident T2DM, blood parameters are present study because of their age, and subjects with also frequently included [8]. a history of diabetes at baseline or incomplete data Also, the increase of fasting plasma glucose (FPG) were also excluded. Finally, 7708 people aged 40 to in the normal range is associated with increased in- 69 years remained eligible for the current study. cident T2DM [17]. Of these, serum triglyceride (TG) Written informed consent was obtained from all has been used to identify people at high risk for subjects. The Institutional Review Board of the Ko- T2DM, alongside obesity [18]. In addition, lipid ac- rean Centers for Disease Control and Prevention ap- cumulation product (LAP) and the product of fasting proved the study protocol. plasma glucose and triglyceride (TyG index), com- posite indices including TG, have been proposed as Measurements and surveys predictors of T2DM [19, 20]. In particular, TyG Height and weight were measured to the nearest index has been used as a marker of insulin resist- 0.1 cm and 0.1 kg using a digital stadiometer and a ance [19]. Although the simple diabetes risk model scale, respectively. Resting blood pressure while sit- is convenient for self-assessment, more accurate pre- ting was measured by trained technicians using a diction models that include blood parameters are standard mercury sphygmomanometer. Blood sam- also required to facilitate more accurate clinical con- ples were collected after fasting for at least 8 h. The sultations [8]. In Korea, most people are registered Friedewald formula was used to indirectly estimate with the National Health Insurance (NHI), which low-density lipoprotein cholesterol levels in subjects provides biannual medical check-ups for middle-aged with plasma TG < 400 mg/dl [25]. Diabetes was de- people, including the measurement of key blood pa- fined by FPG > 126 mg/dl, 2 h post-challenge plasma rameters [21]. Therefore, risk models for incident glucose > 200 mg/dl, HbA > 6.5%, or prescription 1c T2DM that are based on the data obtained from for anti-diabetic medication [26]. Subjects were these medical check-ups would be of great use for questioned by trained interviewers regarding their Lee et al. BMC Endocrine Disorders (2018) 18:33 Page 3 of 10 socio-demographics, family history of diabetes, and Macros were used to calculate cNRI and IDI, and data lifestyle factors including smoking and alcohol con- analysis was performed using SAS 9.4 and MedCalc [32]. sumption. The subjects’ smoking and alcohol con- sumption status was subdivided according to their Results past and present habits. Regular exercise was defined Baseline characteristics as subjects’ exercise was over 90 min as the sum of Table 1 indicates the baseline characteristics of the study moderate and vigorous physical activity a day [27]. subjects. The ages of the subjects were 51.4 ± 8.6 years LAP and TyG index were calculated as follows: for men and 52.0 ± 8.9 years for women. The prevalence of hypertension was higher in men than in women (29.5% vs. 27.0%, P = 0.0149). By contrast, the percentage LAPformen ¼½ WCðÞ cm ‐65 TGðÞ mmol=L of subjects with a family history of diabetes was higher LAPfor women ¼½ WCðÞ cm ‐58 TGðÞ mmol=L in women than in men (11.0% vs. 9.2%, P = 0.0067). TyGindex ¼ ln½ TGðÞ mg=dl FPGðÞ mg=dl =2 There were also significant differences in anthropomet- ric indices between sexes. Mean BMI and WC were 24.1 ± 2.9 kg/m and 83.3 ± 7.6 cm in men, and 24.7 ± 3.2 kg/ Statistical analysis m and 81.2 ± 9.5 cm in women, respectively. Mean TG, Data are expressed as numbers and proportions for FPG, LAP, and TyG index were 170.8 ± 111.6 mg/dl, discrete variables and mean ± SD for continuous vari- 84.5 ± 9.0 mg/dl, 38.1 ± 34.0, and 8.7 ± 0.5 in men, and ables, and were analyzed using Chi-square and Student’s 140.9 ± 75.6 mg/dl, 81.1 ± 7.7 mg/dl, 39.0 ± 30.6, and 8.5 t-tests, respectively. ± 0.5 in women, respectively. TG-related indices also Based on the maximized Youden index, we calcu- showed significant differences between the sexes, with lated sex-specific cut-off points of each index for the exception of LAP. T2DM.Multiplelogisticregressionanalysiswas con- Table 1 Characteristics of the study population at baseline ducted and adjusted for age, BMI, hypertensive sta- Variables Men Women P- tus, family history of diabetes, current smoking and value (n = 3636) (n = 4072) alcohol consumption status, and regular exercise. Age, years 51.4 ± 8.6 52.0 ± 8.9 0.0011 The basic model was derived from the published diabetes risk model for middle-aged Koreans and in- Current Smoker, n(%) 1753 (48.2) 142 (3.5) <.0001 cluded the variables listed above [10, 11]. We tested Current Drinker, n(%) 2580 (71.0) 1085 (26.7) <.0001 multicollinearity for all covariates based on the vari- Hypertension, n(%) 1074 (29.5) 1101 (27.0) 0.0149 able inflation factor (VIF). The area under the re- Family history of diabetes, n(%) 333 (9.2) 449 (11.0) 0.0067 ceiver operating characteristic curve (AROC) was Regular exercise, n(%) 1712 (47.1) 1654 (40.6) <.0001 calculated for each risk model of incident T2DM, in- BMI, kg/m 24.1 ± 2.9 24.7 ± 3.2 <.0001 dicating the diagnostic power of each model for inci- dent T2DM during the follow-up period [28]. WC, cm 83.3 ± 7.6 81.2 ± 9.5 <.0001 Differences in AROC between the basic model and TG, mg/dl 170.8 ± 111.6 140.9 ± 75.6 <.0001 each composite model were analyzed using the TyG index 8.7 ± 0.5 8.5 ± 0.5 <.0001 method of DeLong et al. [29]. Pencina et al. have LAP 38.1 ± 34.0 39.0 ± 30.6 0.1925 suggested category-based net reclassification im- SBP, mmHg 121.2 ± 16.6 119.9 ± 19.1 0.0029 provement (NRI) and integrated discrimination im- DBP, mmHg 81.6 ± 10.8 78.5 ± 11.6 <.0001 provement (IDI) for calculating the usefulness of a new marker in prediction models [30]. The FPG, mg/dl 84.5 ± 9.0 81.1 ± 7.7 <.0001 category-based NRI measures the accuracy of reclas- 2hPG, mg/dl 110.5 ± 32.0 117.2 ± 28.3 <.0001 sification based on how well the subjects are reclas- Total cholesterol, mg/dl 190.3 ± 34.6 188.9 ± 33.9 0.0670 sified as upwards for events and downwards for HDL-C, mg/dl 43.8 ± 10.0 46.0 ± 10.0 <.0001 non-events. However, the category-based NRI can be LDL-C, mg/dl 112.4 ± 34.2 114.7 ± 30.7 0.0017 affected by the number and choice of categories HbA , mg/dl 5.6 ± 0.3 5.5 ± 0.4 0.1636 [31]. The continuous (category-free) NRI (cNRI) is an ex- 1c panded method to solve limitation of the categories. They P values are from t-tests or chi-square tests for analysis of variance for continuous variables and categorical variables also proposed the IDI that calculates the extent of average Abbreviations: BMI body mass index, WC waist circumference, TG triglycerides; sensitivity and ‘1-specificity’ when a new marker is added TyG index, the product of fasting glucose and triglycerides; LAP lipid accumulation product, SBP systolic blood pressure, DBP diastolic blood to the basic model [30]. We calculated the cNRI and IDI pressure, FPG fasting plasma glucose, HDL-C high-density lipoprotein to compare the prediction and reclassification abilities of cholesterol, LDL-C low density lipoprotein cholesterol, HbA 1c each measure when added to the basic model of diabetes. glycated hemoglobin Lee et al. BMC Endocrine Disorders (2018) 18:33 Page 4 of 10 Incidence of T2DM according to each index category 0.599, 0.623, and 0.644 in women respectively. The During the 10 year follow-up, the overall cumula- cut-off points for predicting T2DM were 84.00 cm, tive incidence of T2DM was 21.1% (766 cases) in 172.00 mg/dl, 87.00 mg/dl, 30.50, and 8.86 in men menand 19.6% (797cases)inwomen.Table 2 and 78.17 cm, 122.00 mg/dl, 84.00 mg/dl, 35.84, and shows the overall cumulative incidence of T2DM, 8.52 in women for WC, TG, FPG, LAP, and TyG categorized by quartiles for each index. In men, the index, respectively. cumulative incidences of T2DM across the quar- tiles of TyG index were (lowest-highest) 13.3 and Odds ratios for incident T2DM for each composite 31.5% and those of FPG were 12.9 and 35.9% and predictive model those of WC were 16.4 and 27.7%. In women, the Table 4 shows the odds ratio (OR) for incident values for TyG index were 11.1 and 30.9% and T2DM in higher quartiles compared to the first those FPG were 13.9 and 28.4% and those WC quartile of each index. The unadjusted ORs for all were 13.4 and 24.5%. The increase in the cumula- TG-related indices for incident T2DM were higher tive incidence of T2DM with higher category of than that of WC. These trends were similar after ad- WC was less marked than that with increasing TyG justment for age, BMI, hypertensive status, family index or FPG. history of diabetes, smoking, alcohol consumption status and regular exercise. When the highest quar- Cut-off points of each index for predicting T2DM tile for each index was compared to the lowest, the Table 3 shows the AROC values and cut-off points of adjusted ORs of WC, TG, FPG, LAP, and TyG index the indices for predicting T2DM. The AROCs for were 1.64 (95% confidence interval (CI), 1.13–2.38), WC, TG, FPG, LAP, and TyG index were 0.579, 2.03 (1.59–2.61), 3.85 (2.99–4.97), 2.47 (1.82–3.34), 0.592, 0.660, 0.602, and 0.623 in men 0.576, 0.627, and 2.79 (2.16–3.60) in men, and 1.17 (0.83–1.65), Table 2 Number of incident type 2 diabetes cases according to the quartiles for each measure Categories Men (n = 3636) Women (n = 4072) Quartile Diabetes (%) Quartile Diabetes (%) WC (cm) Q1 (< 78.0) 143 (16.4) Q1 (< 74.0) 128 (13.4) Q2 (78.0–83.2) 162 (17.6) Q2 (74.0–80.6) 198 (18.1) Q3 (83.3–88.2) 207 (22.3) Q3 (80.7–87.8) 222 (22.2) Q4 (≥88.3) 254 (27.7) Q4 (≥87.9) 249 (24.5) TG (mg/dl) Q1 (< 106.0) 137 (15.2) Q1 (< 92.5) 126 (12.4) Q2 (106.0–142.9) 167 (18.4) Q2 (92.5–121.9) 136 (13.6) Q3 (143.0–200.9) 196 (21.5) Q3 (122.0–165.9) 225 (21.9) Q4 (≥201.0) 266 (29.0) Q4 (≥166.0) 310 (30.3) FPG (mg/dl) Q1 (< 78.0) 106 (12.9) Q1 (< 76.0) 133 (13.9) Q2 (78.0–82.9) 132 (14.7) Q2 (76.0–79.9) 143 (16.3) Q3 (83.0–89.9) 190 (19.5) Q3 (80.0–84.9) 206 (18.3) Q4 (≥90.0) 338 (35.9) Q4 (≥85.0) 315 (28.4) LAP Q1 (< 16.7) 137 (15.1) Q1 (< 18.8) 126 (12.4) Q2 (16.7–29.4) 140 (15.4) Q2 (18.8–30.7) 167 (16.4) Q3 (29.5–49.1) 212 (23.3) Q3 (30.8–50.1) 190 (18.7) Q4 (≥49.2) 277 (30.5) Q4 (≥50.2) 314 (30.8) TyG index Q1 (< 8.4) 121 (13.3) Q1 (< 8.2) 113 (11.1) Q2 (8.4–8.6) 153 (16.8) Q2 (8.2–8.4) 140 (13.8) Q3 (8.7–9.0) 206 (22.7) Q3 (8.5–8.7) 229 (22.5) Q4 (≥9.1) 286 (31.5) Q4 (≥8.8) 315 (30.9) Abbreviations: WC waist circumference, TG triglycerides, FPG fasting plasma glucose, LAP lipid accumulation product, TyG index the product of fasting glucose and triglycerides Lee et al. BMC Endocrine Disorders (2018) 18:33 Page 5 of 10 Table 3 The area under the ROC curve (AROC) and cut-off points for indices to predict type 2 diabetes Index AROC (95% CI) Cut-off point Sensitivity (%) Specificity (%) Youden index Men WC (cm) 0.579 (0.563–0.595) 84.00 56.01 57.11 0.13 TG (mg/dl) 0.592 (0.576–0.608) 172.00 46.74 67.84 0.15 FPG (mg/dl) 0.660 (0.645–0.676) 87.00 51.31 72.89 0.24 LAP 0.602 (0.586–0.618) 30.50 62.79 55.54 0.18 TyG index 0.623 (0.607–0.638) 8.86 52.09 66.59 0.19 Women WC (cm) 0.576 (0.561–0.592) 78.17 69.26 43.54 0.13 TG (mg/dl) 0.627 (0.612–0.642) 122.00 66.37 54.41 0.21 FPG (mg/dl) 0.599 (0.584–0.614) 84.00 39.52 75.69 0.15 LAP 0.623 (0.607–0.637) 35.84 57.59 61.59 0.19 TyG index 0.644 (0.629–0.659) 8.52 67.25 55.85 0.23 Abbreviations: AROC area under the receiver operating characteristic curve, WC waist circumference, TG triglycerides, FPG fasting plasma glucose, LAP lipid accumulation product, TyG index, the product of fasting glucose and triglycerides 2.42 (1.90–3.08), 2.15 (1.71–2.71), 2.44 (1.82–3.26), practice because of measurement inaccuracy [34]. and 2.85 (2.22–3.66) in women, respectively. The Among the TG-related indices analyzed in the calculated VIF for all covariates in the multivariate present study, the AROC and reclassification ability model, were below 5.0, indicating no severe multi- of TyG index were higher than for TG or LAP. collinearity among covariates [33]. Although the inclusion of LAP also improved the predictive ability of the risk model for incident dia- Effects of the addition of each index to the basic model betes, this required the additional inclusion of WC, of T2DM on cNRI, IDI, and AROC while this was not required to demonstrate im- Table 5 shows the reclassification and discrimination provements in the risk model by the inclusion of abilities when each measure was added to the basic TG or TyG index [20]. Most studies that used TyG model of diabetes. The AROCs for the addition of index to predict incident T2DM used it as a surro- TyG index or FPG to the basic model were larger gate for insulin resistance, and reported that its than those for the other indices in both sexes. The predictive ability is better than that of the homeo- cNRI for TG, FPG, LAP, and TyG index were 22.7% stasis model of assessment (HOMA-IR) [19, 35, 36]. (P < 0.0001), 48.0% (P < 0.0001), 23.6% (P < 0.0001), However, Abbasi and Reaven showed that the cor- and 38.7% (P < 0.0001) in men, and 28.6% (P < relation between insulin-mediated glucose uptake 0.0001), 21.3% (P < 0.0001), 21.3% (P < 0.0001), and (IMGU) and TyG index is not better than that be- 36.0% (P < 0.0001) in women, respectively. By con- tween IMGU and TG or HOMA-IR [37, 38]. Never- trast, the addition of WC made cNRI only 5.6% (P = theless, it was noted that TyG index is a practical 0.1689), in men and − 2.3% (P = 0.5666), in women. measurefor useinT2DMpredictionbecause of its The IDI was also higher when TG-related indices or cost-efficiency [37]. Moreover, the use of HbA 1c FPG was added than when WC was added to the has been recommended for diagnosis and screening basic model of T2DM in both sexes. of patients. Lim et al. added HbA to a diabetes 1c risk model and demonstrated an increase in pre- Discussion dictive ability for T2DM in a middle-aged Korean We showed that the predictive ability of the simple cohort, while Ahn et al. also demonstrated through T2DM risk model was increased when TG-related longitudinal validation analysis that adding FPG or indices or FPG was added. Recently, several dia- HbA to the simple diabetes risk model also im- 1c betes risk models have been proposed to screen proves its predictive ability [11, 22]. Thus, in gen- high-risk groups, some of which included blood pa- eral, the predictive ability of a diabetes risk model rameters. WC was included as one of the compo- that includes laboratory parameters such as TG, nents in the model because increased WC increases FPG, or HbA is better than that of the simple 1c T2DM risk [8]. However, WC is not easily used in diabetes risk model [11, 14, 22]. Lee et al. BMC Endocrine Disorders (2018) 18:33 Page 6 of 10 Table 4 Multiple logistic regression analyses for each measure in predicting type 2 diabetes (odds ratios and 95% confidence intervals) a) Categories Unadjusted Adjusted WC TG FPG LAP TyG index WC TG FPG LAP TyG index ORs (95% CI) ORs (95% CI) ORs (95% CI) ORs (95% CI) ORs (95% CI) ORs (95% CI) ORs (95% CI) ORs (95% CI) ORs (95% CI) ORs (95% CI) Men Q1 Ref. Ref. Ref. Ref. Ref. Ref. Ref. Ref. Ref. Ref. Q2 1.09 (0.85–1.39) 1.26 (0.99–1.62) 1.16 (0.88–1.53) 1.03 (0.79–1.33) 1.32 (1.02–1.70) 1.07 (0.81–1.40) 1.21 (0.94–1.55) 1.18 (0.89–1.56) 1.04 (0.79–1.36) 1.26 (0.97–1.64) Q3 1.46 (1.15–1.85) 1.54 (1.21–1.95) 1.64 (1.27–2.12) 1.71 (1.35–2.18) 1.91 (1.49–2.45) 1.35 (1.00–1.83) 1.39 (1.08–1.79) 1.68 (1.29–2.19) 1.70 (1.28–2.25) 1.82 (1.41–2.36) Q4 1.95 (1.55–2.46) 2.29 (1.82–2.88) 3.78 (2.96–4.82) 2.47 (1.96–3.11) 2.99 (2.36–3.79) 1.64 (1.13–2.38) 2.03 (1.59–2.61) 3.85 (2.99–4.97) 2.47 (1.82–3.34) 2.79 (2.16–3.60) Women Q1 Ref. Ref. Ref. Ref. Ref. Ref. Ref. Ref. Ref. Ref. Q2 1.43 (1.12–1.82) 1.11 (0.86–1.44) 1.20 (0.93–1.55) 1.39 (1.08–1.78) 1.28 (0.98–1.66) 1.14 (0.88–1.48) 1.02 (0.78–1.33) 1.18 (0.91–1.53) 1.26 (0.97–1.64) 1.19 (0.91–1.55) Q3 1.86 (1.46–2.36) 1.98 (1.56–2.51) 1.38 (1.09–1.75) 1.63 (1.27–2.07) 2.32 (1.82–2.97) 1.27 (0.95–1.69) 1.69 (1.32–2.16) 1.32 (1.04–1.68) 1.35 (1.03–1.78) 1.97 (1.53–2.53) Q4 2.10 (1.66–2.66) 3.07 (2.44–3.87) 2.45 (1.95–3.06) 3.15 (2.51–3.97) 3.59 (2.83–4.55) 1.17 (0.83–1.65) 2.42 (1.90–3.08) 2.15 (1.71–2.71) 2.44 (1.82–3.26) 2.85 (2.22–3.66) a) Adjusted for age, body mass index, status of hypertension, family history of diabetes, smoking status, alcohol consumption, and regular exercise Abbreviations: ORs odds ratios, WC waist circumference, TG triglycerides, FPG fasting plasma glucose, LAP lipid accumulation product, TyG index, the product of fasting glucose and triglycerides Lee et al. BMC Endocrine Disorders (2018) 18:33 Page 7 of 10 Table 5 Reclassification and discrimination results associated with the risk prediction of incident type 2 diabetes according to each measure Parameter Men Women AROC p-value* cNRI p-value IDI p-value AROC p-value* cNRI p-value IDI p-value a) Basic model 0.615 0.621 Basic model + WC 0.619 0.1176 0.056 0.1689 0.002 0.0140 0.621 0.3233 −0.023 0.5666 0.000 1.0000 Basic model + TG 0.632 <.0001 0.227 <.0001 0.007 <.0001 0.652 <.0001 0.286 <.0001 0.019 <.0001 Basic model + FPG 0.692 <.0001 0.480 <.0001 0.064 <.0001 0.652 <.0001 0.214 <.0001 0.018 <.0001 Basic model + LAP 0.634 <.0001 0.236 <.0001 0.007 <.0001 0.644 <.0001 0.213 <.0001 0.016 <.0001 Basic model + TyG index 0.656 <.0001 0.387 <.0001 0.023 <.0001 0.666 <.0001 0.360 <.0001 0.029 <.0001 a) Basic model: age, body mass index, status of hypertension, family history of diabetes, smoking status, alcohol consumption, and regular exercise *P-value for AROC means vs. Basic model Abbreviations: AROC area under the receiver operating characteristic curve, WC waist circumference, TG triglycerides, LAP lipid accumulation product, TyG index the product of fasting glucose and triglycerides, cNRI continuous net reclassification improvement, IDI Integrated Discrimination Improvement The advantages of the simple diabetes risk model universe coverage, the health promotion that offered are that it is inexpensive and patients can use it for the general health check-ups for 17.6 million Koreans self-assessment [8]. Weighed against the improved in 2016, and its following examination rate (77.7%), predictive accuracy obtained by the addition of HbA there should be less burden caused by blood tests 1c and the Oral Glucose Tolerance Test (OGTT) to the than other countries [43]. Therefore, TyG index is T2DM risk model, there are substantial practical con- recommended as a screening tool for the prediction straints on screening the whole population using such of T2DM. Although TyG index predicts incident measurements [22]. As an effective alternative, a T2DM well, its usefulness is inconsistent, when com- two-pronged approach to screening has been pro- pared to fasting glucose [38, 44]. For predicting posed [22, 39]. This approach consists of using the T2DM risk, TyG index was not better than FPG or non-laboratory score for the general population and OGTT in Isfahan Diabetes Prevention Study [38]. using the laboratory score for patients in a higher-risk Wang et al. reported that TyG index, LAP, and vis- group for T2DM. For screening as part of the Korean ceral adiposity were not superior to FPG or WC NHI program, TG-related indices or FPG are more alone as diabetes predictors among Chinese [44]. In suitable as one of the components of the T2DM risk the Vascular-Metabolic CUN cohort, Navarro-Gonzalez et model than OGTT or HbA , given that these mea- al. compared the prediction ability of TyG index and FPG 1c surements are not currently included in primary for onset T2DM [35]. Association between indexes and screening measurements in Korea [23]. To the best of their discrimination for onset T2DM were different de- our knowledge, there is no TyG index criterion yet. pending on the fasting glucose subgroup. When the On the other hand, several studies conducted in highest quartile for each index was compared to the Europe and Asian have shown that the risk of inci- lowest, the hazard ratios (HRs) of TyG index and dent T2DM was increased with increasing TyG index FPG were 3.0 and 7.3 in the impaired fasting glucose [40–42]. Some researchers have proposed a cut-off group. On the other hand, TyG index showed stron- value of TyG index of 8.8 for incident T2DM and in- ger association with onset DM than FPG in the nor- sulin resistance [41, 42]. The subjects in the present mal fasting glucose group (HRs: 6.8 vs. 4.6). The study were classified by the cut-off value of TyG discrimination of TyG index for onset T2DM was index of 8.8. The adjusted odds ratio (95% CI) of in- also better than that of FPG in the normal fasting cident T2DM in the subjects with TyG index ≥8.8 glucose group (AROC: 0.75 vs. 0.66). In the present was 1.95 (1.73–2.20) compared to the counterparts. study, we found the association between some meta- We also have proposed sex-specific cut-off points of bolic syndrome (MetS) components and incident TyG index (≥ 8.86 in men, ≥ 8.52 in women) as pre- T2DM (Appendix 2). Therefore, we compared the dictors of T2DM based on the maximized Youden discrimination of each index for incident T2DM in index. Compared to the counterparts, the adjusted the stratified MetS components (Appendix 3). In the odds ratio (95% CI) of incident T2DM in the men elevated FPG group, the discrimination of FPG with TyG index ≥8.86 and in the women with TyG (AROC: 0.506) for incident T2DM was inferior to index ≥8.52 were 2.01 (1.69–2.39) and 2.16 (1.82– that of other indices. On the other hand, TyG index 2.56), respectively (Appendix 1). Considering NIH’s was a more robust discrimination index than other Lee et al. BMC Endocrine Disorders (2018) 18:33 Page 8 of 10 indices in the group stratified by sex and MetS com- Appendix 2 ponents. Our findings indicate that TyG index is not Table 7 Multiple logistic regression analyses for each MetS only a better predictor for incident diabetes than WC, component in predicting type 2 diabetes (odds ratios and LAP, and TG, but it also has a better reclassification 95% confidence intervals) ability. On the other hand, association between FPG Components Men Women a) of MetS h) h) and its(their) reclassification ability for incident Unadjusted Adjusted Unadjusted Adjusted T2DM were different depending on sex. The present ORs (95% CI) ORs (95% CI) ORs (95% CI) ORs (95% CI) study used community-based long-term prospective Central 1.71 (1.42–2.06) 1.35 (1.10–1.65) 1.56 (1.33–1.83) 1.16 (0.97–1.38) b) obesity cohort data. Therefore, the temporal relationship be- c) Elevated TG 1.70 (1.45–2.00) 1.57 (1.31–1.87) 2.26 (1.93–2.64) 1.90 (1.60–2.26) tween each measure and incident diabetes is clear. d) Low HDL-C 1.14 (0.96–1.34) 1.01 (0.84–1.22) 1.53 (1.28–1.83) 1.21 (1.00–1.47) Our findings indicate that TyG index is not only a better predictor for incident diabetes than WC, LAP, Elevated 5.43 (4.09–7.21) 5.16 (3.85–6.92) 3.65 (2.39–5.57) 3.55 (2.29–5.50) e) FPG and TG, but it also has a better reclassification abil- f) Elevated BP 1.79 (1.52–2.10) 1.47 (1.24–1.75) 1.65 (1.41–1.93) 1.29 (1.09–1.54) ity. It was also noted that association between FPG g) i) i) MetS 2.22 (1.86–2.66) 2.24 (1.87–2.69) 2.22 (1.88–2.61) 2.03 (1.70–2.41) and its reclassification ability for incident T2DM were a) b) Modified NCEP-ATP III criteria [47] with the Korean cut-off for WC, WC ≥ different depending on sex. There were, however, a c) d) 90 cm for men ≥85 for women, Lee et al. [48], TG ≥ 150 mg/dl, HDL-C < few limitations to this study. Firstly, even though e) f) 40 mg/dl for men < 50 mg/dl for women, FPG ≥ 100 mg/dl, Systolic blood g) KoGES is designed to include subjects who live both pressure ≥ 130 mmHg or diastolic blood pressure ≥ 85 mmHg, Number of h) MetS components ≥3, Adjusted for other components of MetS, age, in rural and urban areas, the data are not representa- family history of diabetes, smoking status, alcohol consumption, and i) tive of the entire Korean population [45]. Secondly, regular exercise, Adjusted for age, family history of diabetes, smoking status, alcohol consumption, and regular exercise dietary intake was not included in the analysis. More- Abbreviations: MetS Metabolic syndrome, ORs odds ratios, TG triglyceride, over, Baik et al. had reported that the usefulness of HDL-C high-density lipoprotein cholesterol, FPG fasting plasma glucose, BP blood pressure, WC, waist circumference dietary information in cardiovascular disease risk pre- diction models [46]. However, dietary information has been excluded from the questionnaire in medical check since 2009. Finally, our results were not vali- dated using a separate Korean dataset. Therefore, additional studies are required to validate our data. Conclusions 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 fast- ing glucose level, TyG index was a more robust pre- dictor for onset T2DM than other indexes. Therefore, TyG index can be a useful screening tool for incident T2DM in middle-aged Koreans. Appendix 1 Table 6 Multiple logistic regression analyses for cutoff-points of TyG index in predicting type 2 diabetes (odds ratios and 95% confidence intervals) Cutoff- Men Women points a) a) Unadjusted Adjusted Unadjusted Adjusted ORs (95% CI) ORs (95% CI) ORs (95% CI) ORs (95% CI) TyG index 2.05 (1.75–2.41) 1.93 (1.62–2.29) 2.41 (2.05–2.83) 2.03 (1.71–2.41) b) ≥8.8 TyG index 2.13 (1.82–2.51) 2.01 (1.69–2.39) 2.57 (2.18–3.02) 2.16 (1.82–2.56) c) ≥8.86/8.52 a) Adjusted for age, body mass index, status of hypertension, family history of b) diabetes, smoking status, alcohol consumption, and regular exercise, TyG c) index ≥8.8 cm, Lee et al. [42], TyG index ≥8.86 cm for men ≥8.52 for women in the present study Abbreviations: ORs odds ratios; TyG index, the product of fasting glucose and triglycerides Lee et al. BMC Endocrine Disorders (2018) 18:33 Page 9 of 10 Appendix 3 Table 8 The area under the ROC curve (AROC) for indices to predict type 2 diabetes stratified by MetS components a) b) c) d) e) f) MetS components Central obesity Elevated TG Low HDL-C Elevated FPG Elevated BP Subgroup No Yes No Yes No Yes No Yes No Yes Index AROC AROC AROC AROC AROC AROC AROC AROC AROC AROC Men (n = 2929) (n = 707) (n = 1970) (n = 1666) (n = 2326) (n = 1310) (n = 3423) (n = 213) (n = 2092) (n = 1544) WC 0.553 0.532 0.535 0.569 0.570 0.584 0.571 0.573 0.565 0.559 TG 0.589 0.534 0.546 0.559 0.598 0.574 0.588 0.591 0.572 0.588 FPG 0.648 0.680 0.637 0.676 0.661 0.666 0.621 0.506 0.649 0.661 LAP 0.584 0.537 0.546 0.582 0.598 0.599 0.595 0.604 0.582 0.591 TyG index 0.618 0.569 0.605 0.607 0.631 0.605 0.607 0.586 0.602 0.620 Women (n = 2680) (n = 1392) (n = 2754) (n = 1318) (n = 1276) (n = 2796) (n = 3983) (n = 89) (n = 2639) (n = 1433) WC 0.556 0.519 0.536 0.564 0.520 0.583 0.576 0.506 0.570 0.545 TG 0.610 0.626 0.567 0.559 0.611 0.622 0.629 0.596 0.614 0.621 FPG 0.590 0.598 0.581 0.617 0.550 0.616 0.586 0.566 0.598 0.587 LAP 0.601 0.625 0.553 0.590 0.565 0.630 0.623 0.570 0.608 0.609 TyG index 0.628 0.643 0.595 0.596 0.621 0.643 0.641 0.597 0.633 0.634 a) b) c) d) Modified NCEP-ATP III criteria [47] with the Korean cut-off for WC, WC ≥ 90 cm for men ≥85 for women, Lee et al. [48], TG ≥ 150 mg/dl, HDL-C < 40 mg/dl for e) f) men < 50 mg/dl for women, FPG ≥ 100 mg/dl, Systolic blood pressure ≥ 130 mmHg or diastolic blood pressure ≥ 85 mmHg Abbreviations: MetS Metabolic syndrome, TG triglyceride, HDL-C high-density lipoprotein cholesterol, FPG fasting plasma glucose, BP blood pressure, AROC area under the receiver operating characteristic curve, WC waist circumference, LAP lipid accumulation product, TyG index, the product of fasting glucose and triglycerides Abbreviations Ethics approval and consent to participate AROC: Area under the receiver operating characteristic curve; BMI: Body mass The Institutional Review Board of the Korean Centers for Disease Control and index; CI: Confidence interval; cNRI: Continuous net reclassification Prevention approved the KoGES protocol. Written informed consent was improvement; FPG: Fasting plasma glucose; HbA :Glycatedhemoglobin; obtained from all subjects. 1c HRs: Hazard ratios; IDI: Integrated discrimination improvement; IMGU: Insulin- mediated glucose uptake; KNHANES: Korea National Health and Nutrition Competing interests Examination; KNIH: Korea National Institute of Health; KoGES: Korean Genome The authors declare that they have no competing interest. and Epidemiology Study; LAP: Lipid accumulation product; NHI: National Health Insurance; OGTT: Oral Glucose Tolerance Test; ORs: Odds ratios; T2DM: Type 2 diabetes mellitus; TG: Triglyceride; TyG index: Product of fasting glucose and Publisher’sNote triglyceride; VIF: Variable inflation factor; WC: Waist circumference Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Acknowledgements Epidemiologic data used in this study were from the Korean Genome and Author details Epidemiology Study (KoGES) of the Korea Centers for Disease Control & Division of Cardiovascular Diseases, Center for Biomedical Sciences, Korea Prevention, Republic of Korea. National Institute of Health, 187 Osongsaengmyeng 2-ro, Osong-eup, Heungdeok-gu, Cheongju-si, Chungcheongbuk-do 361-951, South Korea. Funding Department of Public Health Sciences, Graduate School, Korea University, This study was supported by an intramural grant of the Korea National Seoul, South Korea. Institute of Health, Korea 4800–4845-302 (2017-NI63001–00). The funders had no role in the design of the study and collection, analysis, and interpretation Received: 18 July 2017 Accepted: 16 May 2018 of data and in writing the manuscript. Availability of data and materials References The information of KoGES can be obtained at [http://www.nih.go.kr/NIH/ 1. Esteghamati A, Gouya MM, Abbasi M, Delavari A, Alikhani S, Alaedini F, eng/main.jsp > Research infrastructure > KoGES]. The detailed cohort profile Safaie A, Forouzanfar M, Gregg EW. Prevalence of diabetes and impaired and how to access the data are described in the following source: KIM YJ, fasting glucose in the adult population of Iran: National Survey of risk Han BG, KoGES group. Cohort profile: the Korean genome and epidemiology factors for non-communicable diseases of Iran. Diabetes Care. 2008; study (KoGES) Consortium. International journal of epidemiology, 2016, 46.2: 31(1):96–8. e20-e20. The data that support the findings of this study are available from the 2. Kim DJ. The epidemiology of diabetes in Korea. Diabetes Metab J. 2011; Korea National Institute of Health (KNIH) but restrictions apply to the availability 35(4):303–8. of these data. Data can be accessible upon reasonable request and with ap- 3. Ministry of Health and Welfare of Korea KCfDCaP. Korea health statistics proval of a designated research proposal review committee of the KNIH. 2011: Korea National Health and Nutrition Examination Survey (KNHANES V- 2). Seoul: Ministry of Health and Welfare of Korea; 2012. Authors’ contributions 4. Ministry of Health and Welfare of Korea KCfDCaP. Korea health JWL carried out the data analysis and wrote the manuscript. NKL contributed statistics 2014: Korea National Health and Nutrition Examination to study design and advised statistical analyses. HYP contributed to study Survey (KNHANES VI-2). Sejong: Ministry of Health and Welfare of design and critically reviewed the paper. All authors read and approved the Korea; 2015. final manuscript. 5. Federation ID. IDF Diabetes Atlas. 7th ed; 2015. Lee et al. BMC Endocrine Disorders (2018) 18:33 Page 10 of 10 6. Li G, Zhang P, Wang J, An Y, Gong Q, Gregg EW, Yang W, Zhang B, Shuai Y, 30. Pencina MJ, D'Agostino RB, D'Agostino RB, Vasan RS. Evaluating the added Hong J. Cardiovascular mortality, all-cause mortality, and diabetes incidence predictive ability of a new marker: from area under the ROC curve to after lifestyle intervention for people with impaired glucose tolerance in the reclassification and beyond. Stat Med. 2008;27(2):157. Da Qing diabetes prevention study: a 23-year follow-up study. Lancet 31. Pencina MJ, D'Agostino RB, Steyerberg EW. Extensions of net reclassification Diabetes Endocrinol. 2014;2(6):474–80. improvement calculations to measure usefulness of new biomarkers. Stat 7. Tuomilehto J, Schwarz P, Lindström J. Long-term benefits from lifestyle Med. 2011;30(1):11–21. interventions for type 2 diabetes prevention time to expand the efforts. 32. Kennedy K, Pencina M. A SAS macro to compute added predictive ability of Diabetes Care. 2011;34(Supplement 2):S210–4. new markers predicting a dichotomous outcome. In: SouthEeast SAS Users Group Annual Meeting Proceedings: 2010; 2010. 8. Noble D, Mathur R, Dent T, Meads C, Greenhalgh T. Risk models and scores 33. Vatcheva KP, Lee M, McCormick JB, Rahbar MH. Multicollinearity in for type 2 diabetes: systematic review. Bmj. 2011;343:d7163. regression analyses conducted in epidemiologic studies. Epidemiol. 2016; 9. Chien K, Cai T, Hsu H, Su T, Chang W, Chen M, Lee Y, Hu F. A prediction 6(227). https://doi.org/10.4172/2161-1165.1000227. https://www.omicsonline. model for type 2 diabetes risk among Chinese people. Diabetologia. 2009; org/open-access/multicollinearity-in-regression-analyses-conducted-in- 52(3):443–50. inepidemiologic-studies-2161-1165-1000227.php?aid=69442. 10. Lee Y-H, Bang H, Kim HC, Kim HM, Park SW, Kim DJ. A simple screening 34. Sebo P, Beer-Borst S, Haller DM, Bovier PA. Reliability of doctors' score for diabetes for the Korean population development, validation, and anthropometric measurements to detect obesity. Prev Med. 2008;47(4): comparison with other scores. Diabetes Care. 2012;35(8):1723–30. 389–93. 11. Lim N-K, Park S-H, Choi S-J, Lee K-S, Park H-Y. A risk score for predicting the 35. Navarro-González D, Sánchez-Íñigo L, Pastrana-Delgado J, Fernández- incidence of type 2 diabetes in a middle-aged Korean cohort. Circ J. 2012; Montero A, Martinez JA. Triglyceride–glucose index (TyG index) in 76(8):1904–10. comparison with fasting plasma glucose improved diabetes prediction in 12. Lindström J, Tuomilehto J. The diabetes risk score. Diabetes Care. 2003;26(3): patients with normal fasting glucose: the vascular-metabolic CUN cohort. 725–31. Prev Med. 2016;86:99–105. 13. Mann DM, Bertoni AG, Shimbo D, Carnethon MR, Chen H, Jenny NS, 36. Vasques ACJ, Novaes FS, MdS d O, JRM S, Yamanaka A, Pareja JC, Tambascia Muntner P. Comparative validity of 3 diabetes mellitus risk prediction MA, MJA S, Geloneze B. TyG index performs better than HOMA in a Brazilian scoring models in a multiethnic US cohort the multi-ethnic study of population: a hyperglycemic clamp validated study. Diabetes Res Clin Pract. atherosclerosis. Am J Epidemiol. 2010;171(9):980–8. 2011;93(3):e98–e100. 14. Wilson PW, Meigs JB, Sullivan L, Fox CS, Nathan DM, D’Agostino RB. 37. Abbasi F, Reaven G. Statin-induced diabetes: how important is insulin Prediction of incident diabetes mellitus in middle-aged adults: the resistance? J Intern Med. 2015;277(4):498–500. Framingham offspring study. Arch Intern Med. 2007;167(10):1068–74. 38. Janghorbani M, Almasi SZ, Amini M. The product of triglycerides and 15. Consultation WE: Waist circumference and waist-hip Ratio 2011. glucose in comparison with fasting plasma glucose did not improve 16. Ko G, Chan J, Woo J, Lau E, Yeung V, Chow C, Wai H, Li J, So W, Cockram C: diabetes prediction. Acta Diabetol. 2015:1–8. Simple anthropometric indexes and cardiovascular risk factors in Chinese. 39. Wannamethee S, Papacosta O, Whincup P, Thomas M, Carson C, Lawlor D, Int J Obes Relat Metab Disord 1997, 21(11):995–1001. Ebrahim S, Sattar N. The potential for a two-stage diabetes risk algorithm 17. Janghorbani M, Amini M. Normal fasting plasma glucose and risk of combining non-laboratory-based scores with subsequent routine non- prediabetes and type 2 diabetes: the Isfahan diabetes prevention study. fasting blood tests: results from prospective studies in older men and Rev Diabet Stud. 2011;8(4):490. women. Diabet Med. 2011;28(1):23–30. 18. Zhang M, Gao Y, Chang H, Wang X, Liu D, Zhu Z, Huang G. 40. Zheng R, Mao Y. Triglyceride and glucose (TyG) index as a predictor of Hypertriglyceridemic-waist phenotype predicts diabetes: a cohort study in incident hypertension: a 9-year longitudinal population-based study. Lipids Chinese urban adults. BMC Public Health. 2012;12(1):1081. Health Dis. 2017;16(1):175. 19. Lee S-H, Kwon H-S, Park Y-M, Ha H-S, Jeong SH, Yang HK, Lee J-H, Yim H-W, 41. Navarro-González D, Sánchez-Íñigo L, Fernández-Montero A, Pastrana- Kang M-I, Lee W-C. Predicting the development of diabetes using the Delgado J, Martinez JA. TyG index change is more determinant for product of triglycerides and glucose: the Chungju metabolic disease cohort forecasting type 2 diabetes onset than weight gain. Medicine. 2016;95(19) (CMC) study. PLoS One. 2014;9(2):e90430. 42. Lee DY, Lee ES, Kim JH, Park SE, Park C-Y, Oh K-W, Park S-W, Rhee E-J, Lee 20. Kahn HS. The lipid accumulation product is better than BMI for identifying W-Y. Predictive value of triglyceride glucose index for the risk of incident diabetes a population-based comparison. Diabetes Care. 2006;29(1):151–3. diabetes: a 4-year retrospective longitudinal study. PLoS One. 2016;11(9): 21. Song YJ. The south Korean health care system. JMAJ. 2009;52(3):206–9. e0163465. 22. Ahn CH, Yoon JW, Hahn S, Moon MK, Park KS, Cho YM. Evaluation of non- 43. National Health Insurance Service. 2016 National health screening statistical laboratory and laboratory prediction models for current and future diabetes yearbook http://www.nhis.or.kr/menu/boardRetriveMenuSet.xx?menuId=F3328. mellitus: a cross-sectional and retrospective cohort study. PLoS One. 2016; 44. Wang B, Zhang M, Liu Y, Sun X, Zhang L, Wang C, Linlin L, Ren Y, Han C, 11(5):e0156155. Zhao Y. Utility of three novel insulin resistance-related lipid indexes for 23. Korean National Health Insurance Service: National Health Insurance predicting type 2 diabetes mellitus among people with normal fasting [http://www.nhis.or.kr/static/html/wbd/g/a/wbdga0606.html]. glucose in rural China. J Diab. 2018. https://www.ncbi.nlm.nih.gov/pubmed/ 24. Kim Y, Han B-G. Cohort profile: the Korean genome and epidemiology study (KoGES) consortium. Int J Epidemiol. 2016;46(2):e20–e20. 45. Shin C, Abbott R, Lee H, Kim J, Kimm K. Prevalence and correlates of 25. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of orthostatic hypotension in middle-aged men and women in Korea: the low-density lipoprotein cholesterol in plasma, without use of the Korean health and genome study. J Hum Hypertens. 2004;18(10):717–23. preparative ultracentrifuge. Clin Chem. 1972;18(6):499–502. 46. Baik I, Cho N, Kim S, Shin C. Dietary information improves cardiovascular 26. Association AD. Diagnosis and classification of diabetes mellitus. Diabetes disease risk prediction models. Eur J Clin Nutr. 2013;67(1):25. Care. 2010;33(Supplement 1):S62–9. 47. Third Report of the National Cholesterol Education Program. (NCEP) expert 27. Park SK, Ryoo J-H, Oh C-M, Choi J-M, Choi Y-J, Lee KO, Jung JY. The risk of panel on detection, evaluation, and treatment of high blood cholesterol in type 2 diabetes mellitus according to 2-hour plasma glucose level: the Korean adults (adult treatment panel III) final report. Circulation. 2002;106(25): genome and epidemiology study (KoGES). Diabetes Res Clin Pract. 2017. 3143–421. https://www.ncbi.nlm.nih.gov/pubmed/?term=The+risk+of+type+2+diabetes 48. Lee SY, Park HS, Kim DJ, Han JH, Kim SM, Cho GJ, Kim DY, Kwon HS, Kim SR, +mellitus+according+to+2-hour+plasma+glucose+level%3A+the+Korean Lee CB. Appropriate waist circumference cutoff points for central obesity in +genome+and+epidemiology+study. Korean adults. Diabetes Res Clin Pract. 2007;75(1):72–80. 28. Zweig MH, Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem. 1993;39(4):561–77. 29. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–45. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png BMC Endocrine Disorders Springer Journals

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

Free
10 pages

Loading next page...
 
/lp/springer_journal/the-product-of-fasting-plasma-glucose-and-triglycerides-improves-risk-zrKO0dARVV
Publisher
Springer Journals
Copyright
Copyright © 2018 by The Author(s).
Subject
Medicine & Public Health; Endocrinology; Metabolic Diseases; Diabetes; Andrology
eISSN
1472-6823
D.O.I.
10.1186/s12902-018-0259-x
Publisher site
See Article on Publisher Site

Abstract

Background: Screening for risk of type 2 diabetes mellitus (T2DM) is an important public health issue. Previous studies report that fasting plasma glucose (FPG) and triglyceride (TG)-related indices, such as lipid accumulation product (LAP) and the product of fasting glucose and triglyceride (TyG index), are associated with incident T2DM. We aimed to evaluate whether FPG or TG-related indices can improve the predictive ability of a diabetes risk model for middle-aged Koreans. Methods: 7708 Koreans aged 40–69 years without diabetes at baseline were eligible from the Korean Genome and Epidemiology Study. The overall cumulative incidence of T2DM was 21.1% (766 cases) in men and 19.6% (797 cases) in women. Therefore, the overall cumulative incidence of T2DM was 20.3% (1563 cases). Multiple logistic regression analysis was conducted to compare the odds ratios (ORs) for incident T2DM for each index. The area under the receiver operating characteristic curve (AROC), continuous net reclassification improvement (cNRI), and integrated discrimination improvement (IDI) were calculated when each measure was added to the basic risk model for diabetes. Results: All the TG-related indices and FPG were more strongly associated with incident T2DM than WC in our study population. The adjusted ORs for the highest quartiles of WC, TG, FPG, LAP, and TyG index compared to the lowest, were 1.64 (95% CI, 1.13–2.38), 2.03 (1.59–2.61), 3.85 (2.99–4.97), 2.47 (1.82–3.34), and 2.79 (2.16–3.60) in men, and 1.17 (0.83–1.65), 2.42 (1.90–3.08), 2.15 (1.71–2.71), 2.44 (1.82–3.26), and 2.85 (2.22–3.66) in women, respectively. The addition of TG-related parameters or FPG, but not WC, to the basic risk model for T2DM (including age, body mass index, family history of diabetes, hypertension, current smoking, current drinking, and regular exercise) significantly increased cNRI, IDI, and AROC in both sexes. Conclusions: Adding either TyG index or FPG into the basic risk model for T2DM increases its prediction and reclassification ability. Compared to FPG, TyG index was a more robust T2DM predictor in the stratified sex and fasting glucose level. Therefore, TyG index should be considered as a screening tool for identification of people at high risk for T2DM in practice. Keywords: TyG index, Type 2 diabetes mellitus, Risk model * Correspondence: mdhypark@gmail.com Division of Cardiovascular Diseases, Center for Biomedical Sciences, Korea National Institute of Health, 187 Osongsaengmyeng 2-ro, Osong-eup, Heungdeok-gu, Cheongju-si, Chungcheongbuk-do 361-951, South Korea Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Lee et al. BMC Endocrine Disorders (2018) 18:33 Page 2 of 10 Background the prediction of risk of future T2DM. To date, few Type 2 diabetes mellitus (T2DM) is one of the most studies have been undertaken in Korea that compare prevalent non-communicable diseases in the middle-aged thepredictiveability for incidentT2DM ofthe sim- population worldwide, largely because of recent ple model and composite models, which include changes in diet and lifestyle [1, 2]. In the Korean blood test results [11, 22]. Recently, a risk model for National Health and Nutrition Examination Surveys T2DM that included blood test results was proposed (KNHANES), the prevalence of diabetes among adults based on cohort data, and its reclassification ability aged 30 and over was found to have slightly in- was significantly improved when glycated creased, from 12.4% in 2011 to 13.0% in 2014 [3, 4]. hemoglobin (HbA ) was included [11]. However, 1c According to an estimate by the International Dia- HbA is not assessed in the routine health examin- 1c betes Federation, 46.3% of cases of diabetes in Ko- ation and this study did not consider reclassification reans aged 20–79 were undiagnosed in 2015 [5]. ability when other blood test results apart from However, lifestyle intervention can reduce the risk of HbA were included in the T2DM risk model [23]. 1c incident T2DM and mortality in individuals at high Therefore, in the present study, we aimed to iden- risk of diabetes [6, 7]. Therefore, it is important to tify which of the TG-related indices that can be de- screen high-risk individuals for T2DM regularly to rived from general check-up data would improve the ensure early diagnosis. For this reason, risk models prediction ability of the simple T2DM risk model in for diabetes have been proposed in previous studies, middle-aged Koreans. and, recently, T2DM risk prediction models have been reported in Korea [8–14]. Methods Obesity is the most significant risk factor for inci- Study population dent T2DM [15, 16]. Body mass index (BMI) has The Korean Genome and Epidemiology Study been used as a surrogate marker for obesity and in- (KoGES) consists of a gene-environment model and cluded as one of the variables in most risk models for population-based studies [24]. KoGES: Ansan and T2DM [9, 11–14]. However, BMI does not reflect Ansung study is an ongoing prospective cohort study central obesity. Lee et al. selected waist circumference conducted in urban (Ansan) and rural (Ansung) (WC) instead of BMI in their diabetes risk model areas in Korea with biennial follow-ups, which considering its association with diabetes [10]. In the started in 2001. 10038 people underwent an initial systematic review, more than 30% of the diabetes risk examination, and 9001 subjects were included after models stating its components included both BMI exclusion of 1037 who refused to participate or died. and WC [8]. To improve the prediction ability of a Thirty-five participants were not suitable for the risk model for incident T2DM, blood parameters are present study because of their age, and subjects with also frequently included [8]. a history of diabetes at baseline or incomplete data Also, the increase of fasting plasma glucose (FPG) were also excluded. Finally, 7708 people aged 40 to in the normal range is associated with increased in- 69 years remained eligible for the current study. cident T2DM [17]. Of these, serum triglyceride (TG) Written informed consent was obtained from all has been used to identify people at high risk for subjects. The Institutional Review Board of the Ko- T2DM, alongside obesity [18]. In addition, lipid ac- rean Centers for Disease Control and Prevention ap- cumulation product (LAP) and the product of fasting proved the study protocol. plasma glucose and triglyceride (TyG index), com- posite indices including TG, have been proposed as Measurements and surveys predictors of T2DM [19, 20]. In particular, TyG Height and weight were measured to the nearest index has been used as a marker of insulin resist- 0.1 cm and 0.1 kg using a digital stadiometer and a ance [19]. Although the simple diabetes risk model scale, respectively. Resting blood pressure while sit- is convenient for self-assessment, more accurate pre- ting was measured by trained technicians using a diction models that include blood parameters are standard mercury sphygmomanometer. Blood sam- also required to facilitate more accurate clinical con- ples were collected after fasting for at least 8 h. The sultations [8]. In Korea, most people are registered Friedewald formula was used to indirectly estimate with the National Health Insurance (NHI), which low-density lipoprotein cholesterol levels in subjects provides biannual medical check-ups for middle-aged with plasma TG < 400 mg/dl [25]. Diabetes was de- people, including the measurement of key blood pa- fined by FPG > 126 mg/dl, 2 h post-challenge plasma rameters [21]. Therefore, risk models for incident glucose > 200 mg/dl, HbA > 6.5%, or prescription 1c T2DM that are based on the data obtained from for anti-diabetic medication [26]. Subjects were these medical check-ups would be of great use for questioned by trained interviewers regarding their Lee et al. BMC Endocrine Disorders (2018) 18:33 Page 3 of 10 socio-demographics, family history of diabetes, and Macros were used to calculate cNRI and IDI, and data lifestyle factors including smoking and alcohol con- analysis was performed using SAS 9.4 and MedCalc [32]. sumption. The subjects’ smoking and alcohol con- sumption status was subdivided according to their Results past and present habits. Regular exercise was defined Baseline characteristics as subjects’ exercise was over 90 min as the sum of Table 1 indicates the baseline characteristics of the study moderate and vigorous physical activity a day [27]. subjects. The ages of the subjects were 51.4 ± 8.6 years LAP and TyG index were calculated as follows: for men and 52.0 ± 8.9 years for women. The prevalence of hypertension was higher in men than in women (29.5% vs. 27.0%, P = 0.0149). By contrast, the percentage LAPformen ¼½ WCðÞ cm ‐65 TGðÞ mmol=L of subjects with a family history of diabetes was higher LAPfor women ¼½ WCðÞ cm ‐58 TGðÞ mmol=L in women than in men (11.0% vs. 9.2%, P = 0.0067). TyGindex ¼ ln½ TGðÞ mg=dl FPGðÞ mg=dl =2 There were also significant differences in anthropomet- ric indices between sexes. Mean BMI and WC were 24.1 ± 2.9 kg/m and 83.3 ± 7.6 cm in men, and 24.7 ± 3.2 kg/ Statistical analysis m and 81.2 ± 9.5 cm in women, respectively. Mean TG, Data are expressed as numbers and proportions for FPG, LAP, and TyG index were 170.8 ± 111.6 mg/dl, discrete variables and mean ± SD for continuous vari- 84.5 ± 9.0 mg/dl, 38.1 ± 34.0, and 8.7 ± 0.5 in men, and ables, and were analyzed using Chi-square and Student’s 140.9 ± 75.6 mg/dl, 81.1 ± 7.7 mg/dl, 39.0 ± 30.6, and 8.5 t-tests, respectively. ± 0.5 in women, respectively. TG-related indices also Based on the maximized Youden index, we calcu- showed significant differences between the sexes, with lated sex-specific cut-off points of each index for the exception of LAP. T2DM.Multiplelogisticregressionanalysiswas con- Table 1 Characteristics of the study population at baseline ducted and adjusted for age, BMI, hypertensive sta- Variables Men Women P- tus, family history of diabetes, current smoking and value (n = 3636) (n = 4072) alcohol consumption status, and regular exercise. Age, years 51.4 ± 8.6 52.0 ± 8.9 0.0011 The basic model was derived from the published diabetes risk model for middle-aged Koreans and in- Current Smoker, n(%) 1753 (48.2) 142 (3.5) <.0001 cluded the variables listed above [10, 11]. We tested Current Drinker, n(%) 2580 (71.0) 1085 (26.7) <.0001 multicollinearity for all covariates based on the vari- Hypertension, n(%) 1074 (29.5) 1101 (27.0) 0.0149 able inflation factor (VIF). The area under the re- Family history of diabetes, n(%) 333 (9.2) 449 (11.0) 0.0067 ceiver operating characteristic curve (AROC) was Regular exercise, n(%) 1712 (47.1) 1654 (40.6) <.0001 calculated for each risk model of incident T2DM, in- BMI, kg/m 24.1 ± 2.9 24.7 ± 3.2 <.0001 dicating the diagnostic power of each model for inci- dent T2DM during the follow-up period [28]. WC, cm 83.3 ± 7.6 81.2 ± 9.5 <.0001 Differences in AROC between the basic model and TG, mg/dl 170.8 ± 111.6 140.9 ± 75.6 <.0001 each composite model were analyzed using the TyG index 8.7 ± 0.5 8.5 ± 0.5 <.0001 method of DeLong et al. [29]. Pencina et al. have LAP 38.1 ± 34.0 39.0 ± 30.6 0.1925 suggested category-based net reclassification im- SBP, mmHg 121.2 ± 16.6 119.9 ± 19.1 0.0029 provement (NRI) and integrated discrimination im- DBP, mmHg 81.6 ± 10.8 78.5 ± 11.6 <.0001 provement (IDI) for calculating the usefulness of a new marker in prediction models [30]. The FPG, mg/dl 84.5 ± 9.0 81.1 ± 7.7 <.0001 category-based NRI measures the accuracy of reclas- 2hPG, mg/dl 110.5 ± 32.0 117.2 ± 28.3 <.0001 sification based on how well the subjects are reclas- Total cholesterol, mg/dl 190.3 ± 34.6 188.9 ± 33.9 0.0670 sified as upwards for events and downwards for HDL-C, mg/dl 43.8 ± 10.0 46.0 ± 10.0 <.0001 non-events. However, the category-based NRI can be LDL-C, mg/dl 112.4 ± 34.2 114.7 ± 30.7 0.0017 affected by the number and choice of categories HbA , mg/dl 5.6 ± 0.3 5.5 ± 0.4 0.1636 [31]. The continuous (category-free) NRI (cNRI) is an ex- 1c panded method to solve limitation of the categories. They P values are from t-tests or chi-square tests for analysis of variance for continuous variables and categorical variables also proposed the IDI that calculates the extent of average Abbreviations: BMI body mass index, WC waist circumference, TG triglycerides; sensitivity and ‘1-specificity’ when a new marker is added TyG index, the product of fasting glucose and triglycerides; LAP lipid accumulation product, SBP systolic blood pressure, DBP diastolic blood to the basic model [30]. We calculated the cNRI and IDI pressure, FPG fasting plasma glucose, HDL-C high-density lipoprotein to compare the prediction and reclassification abilities of cholesterol, LDL-C low density lipoprotein cholesterol, HbA 1c each measure when added to the basic model of diabetes. glycated hemoglobin Lee et al. BMC Endocrine Disorders (2018) 18:33 Page 4 of 10 Incidence of T2DM according to each index category 0.599, 0.623, and 0.644 in women respectively. The During the 10 year follow-up, the overall cumula- cut-off points for predicting T2DM were 84.00 cm, tive incidence of T2DM was 21.1% (766 cases) in 172.00 mg/dl, 87.00 mg/dl, 30.50, and 8.86 in men menand 19.6% (797cases)inwomen.Table 2 and 78.17 cm, 122.00 mg/dl, 84.00 mg/dl, 35.84, and shows the overall cumulative incidence of T2DM, 8.52 in women for WC, TG, FPG, LAP, and TyG categorized by quartiles for each index. In men, the index, respectively. cumulative incidences of T2DM across the quar- tiles of TyG index were (lowest-highest) 13.3 and Odds ratios for incident T2DM for each composite 31.5% and those of FPG were 12.9 and 35.9% and predictive model those of WC were 16.4 and 27.7%. In women, the Table 4 shows the odds ratio (OR) for incident values for TyG index were 11.1 and 30.9% and T2DM in higher quartiles compared to the first those FPG were 13.9 and 28.4% and those WC quartile of each index. The unadjusted ORs for all were 13.4 and 24.5%. The increase in the cumula- TG-related indices for incident T2DM were higher tive incidence of T2DM with higher category of than that of WC. These trends were similar after ad- WC was less marked than that with increasing TyG justment for age, BMI, hypertensive status, family index or FPG. history of diabetes, smoking, alcohol consumption status and regular exercise. When the highest quar- Cut-off points of each index for predicting T2DM tile for each index was compared to the lowest, the Table 3 shows the AROC values and cut-off points of adjusted ORs of WC, TG, FPG, LAP, and TyG index the indices for predicting T2DM. The AROCs for were 1.64 (95% confidence interval (CI), 1.13–2.38), WC, TG, FPG, LAP, and TyG index were 0.579, 2.03 (1.59–2.61), 3.85 (2.99–4.97), 2.47 (1.82–3.34), 0.592, 0.660, 0.602, and 0.623 in men 0.576, 0.627, and 2.79 (2.16–3.60) in men, and 1.17 (0.83–1.65), Table 2 Number of incident type 2 diabetes cases according to the quartiles for each measure Categories Men (n = 3636) Women (n = 4072) Quartile Diabetes (%) Quartile Diabetes (%) WC (cm) Q1 (< 78.0) 143 (16.4) Q1 (< 74.0) 128 (13.4) Q2 (78.0–83.2) 162 (17.6) Q2 (74.0–80.6) 198 (18.1) Q3 (83.3–88.2) 207 (22.3) Q3 (80.7–87.8) 222 (22.2) Q4 (≥88.3) 254 (27.7) Q4 (≥87.9) 249 (24.5) TG (mg/dl) Q1 (< 106.0) 137 (15.2) Q1 (< 92.5) 126 (12.4) Q2 (106.0–142.9) 167 (18.4) Q2 (92.5–121.9) 136 (13.6) Q3 (143.0–200.9) 196 (21.5) Q3 (122.0–165.9) 225 (21.9) Q4 (≥201.0) 266 (29.0) Q4 (≥166.0) 310 (30.3) FPG (mg/dl) Q1 (< 78.0) 106 (12.9) Q1 (< 76.0) 133 (13.9) Q2 (78.0–82.9) 132 (14.7) Q2 (76.0–79.9) 143 (16.3) Q3 (83.0–89.9) 190 (19.5) Q3 (80.0–84.9) 206 (18.3) Q4 (≥90.0) 338 (35.9) Q4 (≥85.0) 315 (28.4) LAP Q1 (< 16.7) 137 (15.1) Q1 (< 18.8) 126 (12.4) Q2 (16.7–29.4) 140 (15.4) Q2 (18.8–30.7) 167 (16.4) Q3 (29.5–49.1) 212 (23.3) Q3 (30.8–50.1) 190 (18.7) Q4 (≥49.2) 277 (30.5) Q4 (≥50.2) 314 (30.8) TyG index Q1 (< 8.4) 121 (13.3) Q1 (< 8.2) 113 (11.1) Q2 (8.4–8.6) 153 (16.8) Q2 (8.2–8.4) 140 (13.8) Q3 (8.7–9.0) 206 (22.7) Q3 (8.5–8.7) 229 (22.5) Q4 (≥9.1) 286 (31.5) Q4 (≥8.8) 315 (30.9) Abbreviations: WC waist circumference, TG triglycerides, FPG fasting plasma glucose, LAP lipid accumulation product, TyG index the product of fasting glucose and triglycerides Lee et al. BMC Endocrine Disorders (2018) 18:33 Page 5 of 10 Table 3 The area under the ROC curve (AROC) and cut-off points for indices to predict type 2 diabetes Index AROC (95% CI) Cut-off point Sensitivity (%) Specificity (%) Youden index Men WC (cm) 0.579 (0.563–0.595) 84.00 56.01 57.11 0.13 TG (mg/dl) 0.592 (0.576–0.608) 172.00 46.74 67.84 0.15 FPG (mg/dl) 0.660 (0.645–0.676) 87.00 51.31 72.89 0.24 LAP 0.602 (0.586–0.618) 30.50 62.79 55.54 0.18 TyG index 0.623 (0.607–0.638) 8.86 52.09 66.59 0.19 Women WC (cm) 0.576 (0.561–0.592) 78.17 69.26 43.54 0.13 TG (mg/dl) 0.627 (0.612–0.642) 122.00 66.37 54.41 0.21 FPG (mg/dl) 0.599 (0.584–0.614) 84.00 39.52 75.69 0.15 LAP 0.623 (0.607–0.637) 35.84 57.59 61.59 0.19 TyG index 0.644 (0.629–0.659) 8.52 67.25 55.85 0.23 Abbreviations: AROC area under the receiver operating characteristic curve, WC waist circumference, TG triglycerides, FPG fasting plasma glucose, LAP lipid accumulation product, TyG index, the product of fasting glucose and triglycerides 2.42 (1.90–3.08), 2.15 (1.71–2.71), 2.44 (1.82–3.26), practice because of measurement inaccuracy [34]. and 2.85 (2.22–3.66) in women, respectively. The Among the TG-related indices analyzed in the calculated VIF for all covariates in the multivariate present study, the AROC and reclassification ability model, were below 5.0, indicating no severe multi- of TyG index were higher than for TG or LAP. collinearity among covariates [33]. Although the inclusion of LAP also improved the predictive ability of the risk model for incident dia- Effects of the addition of each index to the basic model betes, this required the additional inclusion of WC, of T2DM on cNRI, IDI, and AROC while this was not required to demonstrate im- Table 5 shows the reclassification and discrimination provements in the risk model by the inclusion of abilities when each measure was added to the basic TG or TyG index [20]. Most studies that used TyG model of diabetes. The AROCs for the addition of index to predict incident T2DM used it as a surro- TyG index or FPG to the basic model were larger gate for insulin resistance, and reported that its than those for the other indices in both sexes. The predictive ability is better than that of the homeo- cNRI for TG, FPG, LAP, and TyG index were 22.7% stasis model of assessment (HOMA-IR) [19, 35, 36]. (P < 0.0001), 48.0% (P < 0.0001), 23.6% (P < 0.0001), However, Abbasi and Reaven showed that the cor- and 38.7% (P < 0.0001) in men, and 28.6% (P < relation between insulin-mediated glucose uptake 0.0001), 21.3% (P < 0.0001), 21.3% (P < 0.0001), and (IMGU) and TyG index is not better than that be- 36.0% (P < 0.0001) in women, respectively. By con- tween IMGU and TG or HOMA-IR [37, 38]. Never- trast, the addition of WC made cNRI only 5.6% (P = theless, it was noted that TyG index is a practical 0.1689), in men and − 2.3% (P = 0.5666), in women. measurefor useinT2DMpredictionbecause of its The IDI was also higher when TG-related indices or cost-efficiency [37]. Moreover, the use of HbA 1c FPG was added than when WC was added to the has been recommended for diagnosis and screening basic model of T2DM in both sexes. of patients. Lim et al. added HbA to a diabetes 1c risk model and demonstrated an increase in pre- Discussion dictive ability for T2DM in a middle-aged Korean We showed that the predictive ability of the simple cohort, while Ahn et al. also demonstrated through T2DM risk model was increased when TG-related longitudinal validation analysis that adding FPG or indices or FPG was added. Recently, several dia- HbA to the simple diabetes risk model also im- 1c betes risk models have been proposed to screen proves its predictive ability [11, 22]. Thus, in gen- high-risk groups, some of which included blood pa- eral, the predictive ability of a diabetes risk model rameters. WC was included as one of the compo- that includes laboratory parameters such as TG, nents in the model because increased WC increases FPG, or HbA is better than that of the simple 1c T2DM risk [8]. However, WC is not easily used in diabetes risk model [11, 14, 22]. Lee et al. BMC Endocrine Disorders (2018) 18:33 Page 6 of 10 Table 4 Multiple logistic regression analyses for each measure in predicting type 2 diabetes (odds ratios and 95% confidence intervals) a) Categories Unadjusted Adjusted WC TG FPG LAP TyG index WC TG FPG LAP TyG index ORs (95% CI) ORs (95% CI) ORs (95% CI) ORs (95% CI) ORs (95% CI) ORs (95% CI) ORs (95% CI) ORs (95% CI) ORs (95% CI) ORs (95% CI) Men Q1 Ref. Ref. Ref. Ref. Ref. Ref. Ref. Ref. Ref. Ref. Q2 1.09 (0.85–1.39) 1.26 (0.99–1.62) 1.16 (0.88–1.53) 1.03 (0.79–1.33) 1.32 (1.02–1.70) 1.07 (0.81–1.40) 1.21 (0.94–1.55) 1.18 (0.89–1.56) 1.04 (0.79–1.36) 1.26 (0.97–1.64) Q3 1.46 (1.15–1.85) 1.54 (1.21–1.95) 1.64 (1.27–2.12) 1.71 (1.35–2.18) 1.91 (1.49–2.45) 1.35 (1.00–1.83) 1.39 (1.08–1.79) 1.68 (1.29–2.19) 1.70 (1.28–2.25) 1.82 (1.41–2.36) Q4 1.95 (1.55–2.46) 2.29 (1.82–2.88) 3.78 (2.96–4.82) 2.47 (1.96–3.11) 2.99 (2.36–3.79) 1.64 (1.13–2.38) 2.03 (1.59–2.61) 3.85 (2.99–4.97) 2.47 (1.82–3.34) 2.79 (2.16–3.60) Women Q1 Ref. Ref. Ref. Ref. Ref. Ref. Ref. Ref. Ref. Ref. Q2 1.43 (1.12–1.82) 1.11 (0.86–1.44) 1.20 (0.93–1.55) 1.39 (1.08–1.78) 1.28 (0.98–1.66) 1.14 (0.88–1.48) 1.02 (0.78–1.33) 1.18 (0.91–1.53) 1.26 (0.97–1.64) 1.19 (0.91–1.55) Q3 1.86 (1.46–2.36) 1.98 (1.56–2.51) 1.38 (1.09–1.75) 1.63 (1.27–2.07) 2.32 (1.82–2.97) 1.27 (0.95–1.69) 1.69 (1.32–2.16) 1.32 (1.04–1.68) 1.35 (1.03–1.78) 1.97 (1.53–2.53) Q4 2.10 (1.66–2.66) 3.07 (2.44–3.87) 2.45 (1.95–3.06) 3.15 (2.51–3.97) 3.59 (2.83–4.55) 1.17 (0.83–1.65) 2.42 (1.90–3.08) 2.15 (1.71–2.71) 2.44 (1.82–3.26) 2.85 (2.22–3.66) a) Adjusted for age, body mass index, status of hypertension, family history of diabetes, smoking status, alcohol consumption, and regular exercise Abbreviations: ORs odds ratios, WC waist circumference, TG triglycerides, FPG fasting plasma glucose, LAP lipid accumulation product, TyG index, the product of fasting glucose and triglycerides Lee et al. BMC Endocrine Disorders (2018) 18:33 Page 7 of 10 Table 5 Reclassification and discrimination results associated with the risk prediction of incident type 2 diabetes according to each measure Parameter Men Women AROC p-value* cNRI p-value IDI p-value AROC p-value* cNRI p-value IDI p-value a) Basic model 0.615 0.621 Basic model + WC 0.619 0.1176 0.056 0.1689 0.002 0.0140 0.621 0.3233 −0.023 0.5666 0.000 1.0000 Basic model + TG 0.632 <.0001 0.227 <.0001 0.007 <.0001 0.652 <.0001 0.286 <.0001 0.019 <.0001 Basic model + FPG 0.692 <.0001 0.480 <.0001 0.064 <.0001 0.652 <.0001 0.214 <.0001 0.018 <.0001 Basic model + LAP 0.634 <.0001 0.236 <.0001 0.007 <.0001 0.644 <.0001 0.213 <.0001 0.016 <.0001 Basic model + TyG index 0.656 <.0001 0.387 <.0001 0.023 <.0001 0.666 <.0001 0.360 <.0001 0.029 <.0001 a) Basic model: age, body mass index, status of hypertension, family history of diabetes, smoking status, alcohol consumption, and regular exercise *P-value for AROC means vs. Basic model Abbreviations: AROC area under the receiver operating characteristic curve, WC waist circumference, TG triglycerides, LAP lipid accumulation product, TyG index the product of fasting glucose and triglycerides, cNRI continuous net reclassification improvement, IDI Integrated Discrimination Improvement The advantages of the simple diabetes risk model universe coverage, the health promotion that offered are that it is inexpensive and patients can use it for the general health check-ups for 17.6 million Koreans self-assessment [8]. Weighed against the improved in 2016, and its following examination rate (77.7%), predictive accuracy obtained by the addition of HbA there should be less burden caused by blood tests 1c and the Oral Glucose Tolerance Test (OGTT) to the than other countries [43]. Therefore, TyG index is T2DM risk model, there are substantial practical con- recommended as a screening tool for the prediction straints on screening the whole population using such of T2DM. Although TyG index predicts incident measurements [22]. As an effective alternative, a T2DM well, its usefulness is inconsistent, when com- two-pronged approach to screening has been pro- pared to fasting glucose [38, 44]. For predicting posed [22, 39]. This approach consists of using the T2DM risk, TyG index was not better than FPG or non-laboratory score for the general population and OGTT in Isfahan Diabetes Prevention Study [38]. using the laboratory score for patients in a higher-risk Wang et al. reported that TyG index, LAP, and vis- group for T2DM. For screening as part of the Korean ceral adiposity were not superior to FPG or WC NHI program, TG-related indices or FPG are more alone as diabetes predictors among Chinese [44]. In suitable as one of the components of the T2DM risk the Vascular-Metabolic CUN cohort, Navarro-Gonzalez et model than OGTT or HbA , given that these mea- al. compared the prediction ability of TyG index and FPG 1c surements are not currently included in primary for onset T2DM [35]. Association between indexes and screening measurements in Korea [23]. To the best of their discrimination for onset T2DM were different de- our knowledge, there is no TyG index criterion yet. pending on the fasting glucose subgroup. When the On the other hand, several studies conducted in highest quartile for each index was compared to the Europe and Asian have shown that the risk of inci- lowest, the hazard ratios (HRs) of TyG index and dent T2DM was increased with increasing TyG index FPG were 3.0 and 7.3 in the impaired fasting glucose [40–42]. Some researchers have proposed a cut-off group. On the other hand, TyG index showed stron- value of TyG index of 8.8 for incident T2DM and in- ger association with onset DM than FPG in the nor- sulin resistance [41, 42]. The subjects in the present mal fasting glucose group (HRs: 6.8 vs. 4.6). The study were classified by the cut-off value of TyG discrimination of TyG index for onset T2DM was index of 8.8. The adjusted odds ratio (95% CI) of in- also better than that of FPG in the normal fasting cident T2DM in the subjects with TyG index ≥8.8 glucose group (AROC: 0.75 vs. 0.66). In the present was 1.95 (1.73–2.20) compared to the counterparts. study, we found the association between some meta- We also have proposed sex-specific cut-off points of bolic syndrome (MetS) components and incident TyG index (≥ 8.86 in men, ≥ 8.52 in women) as pre- T2DM (Appendix 2). Therefore, we compared the dictors of T2DM based on the maximized Youden discrimination of each index for incident T2DM in index. Compared to the counterparts, the adjusted the stratified MetS components (Appendix 3). In the odds ratio (95% CI) of incident T2DM in the men elevated FPG group, the discrimination of FPG with TyG index ≥8.86 and in the women with TyG (AROC: 0.506) for incident T2DM was inferior to index ≥8.52 were 2.01 (1.69–2.39) and 2.16 (1.82– that of other indices. On the other hand, TyG index 2.56), respectively (Appendix 1). Considering NIH’s was a more robust discrimination index than other Lee et al. BMC Endocrine Disorders (2018) 18:33 Page 8 of 10 indices in the group stratified by sex and MetS com- Appendix 2 ponents. Our findings indicate that TyG index is not Table 7 Multiple logistic regression analyses for each MetS only a better predictor for incident diabetes than WC, component in predicting type 2 diabetes (odds ratios and LAP, and TG, but it also has a better reclassification 95% confidence intervals) ability. On the other hand, association between FPG Components Men Women a) of MetS h) h) and its(their) reclassification ability for incident Unadjusted Adjusted Unadjusted Adjusted T2DM were different depending on sex. The present ORs (95% CI) ORs (95% CI) ORs (95% CI) ORs (95% CI) study used community-based long-term prospective Central 1.71 (1.42–2.06) 1.35 (1.10–1.65) 1.56 (1.33–1.83) 1.16 (0.97–1.38) b) obesity cohort data. Therefore, the temporal relationship be- c) Elevated TG 1.70 (1.45–2.00) 1.57 (1.31–1.87) 2.26 (1.93–2.64) 1.90 (1.60–2.26) tween each measure and incident diabetes is clear. d) Low HDL-C 1.14 (0.96–1.34) 1.01 (0.84–1.22) 1.53 (1.28–1.83) 1.21 (1.00–1.47) Our findings indicate that TyG index is not only a better predictor for incident diabetes than WC, LAP, Elevated 5.43 (4.09–7.21) 5.16 (3.85–6.92) 3.65 (2.39–5.57) 3.55 (2.29–5.50) e) FPG and TG, but it also has a better reclassification abil- f) Elevated BP 1.79 (1.52–2.10) 1.47 (1.24–1.75) 1.65 (1.41–1.93) 1.29 (1.09–1.54) ity. It was also noted that association between FPG g) i) i) MetS 2.22 (1.86–2.66) 2.24 (1.87–2.69) 2.22 (1.88–2.61) 2.03 (1.70–2.41) and its reclassification ability for incident T2DM were a) b) Modified NCEP-ATP III criteria [47] with the Korean cut-off for WC, WC ≥ different depending on sex. There were, however, a c) d) 90 cm for men ≥85 for women, Lee et al. [48], TG ≥ 150 mg/dl, HDL-C < few limitations to this study. Firstly, even though e) f) 40 mg/dl for men < 50 mg/dl for women, FPG ≥ 100 mg/dl, Systolic blood g) KoGES is designed to include subjects who live both pressure ≥ 130 mmHg or diastolic blood pressure ≥ 85 mmHg, Number of h) MetS components ≥3, Adjusted for other components of MetS, age, in rural and urban areas, the data are not representa- family history of diabetes, smoking status, alcohol consumption, and i) tive of the entire Korean population [45]. Secondly, regular exercise, Adjusted for age, family history of diabetes, smoking status, alcohol consumption, and regular exercise dietary intake was not included in the analysis. More- Abbreviations: MetS Metabolic syndrome, ORs odds ratios, TG triglyceride, over, Baik et al. had reported that the usefulness of HDL-C high-density lipoprotein cholesterol, FPG fasting plasma glucose, BP blood pressure, WC, waist circumference dietary information in cardiovascular disease risk pre- diction models [46]. However, dietary information has been excluded from the questionnaire in medical check since 2009. Finally, our results were not vali- dated using a separate Korean dataset. Therefore, additional studies are required to validate our data. Conclusions 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 fast- ing glucose level, TyG index was a more robust pre- dictor for onset T2DM than other indexes. Therefore, TyG index can be a useful screening tool for incident T2DM in middle-aged Koreans. Appendix 1 Table 6 Multiple logistic regression analyses for cutoff-points of TyG index in predicting type 2 diabetes (odds ratios and 95% confidence intervals) Cutoff- Men Women points a) a) Unadjusted Adjusted Unadjusted Adjusted ORs (95% CI) ORs (95% CI) ORs (95% CI) ORs (95% CI) TyG index 2.05 (1.75–2.41) 1.93 (1.62–2.29) 2.41 (2.05–2.83) 2.03 (1.71–2.41) b) ≥8.8 TyG index 2.13 (1.82–2.51) 2.01 (1.69–2.39) 2.57 (2.18–3.02) 2.16 (1.82–2.56) c) ≥8.86/8.52 a) Adjusted for age, body mass index, status of hypertension, family history of b) diabetes, smoking status, alcohol consumption, and regular exercise, TyG c) index ≥8.8 cm, Lee et al. [42], TyG index ≥8.86 cm for men ≥8.52 for women in the present study Abbreviations: ORs odds ratios; TyG index, the product of fasting glucose and triglycerides Lee et al. BMC Endocrine Disorders (2018) 18:33 Page 9 of 10 Appendix 3 Table 8 The area under the ROC curve (AROC) for indices to predict type 2 diabetes stratified by MetS components a) b) c) d) e) f) MetS components Central obesity Elevated TG Low HDL-C Elevated FPG Elevated BP Subgroup No Yes No Yes No Yes No Yes No Yes Index AROC AROC AROC AROC AROC AROC AROC AROC AROC AROC Men (n = 2929) (n = 707) (n = 1970) (n = 1666) (n = 2326) (n = 1310) (n = 3423) (n = 213) (n = 2092) (n = 1544) WC 0.553 0.532 0.535 0.569 0.570 0.584 0.571 0.573 0.565 0.559 TG 0.589 0.534 0.546 0.559 0.598 0.574 0.588 0.591 0.572 0.588 FPG 0.648 0.680 0.637 0.676 0.661 0.666 0.621 0.506 0.649 0.661 LAP 0.584 0.537 0.546 0.582 0.598 0.599 0.595 0.604 0.582 0.591 TyG index 0.618 0.569 0.605 0.607 0.631 0.605 0.607 0.586 0.602 0.620 Women (n = 2680) (n = 1392) (n = 2754) (n = 1318) (n = 1276) (n = 2796) (n = 3983) (n = 89) (n = 2639) (n = 1433) WC 0.556 0.519 0.536 0.564 0.520 0.583 0.576 0.506 0.570 0.545 TG 0.610 0.626 0.567 0.559 0.611 0.622 0.629 0.596 0.614 0.621 FPG 0.590 0.598 0.581 0.617 0.550 0.616 0.586 0.566 0.598 0.587 LAP 0.601 0.625 0.553 0.590 0.565 0.630 0.623 0.570 0.608 0.609 TyG index 0.628 0.643 0.595 0.596 0.621 0.643 0.641 0.597 0.633 0.634 a) b) c) d) Modified NCEP-ATP III criteria [47] with the Korean cut-off for WC, WC ≥ 90 cm for men ≥85 for women, Lee et al. [48], TG ≥ 150 mg/dl, HDL-C < 40 mg/dl for e) f) men < 50 mg/dl for women, FPG ≥ 100 mg/dl, Systolic blood pressure ≥ 130 mmHg or diastolic blood pressure ≥ 85 mmHg Abbreviations: MetS Metabolic syndrome, TG triglyceride, HDL-C high-density lipoprotein cholesterol, FPG fasting plasma glucose, BP blood pressure, AROC area under the receiver operating characteristic curve, WC waist circumference, LAP lipid accumulation product, TyG index, the product of fasting glucose and triglycerides Abbreviations Ethics approval and consent to participate AROC: Area under the receiver operating characteristic curve; BMI: Body mass The Institutional Review Board of the Korean Centers for Disease Control and index; CI: Confidence interval; cNRI: Continuous net reclassification Prevention approved the KoGES protocol. Written informed consent was improvement; FPG: Fasting plasma glucose; HbA :Glycatedhemoglobin; obtained from all subjects. 1c HRs: Hazard ratios; IDI: Integrated discrimination improvement; IMGU: Insulin- mediated glucose uptake; KNHANES: Korea National Health and Nutrition Competing interests Examination; KNIH: Korea National Institute of Health; KoGES: Korean Genome The authors declare that they have no competing interest. and Epidemiology Study; LAP: Lipid accumulation product; NHI: National Health Insurance; OGTT: Oral Glucose Tolerance Test; ORs: Odds ratios; T2DM: Type 2 diabetes mellitus; TG: Triglyceride; TyG index: Product of fasting glucose and Publisher’sNote triglyceride; VIF: Variable inflation factor; WC: Waist circumference Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Acknowledgements Epidemiologic data used in this study were from the Korean Genome and Author details Epidemiology Study (KoGES) of the Korea Centers for Disease Control & Division of Cardiovascular Diseases, Center for Biomedical Sciences, Korea Prevention, Republic of Korea. National Institute of Health, 187 Osongsaengmyeng 2-ro, Osong-eup, Heungdeok-gu, Cheongju-si, Chungcheongbuk-do 361-951, South Korea. Funding Department of Public Health Sciences, Graduate School, Korea University, This study was supported by an intramural grant of the Korea National Seoul, South Korea. Institute of Health, Korea 4800–4845-302 (2017-NI63001–00). The funders had no role in the design of the study and collection, analysis, and interpretation Received: 18 July 2017 Accepted: 16 May 2018 of data and in writing the manuscript. Availability of data and materials References The information of KoGES can be obtained at [http://www.nih.go.kr/NIH/ 1. Esteghamati A, Gouya MM, Abbasi M, Delavari A, Alikhani S, Alaedini F, eng/main.jsp > Research infrastructure > KoGES]. The detailed cohort profile Safaie A, Forouzanfar M, Gregg EW. Prevalence of diabetes and impaired and how to access the data are described in the following source: KIM YJ, fasting glucose in the adult population of Iran: National Survey of risk Han BG, KoGES group. Cohort profile: the Korean genome and epidemiology factors for non-communicable diseases of Iran. Diabetes Care. 2008; study (KoGES) Consortium. International journal of epidemiology, 2016, 46.2: 31(1):96–8. e20-e20. The data that support the findings of this study are available from the 2. Kim DJ. The epidemiology of diabetes in Korea. Diabetes Metab J. 2011; Korea National Institute of Health (KNIH) but restrictions apply to the availability 35(4):303–8. of these data. Data can be accessible upon reasonable request and with ap- 3. Ministry of Health and Welfare of Korea KCfDCaP. Korea health statistics proval of a designated research proposal review committee of the KNIH. 2011: Korea National Health and Nutrition Examination Survey (KNHANES V- 2). Seoul: Ministry of Health and Welfare of Korea; 2012. Authors’ contributions 4. Ministry of Health and Welfare of Korea KCfDCaP. Korea health JWL carried out the data analysis and wrote the manuscript. NKL contributed statistics 2014: Korea National Health and Nutrition Examination to study design and advised statistical analyses. HYP contributed to study Survey (KNHANES VI-2). Sejong: Ministry of Health and Welfare of design and critically reviewed the paper. All authors read and approved the Korea; 2015. final manuscript. 5. Federation ID. IDF Diabetes Atlas. 7th ed; 2015. Lee et al. BMC Endocrine Disorders (2018) 18:33 Page 10 of 10 6. Li G, Zhang P, Wang J, An Y, Gong Q, Gregg EW, Yang W, Zhang B, Shuai Y, 30. Pencina MJ, D'Agostino RB, D'Agostino RB, Vasan RS. Evaluating the added Hong J. Cardiovascular mortality, all-cause mortality, and diabetes incidence predictive ability of a new marker: from area under the ROC curve to after lifestyle intervention for people with impaired glucose tolerance in the reclassification and beyond. Stat Med. 2008;27(2):157. Da Qing diabetes prevention study: a 23-year follow-up study. Lancet 31. Pencina MJ, D'Agostino RB, Steyerberg EW. Extensions of net reclassification Diabetes Endocrinol. 2014;2(6):474–80. improvement calculations to measure usefulness of new biomarkers. Stat 7. Tuomilehto J, Schwarz P, Lindström J. Long-term benefits from lifestyle Med. 2011;30(1):11–21. interventions for type 2 diabetes prevention time to expand the efforts. 32. Kennedy K, Pencina M. A SAS macro to compute added predictive ability of Diabetes Care. 2011;34(Supplement 2):S210–4. new markers predicting a dichotomous outcome. In: SouthEeast SAS Users Group Annual Meeting Proceedings: 2010; 2010. 8. Noble D, Mathur R, Dent T, Meads C, Greenhalgh T. Risk models and scores 33. Vatcheva KP, Lee M, McCormick JB, Rahbar MH. Multicollinearity in for type 2 diabetes: systematic review. Bmj. 2011;343:d7163. regression analyses conducted in epidemiologic studies. Epidemiol. 2016; 9. Chien K, Cai T, Hsu H, Su T, Chang W, Chen M, Lee Y, Hu F. A prediction 6(227). https://doi.org/10.4172/2161-1165.1000227. https://www.omicsonline. model for type 2 diabetes risk among Chinese people. Diabetologia. 2009; org/open-access/multicollinearity-in-regression-analyses-conducted-in- 52(3):443–50. inepidemiologic-studies-2161-1165-1000227.php?aid=69442. 10. Lee Y-H, Bang H, Kim HC, Kim HM, Park SW, Kim DJ. A simple screening 34. Sebo P, Beer-Borst S, Haller DM, Bovier PA. Reliability of doctors' score for diabetes for the Korean population development, validation, and anthropometric measurements to detect obesity. Prev Med. 2008;47(4): comparison with other scores. Diabetes Care. 2012;35(8):1723–30. 389–93. 11. Lim N-K, Park S-H, Choi S-J, Lee K-S, Park H-Y. A risk score for predicting the 35. Navarro-González D, Sánchez-Íñigo L, Pastrana-Delgado J, Fernández- incidence of type 2 diabetes in a middle-aged Korean cohort. Circ J. 2012; Montero A, Martinez JA. Triglyceride–glucose index (TyG index) in 76(8):1904–10. comparison with fasting plasma glucose improved diabetes prediction in 12. Lindström J, Tuomilehto J. The diabetes risk score. Diabetes Care. 2003;26(3): patients with normal fasting glucose: the vascular-metabolic CUN cohort. 725–31. Prev Med. 2016;86:99–105. 13. Mann DM, Bertoni AG, Shimbo D, Carnethon MR, Chen H, Jenny NS, 36. Vasques ACJ, Novaes FS, MdS d O, JRM S, Yamanaka A, Pareja JC, Tambascia Muntner P. Comparative validity of 3 diabetes mellitus risk prediction MA, MJA S, Geloneze B. TyG index performs better than HOMA in a Brazilian scoring models in a multiethnic US cohort the multi-ethnic study of population: a hyperglycemic clamp validated study. Diabetes Res Clin Pract. atherosclerosis. Am J Epidemiol. 2010;171(9):980–8. 2011;93(3):e98–e100. 14. Wilson PW, Meigs JB, Sullivan L, Fox CS, Nathan DM, D’Agostino RB. 37. Abbasi F, Reaven G. Statin-induced diabetes: how important is insulin Prediction of incident diabetes mellitus in middle-aged adults: the resistance? J Intern Med. 2015;277(4):498–500. Framingham offspring study. Arch Intern Med. 2007;167(10):1068–74. 38. Janghorbani M, Almasi SZ, Amini M. The product of triglycerides and 15. Consultation WE: Waist circumference and waist-hip Ratio 2011. glucose in comparison with fasting plasma glucose did not improve 16. Ko G, Chan J, Woo J, Lau E, Yeung V, Chow C, Wai H, Li J, So W, Cockram C: diabetes prediction. Acta Diabetol. 2015:1–8. Simple anthropometric indexes and cardiovascular risk factors in Chinese. 39. Wannamethee S, Papacosta O, Whincup P, Thomas M, Carson C, Lawlor D, Int J Obes Relat Metab Disord 1997, 21(11):995–1001. Ebrahim S, Sattar N. The potential for a two-stage diabetes risk algorithm 17. Janghorbani M, Amini M. Normal fasting plasma glucose and risk of combining non-laboratory-based scores with subsequent routine non- prediabetes and type 2 diabetes: the Isfahan diabetes prevention study. fasting blood tests: results from prospective studies in older men and Rev Diabet Stud. 2011;8(4):490. women. Diabet Med. 2011;28(1):23–30. 18. Zhang M, Gao Y, Chang H, Wang X, Liu D, Zhu Z, Huang G. 40. Zheng R, Mao Y. Triglyceride and glucose (TyG) index as a predictor of Hypertriglyceridemic-waist phenotype predicts diabetes: a cohort study in incident hypertension: a 9-year longitudinal population-based study. Lipids Chinese urban adults. BMC Public Health. 2012;12(1):1081. Health Dis. 2017;16(1):175. 19. Lee S-H, Kwon H-S, Park Y-M, Ha H-S, Jeong SH, Yang HK, Lee J-H, Yim H-W, 41. Navarro-González D, Sánchez-Íñigo L, Fernández-Montero A, Pastrana- Kang M-I, Lee W-C. Predicting the development of diabetes using the Delgado J, Martinez JA. TyG index change is more determinant for product of triglycerides and glucose: the Chungju metabolic disease cohort forecasting type 2 diabetes onset than weight gain. Medicine. 2016;95(19) (CMC) study. PLoS One. 2014;9(2):e90430. 42. Lee DY, Lee ES, Kim JH, Park SE, Park C-Y, Oh K-W, Park S-W, Rhee E-J, Lee 20. Kahn HS. The lipid accumulation product is better than BMI for identifying W-Y. Predictive value of triglyceride glucose index for the risk of incident diabetes a population-based comparison. Diabetes Care. 2006;29(1):151–3. diabetes: a 4-year retrospective longitudinal study. PLoS One. 2016;11(9): 21. Song YJ. The south Korean health care system. JMAJ. 2009;52(3):206–9. e0163465. 22. Ahn CH, Yoon JW, Hahn S, Moon MK, Park KS, Cho YM. Evaluation of non- 43. National Health Insurance Service. 2016 National health screening statistical laboratory and laboratory prediction models for current and future diabetes yearbook http://www.nhis.or.kr/menu/boardRetriveMenuSet.xx?menuId=F3328. mellitus: a cross-sectional and retrospective cohort study. PLoS One. 2016; 44. Wang B, Zhang M, Liu Y, Sun X, Zhang L, Wang C, Linlin L, Ren Y, Han C, 11(5):e0156155. Zhao Y. Utility of three novel insulin resistance-related lipid indexes for 23. Korean National Health Insurance Service: National Health Insurance predicting type 2 diabetes mellitus among people with normal fasting [http://www.nhis.or.kr/static/html/wbd/g/a/wbdga0606.html]. glucose in rural China. J Diab. 2018. https://www.ncbi.nlm.nih.gov/pubmed/ 24. Kim Y, Han B-G. Cohort profile: the Korean genome and epidemiology study (KoGES) consortium. Int J Epidemiol. 2016;46(2):e20–e20. 45. Shin C, Abbott R, Lee H, Kim J, Kimm K. Prevalence and correlates of 25. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of orthostatic hypotension in middle-aged men and women in Korea: the low-density lipoprotein cholesterol in plasma, without use of the Korean health and genome study. J Hum Hypertens. 2004;18(10):717–23. preparative ultracentrifuge. Clin Chem. 1972;18(6):499–502. 46. Baik I, Cho N, Kim S, Shin C. Dietary information improves cardiovascular 26. Association AD. Diagnosis and classification of diabetes mellitus. Diabetes disease risk prediction models. Eur J Clin Nutr. 2013;67(1):25. Care. 2010;33(Supplement 1):S62–9. 47. Third Report of the National Cholesterol Education Program. (NCEP) expert 27. Park SK, Ryoo J-H, Oh C-M, Choi J-M, Choi Y-J, Lee KO, Jung JY. The risk of panel on detection, evaluation, and treatment of high blood cholesterol in type 2 diabetes mellitus according to 2-hour plasma glucose level: the Korean adults (adult treatment panel III) final report. Circulation. 2002;106(25): genome and epidemiology study (KoGES). Diabetes Res Clin Pract. 2017. 3143–421. https://www.ncbi.nlm.nih.gov/pubmed/?term=The+risk+of+type+2+diabetes 48. Lee SY, Park HS, Kim DJ, Han JH, Kim SM, Cho GJ, Kim DY, Kwon HS, Kim SR, +mellitus+according+to+2-hour+plasma+glucose+level%3A+the+Korean Lee CB. Appropriate waist circumference cutoff points for central obesity in +genome+and+epidemiology+study. Korean adults. Diabetes Res Clin Pract. 2007;75(1):72–80. 28. Zweig MH, Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem. 1993;39(4):561–77. 29. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–45.

Journal

BMC Endocrine DisordersSpringer Journals

Published: May 30, 2018

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off