A cut-off value for endothelial dysfunction based on the triglyceride-glucose index.

A cut-off value for endothelial dysfunction based on the triglyceride-glucose index - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Sci Rep . 2026 Apr 14;16:17261. doi: 10.1038/s41598-026-48550-y A cut-off value for endothelial dysfunction based on the triglyceride-glucose index Takayuki Yamaji 1, 2 , Farina Mohamad Yusoff 2 , Shinji Kishimoto 2 , Masato Kajikawa 3 , Takahiro Harada 2 , Aya Mizobuchi 2 , Shunsuke Tanigawa 2 , Tatsuya Maruhashi 2 , Yukiko Nakano 4 , Yukihito Higashi 1, 2, 3, ✉ Author information Article notes Copyright and License information 1 Center for Radiation Disaster Medical Science, Research Institute for Radiation Biology and Medicine, Hiroshima University, Hiroshima, Japan 2 Department of Regeneration and Medicine, Research Institute for Radiation Biology and Medicine, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551 Japan 3 Division of Regeneration and Medicine, Medical Center for Translational and Clinical Research, Hiroshima University Hospital, Hiroshima, Japan 4 Department of Cardiovascular Medicine, Hiroshima University Graduate School of Biomedical Sciences, Hiroshima, Japan ✉ Corresponding author. Received 2025 May 7; Accepted 2026 Apr 8; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13234090  PMID: 41981050 Abstract We defined triglyceride-glucose (TyG) index < 8.52 as a low TyG index and TyG index ≥ 8.52 as a high TyG index, and divided the subjects into nine groups based on the levels of TyG index and fasting blood glucose (FBG) levels: FBG levels < 90 mg/dL and low TyG index, FBG levels < 90 mg/dL and high TyG index, FBG levels of 90–94 mg/dL and low TyG index, FBG levels of 90–94 mg/dL and high TyG index, FBG levels of 95–99 mg/dL and low TyG index, FBG levels of 95–99 mg/dL and high TyG index, FBG levels of 100–125 mg/dL and low TyG index, FBG levels of 100–125 mg/dL and high TyG index, and diabetes mellitus. The odds ratios for endothelial dysfunction were significantly higher in groups with high TyG index and any levels of FBG, and the group with low TyG index and FBG levels of 95–99 mg/dL, the group with low TyG index and FBG levels of 100–125 mg/dL and the diabetes mellitus group than the FBG levels < 90 mg/dL and low TyG index group. Even at a low TyG index, subjects with FBG levels ≥ 95 mg/dL might have a higher risk for endothelial dysfunction. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-026-48550-y. Subject terms: Cardiology, Endocrinology, Risk factors Introduction Endothelial cells secrete several kinds of vasodilators, such as nitric oxide, and provide anticoagulant and maintain vascular tone 1 , 2 . Endothelial dysfunction is a pathological state in which vascular tone regulation is impaired, leading to loss of vascular homeostasis. When endothelial dysfunction occurs, nitric bioavailability decreases, oxidative stress and inflammation increase, and a pro-thrombotic environment increases, contributing to the development of atherosclerosis and playing an important role in further cardiovascular events 3 – 6 . Therefore, assessing endothelial function is clinically important. Measurement of flow-mediated vasodilation (FMD) in the brachial artery is a noninvasive tool for assessing endothelial function 7 . Endothelial function assessed by FMD is impaired by the coronary risk factors such as dyslipidemia, hypertension, diabetes mellitus, smoking, and physical inactivity are established risk factors for endothelial dysfunction 3 , 6 , 8 , 9 . Insulin resistance (IR) is one of the main mechanisms of the onset of type 2 diabetes mellitus (DM) and is a risk factor for atherosclerosis 10 – 12 . The glucose clamp method is the most accurate method for quantifying IR 13 . However, the glucose clamp method takes much time and effort. In a clinical setting, homeostatic model assessment of IR (HOMA-IR) is the most popular surrogate marker for IR 14 . However, routine measurement of blood insulin level is also limited due to clinical indications. Recently, triglyceride-glucose (TyG) index has been proposed as an index of IR 15 . The TyG index is calculated as ln[fasting blood glucose (FBG) (mg/dL) × fasting triglycerides (mg/dL)/2] and it has been shown to be significantly correlated with IR measured by the glucose clamp method and HOMA-IR 15 , 16 . However, due to insurance limitations, routine measurement of insulin levels in individuals without DM or individuals who are not hospitalized is difficult in Japan. Therefore, there is still uncertainly regarding the relationship between the TyG index and HOMA-IR in Japanese. The TyG index has been reported to be a useful surrogate marker for further cardiovascular events 17 , metabolic syndrome 18 and type 2 DM 19 . A previous study showed that the TyG index was an independent predictor for FMD 20 . We agree with the results of that study because we previously showed that both the levels of FBG and triglycerides were independent predictors of endothelial dysfunction 9 , 21 . However, a previous study focusing on the relationship between TyG index and FMD had a small sample size. Therefore, there is still uncertainly about what levels of the TyG index are associated with endothelial dysfunction. In the present study, we investigated the relationships between HOMA-IR and TyG index in subjects who were divided into four groups according to the TyG index. We examined the threshold levels of the TyG index at which FMD values begin to be affected by endothelial dysfunction. Furthermore, we investigated whether the combination of FBG and the TyG index enables the stratification of risk for endothelial dysfunction. Results Study 1 Relationship between TyG index and HOMA-IR The baseline characteristics of the 99 subjects in Study 1 are summarized in Supplementary Table S1 . The mean age of the subjects was 51 ± 10 years. The 99 subjects included 48 men (49.0%). Among the subjects, 42 (42.4%) had dyslipidemia, 2 (2.0%) had DM and 19 (19.2%) were current smokers. The mean TyG index was 8.7 ± 0.6, and HOMA-IR was 2.4 ± 1.5. Supplementary Table S2 shows univariate relationships among TyG index, HOMA-IR and variables. TyG index was significantly correlated with BMI (r = 0.42, p < 0.001), total cholesterol (r = 0.30, p = 0.002), triglycerides (r = 0.90, p < 0.001), high-density lipoprotein cholesterol (HDL-C) (r = -0.53, p < 0.001), low-density lipoprotein cholesterol (LDL-C) (r = 0.29, p = 0.004), uric acid (r = 0.43, p < 0.001), FBG level (r = 0.27, p = 0.006), hemoglobin A1c (HbA1c) (r = 0.27, p = 0.006) and HOMA-IR (r = 0.47, p < 0.001). HOMA-IR was significantly correlated with BMI (r = 0.45, p < 0.001), triglycerides (r = 0.47, p < 0.001), HDL-C (r = -0.33, p < 0.001), uric acid (r = 0.31, p = 0.002), FBG level (r = 0.21, p = 0.03), and TyG index (r = 0.47, p < 0.001). Study 2 Baseline characteristics of the subjects The baseline characteristics of the 5961 subjects in Study 2 are summarized in Table 1 . The mean age of the subjects was 49 ± 10 years. The 5961 subjects included 4755 men (80.1%). Among the subjects, 2169 (36.4%) had hypertension, 2562 (43.0%) had dyslipidemia, 128 (2.1%) had DM and 1914 (32.2%) were current smokers. The mean FMD value was 6.5 ± 3.1%. Table 1. Clinical Characteristics of Patients in Four Groups of TyG Index. Variables Total (n = 5961) TyG index < 8.12 (n = 1490) TyG index 8.12–8.51 (n = 1486) TyG index 8.52–8.91 (n = 1495) TyG index ≥ 8.92 (n = 1490) P value Age, yr 49 ± 10 46 ± 11 50 ± 10 50 ± 9 51 ± 9 < 0.001 Men (%) 4775 (80.1) 921 (61.8) 1185 (79.7) 1308 (87.5) 1361 (91.3) < 0.001 Body mass index, kg/m 2 23.2 ± 3.2 21.5 ± 2.9 22.8 ± 2.8 23.8 ± 3.0 24.7 ± 3.1 < 0.001 Heart rate, bpm 63 ± 10 62 ± 10 63 ± 10 63 ± 10 65 ± 11 < 0.001 Systolic blood pressure, mmHg 127 ± 16 121 ± 16 126 ± 16 129 ± 16 131 ± 16 < 0.001 Diastolic blood pressure, mmHg 80 ± 12 76 ± 12 79 ± 12 81 ± 12 83 ± 11 < 0.001 Total cholesterol, mg/dL 204 ± 33 190 ± 30 199 ± 30 208 ± 30 219 ± 32 < 0.001 Triglycerides, mg/dL 104 (72, 150) 58 (48, 65) 87 (79, 96) 124 (112, 136) 197 (169, 247) < 0.001 HDL-C, mg/dL 60 ± 16 70 ± 16 62 ± 14 57 ± 13 51 ± 12 < 0.001 LDL-C, mg/dL 120 ± 29 109 ± 26 119 ± 28 127 ± 28 125 ± 30 < 0.001 Creatinine, mg/dL 0.82 ± 0.19 0.77 ± 0.26 0.81 ± 0.15 0.84 ± 0.14 0.85 ± 0.17 < 0.001 Uric acid, mg/dL 5.8 ± 1.4 5.2 ± 1.3 5.6 ± 1.3 6.0 ± 1.3 6.4 ± 1.3 < 0.001 Fasting blood glucose, mg/dL 97 ± 15 91 ± 9 95 ± 10 98 ± 11 105 ± 21 < 0.001 Hemoglobin A1c, % 5.5 ± 0.4 5.4 ± 0.3 5.5 ± 0.3 5.5 ± 0.4 5.7 ± 0.6 < 0.001 Medical history, n (%) Hypertension 2169 (36.4) 372 (25.0) 510 (34.3) 590 (39.5) 697 (46.8) < 0.001 Dyslipidemia 2562 (43.0) 191 (12.8) 336 (22.6) 626 (41.9) 1409 (94.6) < 0.001 Diabetes mellitus 128 (2.1) 8 (0.5) 11 (0.7) 20 (1.3) 89 (6.0) < 0.001 CVD, n (%) 134 (2.2) 16 (1.1) 41 (2.8) 35 (2.3) 42 (2.8) 0.004 Current Smoking, n (%) 1914 (32.2) 327 (22.0) 458 (30.9) 537 (35.9) 592 (40.0) < 0.001 Medication, n (%) Antihypertensive drugs 1097 (18.4) 189 (12.7) 266 (17.9) 265 (17.7) 377 (25.3) < 0.001 Open in a new tab TyG indicates triglyceride-glucose; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; CVD, cardiovascular disease. Relationships between TyG index and variables Supplementary Table S3 shows univariate relationships among TyG index, FMD and variables. TyG index was significantly correlated with age (r = 0.18, p < 0.001), BMI (r = 0.38, p < 0.001), heart rate (r = 0.13, p < 0.001), systolic blood pressure (r = 0.24, p < 0.001), diastolic blood pressure (r = 0.22, p < 0.001), total cholesterol (r = 0.36, p < 0.001), triglycerides (r = 0.84, p < 0.001), HDL-C (r = − 0.47, p < 0.001), LDL-C (r = 0.22, p < 0.001), creatinine (r = 0.18, p < 0.001), uric acid (r = 0.34, p < 0.001), FBG level (r = 0.41, p < 0.001), HbA1c (r = 0.30, p < 0.001) and FMD (r = − 0.17, p < 0.001). FMD was significantly correlated with age (r = − 0.28, p < 0.001), BMI (r = − 0.17, p < 0.001), heart rate (r = 0.05, p < 0.001), systolic blood pressure (r = − 0.17, p < 0.001), diastolic blood pressure (r = − 0.12, p < 0.001), total cholesterol (r = − 0.08, p < 0.001), triglycerides (r = − 0.11, p < 0.001), HDL-C (r = 0.06, p < 0.001), LDL-C (r = − 0.07, p < 0.001), creatinine (r = − 0.05, p < 0.001), uric acid (r = − 0.14, p < 0.001), FBG level (r = − 0.12, p < 0.001), HbA1c (r = − 0.10, p < 0.001) and TyG index (r = − 0.17, p < 0.001). We next evaluated the incremental predictive value of the TyG index and constructed two logistic regression models for endothelial dysfunction. Model 1 was adjusted for high age, sex, obesity, hypertension and smoking, and Model 2 additionally included the TyG index. The area under the curve (AUC) values of the two models were compared using the DeLong test. The AUC of Model 2 was significantly larger than that of Model 1, indicating that the TyG index provided a statistically significant improvement in the incremental value (AUC 0.63 vs. 0.65, p < 0.001) (Fig. S2 ). FMD values in four groups of TyG index levels The baseline characteristics stratified by TyG index quartiles are presented in Table 1 . There were significant differences in age, sex, BMI, heart rate, systolic blood pressure, diastolic blood pressure, levels of total cholesterol, triglycerides, HDL-C, LDL-C, creatinine, uric acid, FBG, and HbA1c, prevalence of hypertension, dyslipidemia, DM and cardiovascular disease, and use of antihypertensive drugs among the four groups. Figure 1 shows the FMD values in the four groups. The FMD values were 7.1 ± 3.3% in the TyG index < 8.12 group, 6.7 ± 3.0% in the TyG index 8.12–8.51 group, 6.3 ± 2.9% in the TyG index 8.52–8.91 group, and 5.8 ± 2.9% in the TyG index ≥ 8.92 group ( p < 0.001). FMD values in the TyG index 8.12–8.51 group, TyG index 8.52–8.91 group and the TyG index ≥ 8.92 groups were significantly smaller than those in the TyG index < 8.12 group ( p < 0.001). The division points for the lowest quartile and second quartile were FMD of 4.4%. Therefore, we defined endothelial dysfunction as FMD of < 4.4%. We took the TyG index < 8.12 group as a reference for deriving endothelial dysfunction in the other groups. After adjustments for age ≥ 65 years, gender, presence of hypertension, presence of DM, presence of dyslipidemia, presence of obesity, and current smoking, the odds ratios for endothelial dysfunction were significantly higher in the TyG index 8.52–8.91 group (Odd ratio (OR): 136, 95% confidence intervals (CI) 1.13–1.65) and TyG index ≥ 8.92 group (OR 1.59, 95% CI 1.27–2.00) than in the reference group (Table S4 ). For every 1-SD (0.59) increase in TyG index, the odds ratio for endothelial dysfunction was 22% higher even after adjustment for confounding factors (Table S4 ). A cubic spline curve showed a marked increase in the odds ratio for endothelial dysfunction with increasing TyG index. A clear inflection point was observed at a TyG index of 8.52, beyond which the odds ratio for endothelial dysfunction increased more steeply, indicating that a TyG index ≥ 8.52 was associated with a significantly higher odds ratio for endothelial dysfunction (Fig. 2 ). Fig. 1. Open in a new tab Bar graph shows flow-mediated vasodilation in subjects with triglyceride-glucose (TyG) index levels of < 8.12, 8.12–8.51, 8.52–8.91 and ≥ 8.92. Fig. 2. Open in a new tab A cubic spline of the relationship of triglyceride-glucose index with log odds ratios and 95% confidence intervals for endothelial dysfunction using the triglyceride-glucose index of 8.52 as a reference. Endothelial dysfunction was defined as flow-mediated vasodilation of less than 4.4%. Adjusted for age ≥ 65 years, sex, obesity, current smoking, and presence of hypertension. FMD values in nine groups of FBG and TyG index levels Finally, we investigated whether the combination of FBG and TyG index enables risk stratification of endothelial dysfunction. Table S5 shows both FBG ≥ 95 mg/dL (OR 1.30, 95% CI 1.14–1.48) and TyG index ≥ 8.52 (OR 1.27, 95% CI 1.10–1.48) were independent predictors for endothelial dysfunction. Next, we defined low TyG index as TyG index < 8.52 and high TyG index as TyG index levels of ≥ 8.52, and we divided the subjects into nine groups based on FBG level and TyG index: < 90 mg/dL and low TyG index, 90–94 mg/dL and low TyG index, 95–99 mg/dL and low TyG index, 100–125 mg/dL and low TyG index, < 90 mg/dL and high TyG index, 90–94 mg/dL and high TyG index, 95–99 mg/dL and high TyG index, 100–125 mg/dL and high TyG index and FBG ≥ 126 mg/dL or DM groups. The baseline characteristics of subjects are also summarized in Table 2 . FMD values were 7.3 ± 3.2% in the FBG < 90 mg/dL and low TyG index group, 7.1 ± 3.3% in the 90–94 mg/dL and low TyG index group, 6.8 ± 3.2% in the 95–99 mg/dL and low TyG index group, 6.2 ± 2.9% in the 100–125 mg/dL and low TyG index group, 6.3 ± 2.8% in the FBG < 90 mg/dL and high TyG index group, 6.4 ± 2.9% in the 90–94 mg/dL and high TyG index group, 6.2 ± 3.0% in the 95–99 mg/dL and high TyG index group, 5.8 ± 2.9% in the 100–125 mg/dL and high TyG index group and 5.4 ± 2.7% in the FBG ≥ 126 mg/dL or DM group ( p < 0.001; Fig. S3 ). Table 2. Clinical Characteristics of Patients in Nine Groups among FBG and TyG Index. Variables FBG < 90 mg/dL And low TyG index (n = 1067) FBG 90–95 mg/dL and low TyG index (n = 748) FBG 95–99 mg/dL and low TyG index (n = 557) FBG 100–125 mg/dL and low TyG index (n = 574) FBG < 90 mg/dL and high TyG index (n = 458) FBG 90–95 mg/dL and high TyG index (n = 542) FBG 95–99 mg/dL and high TyG index (n = 595) FBG 100–125 mg/dL and high TyG index (n = 1191) DM (n = 229) P value Age, yr 45 ± 11 48 ± 11 49 ± 10 53 ± 9 48 ± 10 49 ± 9 49 ± 9 52 ± 9 57 ± 9 < 0.001 Men (%) 651 (61.0) 511 (68.3) 439 (78.8) 485 (84.5) 393 (85.8) 480 (88.6) 536 (90.1) 1092 (91.7) 188 (82.1) < 0.001 Body mass index, kg/m 2 21.5 ± 2.8 22.0 ± 2.7 22.7 ± 2.8 23.2 ± 3.0 23.7 ± 3.0 23.7 ± 3.0 24.1 ± 2.9 24.6 ± 3.2 24.9 ± 3.6 < 0.001 Heart rate, bpm 61 ± 9 63 ± 10 63 ± 9 64 ± 11 63 ± 9 64 ± 9 64 ± 10 65 ± 11 69 ± 12 < 0.001 Systolic blood pressure, mmHg 119 ± 16 123 ± 15 126 ± 15 128 ± 15 127 ± 15 128 ± 15 128 ± 16 132 ± 16 136 ± 16 < 0.001 Diastolic blood pressure, mmHg 75 ± 12 77 ± 12 79 ± 12 80 ± 11 80 ± 11 81 ± 12 81 ± 12 83 ± 12 84 ± 11 < 0.001 Total cholesterol, mg/dL 192 ± 31 195 ± 29 194 ± 29 198 ± 32 210 ± 32 213 ± 32 212 ± 30 216 ± 32 212 ± 37 < 0.001 Triglycerides, mg/dL 71 (54, 90) 73 (59, 89) 74 (60, 87) 72 (60, 83) 151 (132, 197) 149 (126, 195) 150 (122, 192) 149 (119, 198) 144 (93, 200) < 0.001 HDL-C, mg/dL 67 ± 16 67 ± 15 65 ± 14 65 ± 16 52 ± 12 54 ± 13 54 ± 13 55 ± 14 55 ± 15 < 0.001 LDL-C, mg/dL 111 ± 27 114 ± 27 115 ± 26 119 ± 29 123 ± 30 125 ± 30 125 ± 28 128 ± 29 123 ± 32 < 0.001 Creatinine, mg/dL 0.78 ± 0.29 0.79 ± 0.17 0.80 ± 0.16 0.80 ± 0.15 0.83 ± 0.15 0.84 ± 0.15 0.86 ± 0.14 0.86 ± 0.17 0.80 ± 0.19 < 0.001 Uric acid, mg/dL 5.2 ± 1.3 5.3 ± 1.3 5.6 ± 1.3 5.7 ± 1.3 5.9 ± 1.3 6.1 ± 1.3 6.2 ± 1.2 6.3 ± 1.3 5.9 ± 1.5 < 0.001 Fasting blood glucose, mg/dL 84 ± 4 92 ± 1 97 ± 6 105 ± 5 85 ± 4 92 ± 1 97 ± 5 107 ± 6 143 ± 34 < 0.001 Hemoglobin A1c, % 5.3 ± 0.3 5.4 ± 0.3 5.5 ± 0.3 5.6 ± 0.3 5.4 ± 0.3 5.4 ± 0.3 5.4 ± 0.3 5.6 ± 0.3 6.8 ± 1.1 < 0.001 Medical history, n (%) Hypertension 219 (20.5) 211 (28.2) 189 (33.9) 240 (41.8) 148 (32.3) 198 (36.5) 241 (40.5) 561 (47.1) 162 (70.7) < 0.001 Dyslipidemia 162 (15.2) 125 (16.7) 104 (18.7) 127 (22.1) 314 (68.6) 365 (67.3) 395 (66.4) 824 (69.2) 146 (63.8) < 0.001 CVD, n (%) 10 (0.9) 13 (1.7) 15 (2.7) 18 (3.1) 11 (2.4) 9 (1.7) 10 (1.7) 33 (2.8) 15 (6.6) 0.14 Current Smoking, n (%) 299 (28.1) 189 (25.3) 130 (23.4) 160 (27.9) 200 (43.7) 214 (39.6) 247 (41.5) 411 (34.6) 64 (28.6) < 0.001 Medication, n (%) Antihypertensive drugs 93 (8.7) 113 (15.1) 101 (18.1) 135 (23.5) 67 (14.6) 93 (17.2) 101 (17.0) 297 (24.9) 97 (42.4) < 0.001 Open in a new tab FBG indicates fasting blood glucose; TyG, triglyceride-glucose; DM, diabetes mellitus; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; CVD, cardiovascular disease. We took the FBG < 90 mg/dL and low TyG index group as a reference for deriving the odds ratios for endothelial dysfunction in the other groups. After adjustment for confounding factors, the odds ratios for endothelial dysfunction were significantly higher in the FBG 95–99 mg/dL and low TyG index group (OR 1.33, 95% CI 1.02–1.73), the FBG 100–125 mg/dL and low TyG index group (OR 1.51, 95% CI 1.16–1.95), the FBG < 90 mg/dL and the high TyG index group (OR 1.46, 95% CI 1.10–1.94), the FBG 90–94 mg/dL and high TyG index group (OR 1.34, 95% CI 1.01–1.76), the FBG 95–99 mg/dL and high TyG index group (OR 1.63, 95% CI 1.26–2.12), the FBG 100–125 mg/dL and high TyG index group (OR 1.86, 95% CI 1.48–2.34), and the FBG ≥ 126 mg/dL or DM group (OR 1.70, 95% CI 1.21–2.39) (Table S6 and Fig. 3 ). Fig. 3. Open in a new tab Odds ratios and 95% confidence intervals for endothelial dysfunction using the group with fasting blood glucose (FBG) < 90 mg/dL and triglyceride-glucose (TyG) index of < 8.52 (low TyG index) as a reference. Discussion In the present study, we demonstrated that the TyG index was significantly associated with IR and endothelial function in Japanese, that both FBG ≥ 95 mg/dL and TyG index ≥ 8.52 were independently associated with endothelial dysfunction, and that the combination of FBG and TyG index enables stratification of the risk for endothelial dysfunction. The strengths of this study are that the study that the TyG index, a simple indicator calculated using FBG and triglycerides, reflects IR and that a cut-off value of the TyG index for endothelial dysfunction was determined. The results of this study support the results of a previous study showing that TyG index is a useful marker for IR, atherosclerosis and cardiovascular events 16 . Recently, several investigators have shown the usefulness of the TyG index. However, the cut-off value for the TyG index is still controversial. Kim et al. showed that the highest quartile of TyG index group (9.15 ± 0.27) had a significantly higher odds ratio for prevalence of coronary artery calcification than the odds ratio in the lowest quartile of TyG index group (7.75 ± 0.21) 16 . Huang et al. showed that TyG index ≥ 9.32 was a risk for all-cause death, cardiovascular death, and cardiovascular events 22 , 23 . Cai et al. showed that TyG index ≥ 9.83 was a risk for all-cause death and cardiovascular death in subjects at high risk for cardiovascular disease 23 . However, there has been no study focusing on the cut-off value of TyG index for defining endothelial dysfunction. Our proposed cut-off value, TyG index ≥ 8.52, for endothelial dysfunction is approximately 1.0 to 1.3-times lower than those cut off values for cardiovascular events. Endothelial dysfunction may occur from lower levels of TyG index. Next, we investigated whether the combination of FBG and TyG index enables stratification of the risk of endothelial dysfunction. In our previous study, we investigated relationship of FBG with endothelial function using measurement of FMD in the brachial artery and we showed that an FBG level of 95–99 mg/dL was an independent predictor for endothelial dysfunction 9 . However, our previous study, which focused on FMD and FBG, was unable to distinguish whether the decrease in FMD was influenced by high blood glucose levels or by IR 9 . In the present study, we showed that even at low FBG levels, subjects with a high TyG index might have a higher risk for endothelial dysfunction, and also even at a low TyG index, subjects with FBG levels ≥ 95 mg/dL might have a higher risk for endothelial dysfunction. From the aspect of endothelial function, it was necessary to pay attention to both IR and blood glucose. A possible mechanism by which IR impairs endothelial function has been postulated. Insulin per se exhibits seemingly contradictory effects on blood vessels, acting as both a vasodilator and a vasoconstrictor. One insulin-related pathway promotes vasodilation by an increase in nitric oxide (NO) production through activation of the phosphoinositide 3-kinase (PI3-K)/Akt pathway under the condition of normal glucose levels or non-IR. On the other hand, under the condition of IR, insulin inactivates the PI3-K/Akt pathway, decreases NO production and increases the production of endothelin-1 and reactive oxygen species by the mitogen-activated protein kinase/extracellular signal-regulating kinase pathway, resulting in endothelial dysfunction 24 . In a high glucose state, glucose transporter 1 in endothelial cells is constantly active, making endothelial dysfunction more likely to occur due to high blood glucose levels, and endothelial dysfunction might occur even at lower glucose levels than those in impaired fasting glucose and DM. In hyperglycemic conditions, glucose transporter 1 in endothelial cells remains constantly active, leading to an increased intracellular glucose concentration. This elevated glucose uptake can result in endothelial dysfunction, even at blood glucose levels lower than those typically associated with impaired fasting glucose or DM. Consequently, endothelial dysfunction may occur at glucose concentrations below the standard diagnostic thresholds for these conditions 25 . These findings suggest both IR and high blood glucose are associated with endothelial dysfunction. Abnormal glucose metabolism and endothelial dysfunction can create a vicious cycle, where each condition exacerbates the other. Impaired glucose metabolism leads to endothelial dysfunction, which in turn worsens glucose metabolism, contributing to the progression of cardiovascular disease. This study has several limitations. First, although this study was conducted in multiple centers, this study was a cross-sectional study. We showed an association between TyG index and FMD, but we did not establish a causal relationship between the TyG index and FMD. Second, the subjects of this study were Japanese. Insulin secretion capacity in Asians has been shown to be lower than that in other races 26 . Therefore, assessing other races would enable more specific conclusions regarding the role of TyG index in endothelial function to be drawn. Third, in Japanese insurance regulations, measurement of serum insulin levels is restricted in individuals without DM or non-hospitalized individuals. Therefore, we selected primary aldosteronism because its diagnosis requires inpatient evaluation. In addition, primary aldosteronism is considered to have relatively modest effects on glucose metabolism. However, the number of subjects in whom HOMA-IR was assessed was insufficient to draw a conclusion. Furthermore, we cannot deny the possibility of selection bias. Fourth, we cannot deny the possibility that the TyG index does not strictly reflect IR. However, in the present study, we showed the relationship between TyG index and HOMA-IR, which is clinically used as an IR marker even in a small sample size. Furthermore, we showed that the TyG index, simply calculated from FBG and triglycerides, is associated with endothelial function, and we proposed a cut-off value for detecting endothelial dysfunction based on the TyG index. Fifth, we excluded patients receiving antidiabetic and lipid-lowering drug treatment to minimize the potential effects of these drugs on the TyG index. Furthermore, the participants were predominantly male and older. Therefore, the generalizability of our findings may be limited due to the predominance of older male participants and the exclusion of individuals receiving medical treatment. Sixth, the cut-off value for the TyG index identified in this study is applicable to the Japanese population, specifically for the outcome of FMD < 4.4%. Conclusions In conclusion, TyG index ≥ 8.52 is associated with endothelial dysfunction. Even at low FBG levels, individuals with a high TyG index might have a higher risk for endothelial dysfunction, and also even at a low TyG index, individuals with FBG levels ≥ 95 mg/dL might have a higher risk for endothelial dysfunction. The combination of FBG and TyG index enables stratification of the risk for endothelial dysfunction. Methods Study subjects Study 1 Study 1 was a single center retrospective cross-sectional study that was carried out to investigate the association between TyG index and HOMA-IR. We enrolled 550 patients whose serum insulin levels were measured between November 2016 and July 2024. Subjects who did not have secondary hypertension (n = 379) and those receiving lipid-lowering drugs (n = 62) or anti-diabetic drugs (n = 10) were excluded from this study. Finally, 99 participants were enrolled in Study 1 (Fig. S1 A). Study 2 The Flow-mediated Dilation Japan (FMD-J) Registry was a prospective multicenter registry that was established between April 2010 and August 2018 at 31 institutes in Japan with the aim of determining the usefulness of FMD measurement. All of the subjects had an obligation to undergo health screening every year under the regulations of the society-managed health insurance union in Japan. The design of the FMD-J study has been described in detail 27 . Briefly, the FMD-J study included three study arms: A, B, and C. Patients diagnosed with coronary artery disease were enrolled in Study A; patients with controlled hypertension or diabetes mellitus who had been receiving antihypertensive or antidiabetic drug treatment for at least 6 months were enrolled in Study B; and subjects who underwent annual health checkups were enrolled in Study C. Subjects with missing triglyceride data (n = 19), missing information on lipid-lowering drugs (n = 6), inadequate FMD recordings (n = 3), those taking lipid-lowering drug therapy (n = 1173), and those receiving anti-diabetic drug therapy (n = 223) were excluded from this study. Finally, we enrolled 5961 subjects in Study 2 (Fig. S1 B). Hypertension was defined according to the Japanese Society of Hypertension Guidelines for the Management of Hypertension (JSH 2019) as the use of antihypertensive drugs or systolic blood pressure of more than 140 mm Hg or diastolic blood pressure of more than 90 mm Hg measured in a sitting position on at least 3 occasions 28 . Dyslipidemia was defined according to the third report of the National Cholesterol Education Program 29 . DM was defined according to the American Diabetes Association criteria, including FBG ≥ 126 mg/dL, HbA1c ≥ 6.5%, or the use of antidiabetic drugs 30 . Clinical definitions of cardiovascular disease, coronary heart disease, and cerebrovascular disease were based on those used in the FMD-J study 27 . Cardiovascular disease was defined as coronary heart disease and cerebrovascular disease. Coronary heart disease included angina pectoris, prior myocardial infarction, and unstable angina. Cerebrovascular disease included ischemic stroke, hemorrhagic stroke, and transient ischemic attack 27 . All methods followed the Declaration of Helsinki and relevant guidelines and regulations. Written informed consent for participation in the study was obtained from all subjects. The Ethics Committee of Hiroshima University approved the study protocol. (E-1902 and E-2002) The protocol was registered in the University Hospital Medical Information Network Clinical Trials Registry (UMIN000012950: first registration data 01/24/2014, UMIN000012951: first registration data 01/24/2014, UMIN000012952: first registration data 01/24/2014 and UMIN000003409: first registration data 03/30/2010). Study protocol The protocol used for measurement of FMD was the same in both studies. The subjects fasted overnight and abstained from alcohol, smoking, caffeine and antioxidant vitamins for at least 12 h before the study. The participants were kept in the supine position in a quiet, dark, air-conditioned room (constant temperature of 22 °C to 25 °C) throughout the study. A 23-gauge polyethylene catheter was inserted into the deep antecubital vein to obtain blood samples for measurements including measurement of FBG. After maintaining the supine position for 30 min, FMD was measured. The observers were blind to the form of examination. Study 1. Relationship between the TyG index and HOMA-IR In Study 1, we investigated the relationship between TyG index and HOMA-IR. The TyG index was calculated by the following equation: Ln [FBG (mg/dL) × fasting triglycerides (mg/dL)/2] 15 . HOMA-IR was calculated by the following equation: FBG (mg/dL) × fasting insulin (μU/mL)/405 14 . Study 2. Relationship between TyG index and variables We investigated the relationship between TyG index and endothelial function assessed by measurement of FMD. We divided the subjects into four groups according to the quartile values of TyG index: < 8.12, 8.12–8.51, 8.52–8.91, and ≥ 8.92. We next divided the subjects into nine groups based on their FBG levels and TyG index: FBG levels < 90 mg/dL and low TyG index (TyG index < 8.52), FBG levels of 90–94 mg/dL and low TyG index, FBG levels of 95–99 mg/dL and low TyG index, FBG levels of 100–125 mg/dL and low TyG index, FBG levels < 90 mg/dL and high TyG index (TyG index ≥ 8.52), FBG levels of 90–94 mg/dL and high TyG index, FBG levels of 95–99 mg/dL and high TyG index, FBG levels of 100–125 mg/dL and high TyG index, and FBG levels ≥ 126 mg/dL or DM. We measured the vascular response to reactive hyperemia in the brachial artery for assessment of FMD. Measurement of FMD A high-resolution linear artery transducer was coupled to computer-assisted analysis software (UNEXEF18G, UNEX Co., Nagoya, Japan) that used an automated edge detection system for measurement of the brachial artery diameter 31 . A blood pressure cuff was placed around the forearm of each subject. The brachial artery was scanned longitudinally 5 to 10 cm above the elbow. When the clearest B-mode image of the anterior and posterior intimal interfaces between the lumen and vessel wall was obtained, the transducer was held at the same point throughout the scan by using a special probe holder (UNEX Co.) to ensure consistency of the imaging. Depth and gain setting were set to optimize the images of the arterial lumen wall interface. When the tracking gate was placed on the intima, the artery diameter was automatically tracked, and the waveform of diameter changes over the cardiac cycle was displayed in real time using the FMD mode of the tracking system. This allowed the ultrasound images to be optimized at the start of the scan and the transducer position to be adjusted immediately for optimal tracking performance throughout the scan. Pulsed Doppler flow was assessed at baseline and during peak hyperemic flow, which was confirmed to occur within 15 s after cuff deflation. Blood flow velocity was calculated from the color Doppler data and was displayed as a waveform in real time. Baseline longitudinal images of the artery were acquired for 30 s, and then the blood pressure cuff was inflated to 50 mm Hg above systolic pressure for 5 min. Blood flow volume was calculated by multiplying the Doppler flow velocity (corrected for the angle) by heart rate and vessel cross-sectional area (− r 2 . Reactive hyperemia was calculated as the maximum percentage increase in flow after cuff deflation compared with baseline flow. The correlation coefficient between FMD analyzed at the core laboratory and participant institutions was 0.84 ( p < 0.001). Statistical analysis Results are presented as means ± SD or medians (interquartile range). All reported probability values were 2-sided, and a probability value of < 0.05 was considered statistically significant. Categorical values were compared by means of the chi-square test. Continuous variables were compared by using ANOVA multiple groups. Comparisons between the groups categorized according to the TyG index levels and FBG levels were carried out using ANOVA with Tukey’s post hoc test. OR and 95% CI were calculated using logistic regression analysis. Receiver operating characteristic (ROC) curve analysis was performed to assess the sensitivity and specificity of the TyG index in predicting FMD < 4.4%. The AUC of the TyG index for predicting FMD < 4.4% was assessed. The AUC values of the two models were compared using DeLong’s test. Multivariate logistic regression analysis was performed to identify independent variables associated with lower quartiles of FMD (< 4.4%). Age ≥ 65 years, sex, obesity [body mass index (BMI) ≥ 30], current smoking, presence of hypertension and presence of dyslipidemia were entered into the multivariate logistic regression analysis. The effect of TyG index on endothelial dysfunction was assessed as a continuous variable by cubic spline curves with the TyG index of 8.52 as the reference. All data were processed using JMP Pro. Ver 18.0 software (SAS Institute, Cary, NC, USA) and R software version 3.5.1 (R Foundation for Statistical Computing, Vienna, Austria). Supplementary Information Below is the link to the electronic supplementary material. Supplementary Material 1 (429.3KB, docx) Acknowledgements The authors would like to thank all of the patients who participated in this study. In addition, we thank Miki Kumiji, Megumi Wakisaka, Ki-ichiro Kawano and Satoko Michiyama for their excellent secretarial assistance; FMD-J investigators Takayuki Hidaka, MD, PhD; Shuji Nakamura, MD, PhD; Junko Soga, MD, PhD; Yuichi Fujii, MD, PhD; Naomi Idei, MD; Noritaka Fujimura, MD, PhD; Shinsuke Mikami, MD, PhD; Yumiko Iwamoto, MD; Akimichi Iwamoto, MD, PhD; Takeshi Matsumoto, MD, PhD; Nozomu Oda, MD, PhD (Department of Cardiovascular Medicine, Hiroshima University Graduate School of Biomedical Sciences, Hiroshima, Japan); Kana Kanai, PhD; Haruka Morimoto, PhD (Department of Cardiovascular Regeneration and Medicine, Research Institute for Radiation Biology and Medicine, Hiroshima University, Hiroshima, Japan); Tomohisa Sakashita, MD, PhD; Yoshiki Kudo, MD, PhD (Department of Obstetrics and Gynecology, Hiroshima University Graduate School of Biomedical Sciences, Hiroshima, Japan); Taijiro Sueda, MD, PhD (Department of Surgery, Hiroshima University Graduate School of Biomedical Sciences, Hiroshima, Japan); Akira Yamashina, MD, PhD (Department of Cardiology, Tokyo Medical University, Tokyo, Japan); Bonpei Takase, MD, PhD, FAHA (Division of Biomedical Engineering, National Defense Medical College Research Institute, Tokorozawa, Japan); Takahide Kohro, MD, PhD (Department of Cardiology, Tokyo Medical University, Tokyo, Japan); Toru Suzuki, MD, PhD (Cardiovascular Medicine, University of Leicester, Leicester, UK); Tomoko Ishizu, MD, PhD (Cardiovascular Division, Institute of Clinical Medicine, University of Tsukuba, Ibaraki, Japan); Shinichiro Ueda, MD, PhD (Department of Clinical Pharmacology and Therapeutics, University of the Ryukyu School of Medicine, Okinawa, Japan); Tsutomu Yamazaki, MD, PhD (Clinical Research Support Center, Faculty of Medicine, The University of Tokyo, Tokyo, Japan); Tomoo Furumoto, MD, PhD (Department of Cardiovascular Medicine, Hokkaido University Graduate School of Medicine, Hokkaido, Japan); Kazuomi Kario, MD, PhD (Division of Cardiovascular Medicine, Jichi Medical University School of Medicine, Tochigi, Japan); Teruo Inoue, MD, PhD (Department of Cardiovascular Medicine, Dokkyo Medical University, Mibu, Tochigi, Japan); Shinji Koba, MD, PhD (Department of Medicine, Division of Cardiology, Showa University School of Medicine, Tokyo, Japan); Kentaro Watanabe, MD, PhD (Department of Neurology, Hematology, Metaboism, Endocrinology and Diabetology (DNHMED), Yamagata University School of Medicine, Yamagata, Japan); Yasuhiko Takemoto, MD, PhD (Department of Internal Medicine and Cardiology, Osaka City University Graduate School of Medicine, Osaka, Japan); Takuzo Hano, MD, PhD (Department of Medical Education and Population-based Medicine, Postgraduate School of Medicine, Wakayama Medical University, Wakayama, Japan); Masataka Sata, MD, PhD (Department of Cardiovascular Medicine, Institute of Health Biosciences, The University of Tokushima Graduate School, Tokushima, Japan); Yutaka Ishibashi, MD, PhD (Department of General Medicine, Shimane University Faculty of Medicine, Izumo, Japan); Koichi Node, MD, PhD (Department of Cardiovascular and Renal Medicine, Saga University, Saga, Japan); Koji Maemura, MD, PhD (Department of Cardiovascular Medicine, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan); Yusuke Ohya, MD, PhD (The Third Department of Internal Medicine, University of the Ryukyus, Okinawa, Japan); Taiji Furukawa, MD, PhD (Department of Internal Medicine, Teikyo University School of Medicine, Tokyo, Japan); Hiroshi Ito, MD, PhD (Department of Cardiovascular Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Japan); and Hisao Ikeda, MD, PhD (Faculty of Fukuoka Medical Technology, Teikyo University, Omuta, Japan). Author contributions T.Yamaji. and Y.Higashi, drafting the article and conception of the study; T.Yamaji., F.M.Y., S.K., M.K., T.H., A.M., S.T., and T.M. acquiring subjects and/or data; Y.N. revising the article critically for important intellectual content. Y. Higashi is the guarantor of this work and, as such, had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of data analysis. Funding Grant-in-Aid for Scientific Research from the Ministry of Education, Science and Culture of Japan (18590815 and 21590898 to Y.Higashi), a Grant-in-Aid of Japanese Arteriosclerosis Prevention Fund (to Y. Higashi), Hirose Fundation (to T. Yamaji). Data availability The datasets generated during and/or analyzed during the current study are not publicly available due to protecting participant confidentiality but are available from the corresponding author on reasonable request. Declarations Competing interests The authors declare no competing interests. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References 1. Ross, R. Atherosclerosis–An inflammatory disease. N. Engl. J. Med. 340 , 115–126. 10.1056/nejm199901143400207 (1999). [ DOI ] [ PubMed ] [ Google Scholar ] 2. Higashi, Y., Noma, K., Yoshizumi, M. & Kihara, Y. Endothelial function and oxidative stress in cardiovascular diseases. Circ. J. 73 , 411–418 (2009). [ DOI ] [ PubMed ] [ Google Scholar ] 3. Yamaji, T. et al. Self-reported total sitting time on a non-working day is associated with blunted flow-mediated vasodilation and blunted nitroglycerine-induced vasodilation. Sci. Rep. 12 , 6366. 10.1038/s41598-022-10242-8 (2022). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 4. Oda, N. et al. Endothelial function is impaired in relation to alcohol intake even in the case of light alcohol consumption in Asian men; flow-mediated dilation Japan (FMD-J) study. Int. J. Cardiol. 230 , 523–528. 10.1016/j.ijcard.2016.12.065 (2017). [ DOI ] [ PubMed ] [ Google Scholar ] 5. Matsui, S. et al. Decreased frequency and duration of tooth brushing is a risk factor for endothelial dysfunction. Int. J. Cardiol. 241 , 30–34. 10.1016/j.ijcard.2017.03.049 (2017). [ DOI ] [ PubMed ] [ Google Scholar ] 6. Hashimoto, H. et al. Smoking status and endothelial function in Japanese men. Sci. Rep. 11 , 95. 10.1038/s41598-020-80012-x (2021). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 7. Higashi, Y. Noninvasive assessment of vascular function: From physiological tests to biomarkers. JACC. Asia 4 , 898–911. 10.1016/j.jacasi.2024.09.015 (2024). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 8. Maruhashi, T. et al. Relationship between flow-mediated vasodilation and cardiovascular risk factors in a large community-based study. Heart 99 , 1837–1842. 10.1136/heartjnl-2013-304739 (2013). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 9. Yamaji, T. et al. Pre-impaired fasting glucose state is a risk factor for endothelial dysfunction: Flow-mediated Dilation Japan (FMD-J) study. BMJ Open Diabetes Res. Care. 10.1136/bmjdrc-2020-001610 (2020). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 10. Defronzo, R. A. Banting Lecture. From the triumvirate to the ominous octet: A new paradigm for the treatment of type 2 diabetes mellitus. Diabetes 58 , 773–795. 10.2337/db09-9028 (2009). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 11. Tao, L. C., Xu, J. N., Wang, T. T., Hua, F. & Li, J. J. Triglyceride-glucose index as a marker in cardiovascular diseases: Landscape and limitations. Cardiovasc. Diabetol. 21 , 68. 10.1186/s12933-022-01511-x (2022). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 12. Sasaki, N., Ueno, Y., Ozono, R., Nakano, Y. & Higashi, Y. Insulin resistance in the adipose tissue predicts future vascular resistance: The Hiroshima study on glucose metabolism and cardiovascular diseases. Atherosclerosis 393 , 117547. 10.1016/j.atherosclerosis.2024.117547 (2024). [ DOI ] [ PubMed ] [ Google Scholar ] 13. Bergman, R. N., Finegood, D. T. & Ader, M. Assessment of insulin sensitivity in vivo. Endocr. Rev. 6 , 45–86. 10.1210/edrv-6-1-45 (1985). [ DOI ] [ PubMed ] [ Google Scholar ] 14. Matthews, D. R. et al. Homeostasis model assessment: Insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28 , 412–419. 10.1007/bf00280883 (1985). [ DOI ] [ PubMed ] [ Google Scholar ] 15. Unger, G., Benozzi, S. F., Perruzza, F. & Pennacchiotti, G. L. Triglycerides and glucose index: A useful indicator of insulin resistance. Endocrinol. Nutr. 61 , 533–540. 10.1016/j.endonu.2014.06.009 (2014). [ DOI ] [ PubMed ] [ Google Scholar ] 16. Kim, M. K. et al. Relationship between the triglyceride glucose index and coronary artery calcification in Korean adults. Cardiovasc. Diabetol. 16 , 108. 10.1186/s12933-017-0589-4 (2017). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 17. Ding, X., Wang, X., Wu, J., Zhang, M. & Cui, M. Triglyceride-glucose index and the incidence of atherosclerotic cardiovascular diseases: A meta-analysis of cohort studies. Cardiovasc. Diabetol. 20 , 76. 10.1186/s12933-021-01268-9 (2021). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 18. Son, D. H., Lee, H. S., Lee, Y. J., Lee, J. H. & Han, J. H. Comparison of triglyceride-glucose index and HOMA-IR for predicting prevalence and incidence of metabolic syndrome. Nutr. Metab. Cardiovasc. Dis. 32 , 596–604. 10.1016/j.numecd.2021.11.017 (2022). [ DOI ] [ PubMed ] [ Google Scholar ] 19. Zhang, M. et al. Cumulative increased risk of incident type 2 diabetes mellitus with increasing triglyceride glucose index in normal-weight people: The Rural Chinese Cohort Study. Cardiovasc. Diabetol. 16 , 30. 10.1186/s12933-017-0514-x (2017). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 20. Li, Y. et al. Association between triglyceride-glucose index and endothelial dysfunction. Endocrine 85 , 717–723. 10.1007/s12020-024-03785-5 (2024). [ DOI ] [ PubMed ] [ Google Scholar ] 21. Kajikawa, M. et al. Relationship between serum triglyceride levels and endothelial function in a large community-based study. Atherosclerosis 249 , 70–75. 10.1016/j.atherosclerosis.2016.03.035 (2016). [ DOI ] [ PubMed ] [ Google Scholar ] 22. Huang, R. et al. Prognostic value of triglyceride glucose (TyG) index in patients with acute decompensated heart failure. Cardiovasc. Diabetol. 21 , 88. 10.1186/s12933-022-01507-7 (2022). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 23. Cai, X. L. et al. Prognostic value of triglyceride glucose index in population at high cardiovascular disease risk. Cardiovasc. Diabetol. 22 , 198. 10.1186/s12933-023-01924-2 (2023). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 24. Vicent, D. et al. The role of endothelial insulin signaling in the regulation of vascular tone and insulin resistance. J. Clin. Invest. 111 , 1373–1380. 10.1172/jci15211 (2003). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 25. Veys, K. et al. Role of the GLUT1 glucose transporter in postnatal CNS angiogenesis and blood-brain barrier integrity. Circ. Res. 127 , 466–482. 10.1161/circresaha.119.316463 (2020). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 26. Kodama, K. et al. Ethnic differences in the relationship between insulin sensitivity and insulin response: A systematic review and meta-analysis. Diabetes Care 36 , 1789–1796. 10.2337/dc12-1235 (2013). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 27. Tomiyama, H. et al. A multicenter study design to assess the clinical usefulness of semi-automatic measurement of flow-mediated vasodilatation of the brachial artery. Int. Heart J. 53 , 170–175 (2012). [ DOI ] [ PubMed ] [ Google Scholar ] 28. Umemura, S. et al. The Japanese Society of Hypertension Guidelines for the Management of Hypertension (JSH 2019). Hypertens. Res. 42 , 1235–1481. 10.1038/s41440-019-0284-9 (2019). [ DOI ] [ PubMed ] [ Google Scholar ] 29. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III). Jama . 285 , 2486–2497 (2001). [ DOI ] [ PubMed ] 30. American Diabetes Association. Clinical practice recommendations 1999. Diabetes Care 22 (Suppl 1), S1-114 (1999). [ PubMed ] [ Google Scholar ] 31. Maruhashi, T. et al. Nitroglycerine-induced vasodilation for assessment of vascular function: A comparison with flow-mediated vasodilation. Arterioscler. Thromb. Vasc. Biol. 33 , 1401–1408. 10.1161/atvbaha.112.300934 (2013). [ DOI ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Supplementary Material 1 (429.3KB, docx) Data Availability Statement The datasets generated during and/or analyzed during the current study are not publicly available due to protecting participant confidentiality but are available from the corresponding author on reasonable request. Articles from Scientific Reports are provided here courtesy of Nature Publishing Group ACTIONS View on publisher site PDF (1.7 MB) Cite Collections Permalink RESOURCES Similar articles Cited by other articles Links to NCBI Databases Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

Related documents

• Improving Glucose and Lipid Metabolism Through Caloric Restriction Using Diet or Surgery (CRUDOS)#839732
• Elucidation and Monitoring Postprandial Endothelial Function#872627
• Randomized, Double-blind Crossover Trial Comparing Low-GI Functional vs. Standard Wholegrain Carbohydrates on Glycolipid Metabolism and Vascular Stress Markers in Adults With Suboptimal Triglyceridemia#969939
• Autonomous metabolic reprogramming and oxidative stress characterize endothelial dysfunction in acute myocardial infarction#284499
• Assessment of dapagliflozin effect on diabetic endothelial dysfunction of brachial artery – Brazilian Heart Study 2 (ADDENDA-BHS2)#453175