Background: The triglyceride glucose index (TyG index) has been regarded as a reliable surrogate marker of insulin resistance and an independent predictor of diabetes. However, few studies have reported the association between the TyG index and diabetes in the elderly population. Accordingly, this study aimed to investigate the association between the TyG index and diabetes progression in elderly Chinese.Methods: Baseline medical history, fasting plasma glucose (FPG), glucose levels during the oral glucose tolerance test (OGTT) after 1-hour (1h-PG) and 2-hour (2h-PG), and triglyceride (TG) were obtained from a cohort of 862 elderly (aged ≥ 60 years) Chinese in the Beijing urban area between 1998 and 1999. A follow-up visit was conducted between 1998 and 2019 to assess incident diabetes. TyG index was calculated by the following formula ln[TG (mg/dL) × FPG (mg(dL)/2]. The predictive values of TyG index, lipids, and glucose levels during OGTT were assessed alone and also in a clinical prediction model comprising traditional risk factors using concordance index (C-index). Areas under the receiver operating characteristics curves (AUC) and 95% CIs were calculated.Results: After 20 years of follow-up, there were 544 cases of incident type 2 diabetes mellitus (63.1% of incidence). The multivariable HRs (95% CI) for TyG index, FPG, 1h-PG and 2h-PG, high-density lipoprotein-cholesterol (HDL-c), and TG were 1.525 (1.290-1.804), 1.350 (1.181-1.544), 1.337 (1.282-1.395), 1.401 (1.327-1.480), 0.505 (0.375-0.681), and 1.120 (1.053-1.192), respectively. The corresponding C-index were 0.623, 0.617, 0.704, 0.694, 0.631, and 0.610, respectively. The AUC (95% CI) for the TyG index, FPG, 1h-PG, 2h-PG, HDL-c, and TG were 0.608 (0.569-0.647), 0.587 (0.548-0.625), 0.766 (0.734-0.797), 0.713 (0.679-0.747), 0.397 (0.358-0.435), and 0.588 (0.549-0.628). The AUC of the TyG index was higher than that of TG but did not differ with FPG and HDL-c. In addition, the AUCs of 1h-PG and 2h-PG were higher than that of the TyG index.Conclusions: Elevated TyG index is independently correlated with an increased risk of incident diabetes in the elderly male population, but it is not superior to OGTT 1h-PG and 2h-PG in predicting the risk of diabetes.

There have been few reports regarding the association between 1 h-PG concentration and type 2 diabetes mellitus (T2DM) in the elderly. This study was performed to assess the efficacy of 1 h-PG and 2 h-PG values in predicting future risk of T2DM in elderly. The study population consisted of 928 male volunteers ≥ 55 years old without diabetes who were involved in a retrospective-prospective cohort study over 20 years with a baseline fasting plasma glucose and OGTT that included measurement of 1 h-PG and 2 h-PG. The predictive capabilities of FPG and 1 h-PG, 2 h-PG values obtained during OGTT alone and added to a clinical prediction model consisting of traditional diabetes risk factors were assessed. Overall, 577 of all the 928 study participants (62%) developed T2DM over the 20-year follow-up. 1 h-PG and 2 h-PG values predicted T2DM and remained independent predictors of T2DM after adjusting for various traditional risk factors [HR = 1.269 (95% CI = 1.214-1.327), P < 0.001; HR = 1.269 (95% CI = 1.179-1.366), P < 0.001, respectively]. C-statistics for 1-h PG (C-statistics 0.794 [95% CI 0.765-0.823]) was significantly greater than that for 2-h PG (C-statistic 0.747 [95% CI 0.716-0.779]) in models adjusting for various traditional risk factors. 1 h-PG had the greatest area under the ROC curve (AUC, 0.766), which was greater than that of 2 h-PG (0.719). 1 h-PG is useful as a predictor of future development of T2DM independently of traditional risk factors in an elderly Chinese male population.

Introduction: Elevated one-hour plasma glucose (1 h-PG) during oral glucose tolerance test predicts the development of type 2 diabetes mellitus and its complications. However, to date, there have been no studies investigating the predictive values of 1 h-PG for the risk of cardiovascular diseases (CVDs) and all-cause mortality in the elderly population in China. This study aimed to evaluate and compare the effectiveness of 1 h-PG and two-hour plasma glucose (2 h-PG) to predict the risk of CVD and all-cause mortality in the Chinese elderly population.Materials and methods: This retrospective and prospective cohort study was conducted using data obtained from the Chinese People’s Liberation Army General Hospital. All the non-diabetic elderly participants, who had plasma glucose measured at 0, 1, and 2 h during an OGTT (75 g glucose), were followed for 20 years. The primary outcomes were all-cause mortality, myocardial infarction, unstable angina, and stroke. Multivariate-adjusted Cox proportional hazard regression models were performed to examine the association between risk factors and outcomes and to estimate the risk of CVD and all-cause mortality based on 1 h-PG levels.Results: A total of 862 non-diabetic male individuals were included. The median age was 74.0 (25th–75th percentile: 68.0–79.0) years. There were 480 CVD events and 191 deaths during 15,527 person-years of follow-up. The adjusted hazard ratio (HR) of 1 h-PG as a continuous variable was 1.097 (95% CI 1.027–1.172; P = 0.006) for CVD events and 1.196 (95% CI 1.115–1.281; P < 0.001) for higher risk of mortality. When compared with the lowest 1 h-PG tertile, the other tertiles were associated with CVD events (HR 1.464, 95% CI 1.031–2.080; P = 0.033 and HR 1.538, 95% CI 1.092–2.166; P = 0.014, for tertile 2 and tertile 3 compared with tertile 1, respectively), and the highest 1 h-PG tertile had a significantly higher risk of mortality (HR 2.384, 95% CI 1.631–3.485; P < 0.001) after full adjustment. Compared with 1 h-PG, 2 h-PG had similar abilities to predict all-cause mortality. However, 2 h-PG was less closely associated with CVD when examined in the fully adjusted model, neither as a continuous variable nor as a categorical variable. Conversely, 1 h-PG remained an independent predictor of CVD and all-cause mortality after adjusting for various traditional risk factors.Conclusion: Patients with higher 1 h-PG had a significantly increased risk of CVD and all-cause mortality regardless of prediabetes status or development of diabetes at follow-up. The 1 h-PG level might be a better predictor of cardiovascular risk than the 2 h-PG level for the Chinese elderly population.

Background: Isolated Impaired Glucose Tolerance (I-IGT) represents a specific prediabetic state that typically requires a standardized oral glucose tolerance test (OGTT) for diagnosis. This study aims to predict glucose tolerance status in Chinese Han men at fasting state using machine learning (ML) models with demographic, anthropometric, and laboratory data.Methods: The study population consisted of 1,117 Chinese Han men aged 50–87 years. Baseline variables including age, fasting plasma glucose (FPG), high blood pressure (HBP), body mass index (BMI), waist to hip ratio (WHR), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were collected from electronic medical records (EMRs) for machine learning model training and validation. Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbors (KNN), Naive Bayes (NB), Adaptive Boosting (AdaBoost) and Gradient Boosting Machines (GBM) were tested for machine learning model performance comparison. Model performance was evaluated using metrics including accuracy, recall, F1 score, positive predictive value (PPV), negative predictive value (NPV), and the area under the receiver operating characteristic curve (AUC). Shapley Additive Explanations (SHAP) and confusion matrix plots were used for model interpretation.Results: The RF model demonstrated the best overall performance with a 96.7% accuracy, recall of 91.4%, F1 score of 95.7%, PPV of 99.1%, and NPV of 95.6%. The AUC values for the SVM, DT, RF, LR, KNN, NB, AdaBoost, and GBM models were 0.97, 0.92, 0.96, 0.97, 0.88, 0.88, 0.97, and 0.97, respectively. While the RF model showed strong overall performance, the LR model had the highest AUC, indicating superior discriminatory power. FPG was identified as the most important predictor for I-IGT, followed by HDL, TC, HBP, BMI, and WHR. Individuals with FPG levels higher than 5.1 mmol/L were more likely to have I-IGT; the performance metrics for this cut-off value were: 89.35% accuracy, 89.79% recall, 85.22% F1 score, 81.09% PPV, 94.38% NPV, and 0.95 AUC.Conclusion: Machine learning models based on demographic and clinical characteristics offer a cost-effective method for predicting I-IGT in Chinese Han men aged over 50, without the need for an OGTT. These models could complement existing early diagnostic strategies, thereby enhancing the early detection and prevention of diabetes. Additionally, FPG alone could serve as an efficient screening tool for the early identification of I-IGT in clinical settings.