Background: Previous studies have revealed that triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C) is one of major risk factors of insulin resistance and diabetes. However, study on the association between TG/ HDL-C and diabetes mellitus (DM) risk is limited, especially in Chinese people. This study was undertaken to investigate the relationship between TG/HDL-C and incident of diabetes in a large cohort in Chinese population. Methods: The present study was a retrospective cohort study. A total of 114,787 adults from Rich Healthcare Group in China, which includes all medical records for participants who received a health check from 2010 to 2016. The target independent variable and the dependent variable were triglyceride to high-density lipoprotein cholesterol ratio measured at baseline and incident of diabetes mellitus appeared during follow-up respectively. Covariates involved in this study included age, gender, body mass index, diastolic blood pressure, systolic blood pressure, fasting plasma glucose, total cholesterol, low density lipoprotein cholesterol, serum creatinine, smoking and drinking status and family history of diabetes. Cox proportional-hazards regression was used to investigate the association of TG/HDL-C and diabetes. Generalized additive models was used to identify non-linear relationships. Additionally, we also performed a subgroup analysis. It was stated that the data had been uploaded to the DATADRYAD website.(Continued on next page) Result: After adjusting age, gender, body mass index, systolic blood pressure, diastolic blood pressure, fasting blood glucose, total cholesterol, low density lipoprotein cholesterol, serum creatinine, smoking and drinking status and family history of diabetes, result showed TG/HDL-C was positively associated with incident of diabetes mellitus (HR = 1.159, 95%CI (1.104, 1.215)). A non-linear relationship was detected between TG/HDL-C and incident of diabetes, which had an inflection point of TG/HDL-C was 1.186. The effect sizes and the confidence intervals on the left and right sides of the inflection point were 1.718(1.433,2.060) and 1.049(0.981,1.120), respectively. Subgroup analysis showed, the stronger association can be found in the population with fasting plasma glucose (FPG) < 6.1 mmol/L (P for interaction< 0.0001; HR = 1.296 with FPG < 6.1 mmol/L vs HR = 1.051 with FPG ≥ 6.1 mmol/L).The same trend was also seen in the population with body mass index (BMI)(≥18.5, < 24 kg/m 2 ) (P for interaction = 0.010,HR = 1.324) and family history without diabetes(P for interaction = 0.025, HR = 1.170).Conclusion: TG/HDL-C is positively associated with diabetes risk. The relationship between TG/HDL-C and incident of diabetes is also non-linear. TG/HDL-C was strong positively related to incident of diabetes when TG/HDL-C is less than 1.186.

Purpose: We aimed to establish and validate a risk assessment system that combines demographic and clinical variables to predict the 3-year risk of incident diabetes in Chinese adults.Methods: A 3-year cohort study was performed on 15,928 Chinese adults without diabetes at baseline. All participants were randomly divided into a training set (n = 7,940) and a validation set (n = 7,988). XGBoost method is an effective machine learning technique used to select the most important variables from candidate variables. And we further established a stepwise model based on the predictors chosen by the XGBoost model. The area under the receiver operating characteristic curve (AUC), decision curve and calibration analysis were used to assess discrimination, clinical use and calibration of the model, respectively. The external validation was performed on a cohort of 11,113 Japanese participants.Result: In the training and validation sets, 148 and 145 incident diabetes cases occurred. XGBoost methods selected the 10 most important variables from 15 candidate variables. Fasting plasma glucose (FPG), body mass index (BMI) and age were the top 3 important variables. And we further established a stepwise model and a prediction nomogram. The AUCs of the stepwise model were 0.933 and 0.910 in the training and validation sets, respectively. The Hosmer-Lemeshow test showed a perfect fit between the predicted diabetes risk and the observed diabetes risk (p = 0.068 for the training set, p = 0.165 for the validation set). Decision curve analysis presented the clinical use of the stepwise model and there was a wide range of alternative threshold probability spectrum. And there were almost no the interactions between these predictors (most P-values for interaction &gt;0.05). Furthermore, the AUC for the external validation set was 0.830, and the Hosmer-Lemeshow test for the external validation set showed no statistically significant difference between the predicted diabetes risk and observed diabetes risk (P = 0.824).Conclusion: We established and validated a risk assessment system for characterizing the 3-year risk of incident diabetes.