Association of mean arterial pressure with 5-year risk of incident diabetes in Chinese adults：a secondary population-based cohort study.

OBJECTIVE: Hypertension predicts the development of diabetes. However, there are still lacking high-quality studies on the correlation between mean arterial pressure (MAP) and incident diabetes. We aimed to explore the relationship between MAP and diabetes in Chinese adults. DESIGN: This is a secondary retrospective cohort study and the data were downloaded from the 'DATADRYAD' database (www.Datadryad.org). PARTICIPANTS: The study included 210 418 adults without diabetes at baseline between 2010 and 2016 across 32 sites and 11 cities in China. SETTING: The target-independent and dependent variables were MAP measured at baseline and diabetes occurred during follow-up. Cox proportional hazards regression was used to explore the relationship between MAP and diabetes. PRIMARY OUTCOME MEASURES: The outcome was incident diabetes, which was defined as fasting blood glucose ≥7.00 mmol/L and/or self-reported diabetes during follow-up. Patients were censored either at the time of the diagnosis or at the last visit, whichever comes first. RESULTS: 3927 participants developed diabetes during a 5-year follow-up. After adjusting covariates, MAP positively correlated with diabetes (HR=1.008, 95% CI 1.005 to 1.011, p<0.001), and the absolute risk difference was 0.02%. E-value analysis and multiple imputations were used to explore the robustness of the results. The relationship between MAP and diabetes was also non-linear, and the inflection point of MAP was 100.333 mm Hg. Subgroup analysis revealed a stronger association between MAP and diabetes in people with age (≥30,<50 years old), fasting plasma glucose <6.1 mmol/L and drinking. Additionally, receiver operating characteristic (ROC) curves showed the predictive performance of MAP for diabetes was similar to systolic blood pressure (SBP) (area under the curve (AUC)=0.694 with MAP vs AUC=0.698 with SBP). CONCLUSIONS: MAP is an independent predictor for a 5-year risk of incident diabetes among Chinese adults. The relationship between MAP and diabetes is also non-linear. When MAP is below 100.333 mm Hg, MAP is closely positively related to diabetes.

Machine Learning for Predicting the 3-Year Risk of Incident Diabetes in Chinese Adults.

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 >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.

Association of hypertension and incident diabetes in Chinese adults: a retrospective cohort study using propensity-score matching.

BACKGROUND: Reliable quantification of the relationship between hypertension and diabetes risk is limited, especially among Chinese people. We aimed to investigate the association between hypertension and the risk of diabetes in a large cohort of the Chinese population. METHODS: This was a retrospective propensity score-matched cohort study among 211,809 Chinese adults without diabetes at baseline between 2010 and 2016. The target independent and dependent variable were hypertension at baseline and incident diabetes during follow-up respectively. The propensity score matching using a non-parsimonious multivariable logistic regression was conducted to balance the confounders between 28,711 hypertensive patients and 28,711 non-hypertensive participants. The doubly robust estimation method was used to investigate the association between hypertension and diabetes. RESULTS: In the propensity-score matching cohort, diabetes risk increased by 11.0% among hypertensive patients (HR = 1.110, 95% confidence interval (CI): 1.031-1.195, P = 0.00539). And diabetes risk dropped to 8.3% among hypertensive subjects after adjusting for the propensity score (HR = 1.083, 95%CI: 1.006-1.166, P = 0.03367). Compared to non-hypertensive participants with low propensity score, the risk of incident diabetes increased by 2.646 times among hypertensive patients with high propensity score (HR = 3.646, 95%CI: 2.635-5.045, P < 0.0001). CONCLUSION: Hypertension was associated with an 11.0% increase in the risk of developing diabetes in Chinese adults. And the figure dropped to 8.3% after adjusting the propensity score. Additionally, compared to non-hypertensive participants with low propensity scores, the risk of incident diabetes increased by 2.646 times among hypertensive patients with high propensity scores.

Applying latent class analysis to risk stratification of incident diabetes among Chinese adults.

OBJECTIVE: To use latent class analysis to identify unobservable subpopulations amongst the heterogeneous population and explore the relationship between subpopulations and incident diabetes among Chinese adults. METHODS: The retrospective study included 32,312 Chinese adults without diabetes at baseline. Latent class indicators included demographic and clinical variables. The outcome was incident diabetes. The relationship between latent class and outcome was evaluated with Cox proportional hazard regression analysis. RESULTS: After screening, the two-class latent class model best fits the population. Participants in class 2 are characterized by higher age, body mass index, systolic and diastolic blood pressure, fasting plasma glucose, total cholesterol, triglyceride, low-density lipoprotein cholesterol, serum creatinine, serum urea nitrogen, alanine aminotransferase, and a higher proportion of males, ever/current smokers and drinkers, but lower high-density lipoprotein cholesterol and a lower proportion of family history of diabetes. The risk of diabetes in class 2 was 5.451 times (HR: 6.451, 95%CI: 4.179-9.960, P < 0.00001) and 5.264 times (HR: 6.264, 95%CI: 4.680-8.385, P < 0.00001) higher than that in class 1 during 3-year and 5-year follow-up, respectively. CONCLUSIONS: We used latent class analysis to identify two distinct subpopulations with differential risk of diabetes during 3-year and 5-year follow-up.

A prediction nomogram for the 3-year risk of incident diabetes among Chinese adults.

Identifying individuals at high risk for incident diabetes could help achieve targeted delivery of interventional programs. We aimed to develop a personalized diabetes prediction nomogram for the 3-year risk of diabetes among Chinese adults. This retrospective cohort study was among 32,312 participants without diabetes at baseline. All participants were randomly stratified into training cohort (n = 16,219) and validation cohort (n = 16,093). The least absolute shrinkage and selection operator model was used to construct a nomogram and draw a formula for diabetes probability. 500 bootstraps performed the receiver operating characteristic (ROC) curve and decision curve analysis resamples to assess the nomogram's determination and clinical use, respectively. 155 and 141 participants developed diabetes in the training and validation cohort, respectively. The area under curve (AUC) of the nomogram was 0.9125 (95% CI, 0.8887-0.9364) and 0.9030 (95% CI, 0.8747-0.9313) for the training and validation cohort, respectively. We used 12,545 Japanese participants for external validation, its AUC was 0.8488 (95% CI, 0.8126-0.8850). The internal and external validation showed our nomogram had excellent prediction performance. In conclusion, we developed and validated a personalized prediction nomogram for 3-year risk of incident diabetes among Chinese adults, identifying individuals at high risk of developing diabetes.

An in-depth analysis of glycosylated haemoglobin level, body mass index and left ventricular diastolic dysfunction in patients with type 2 diabetes.

BACKGROUND: Glycosylated hemoglobin (HbA1c) has a detrimental impact on the myocardium with left ventricular (LV) diastolic dysfunction. Obesity is a risk factor of type 2 diabetes. To understand the relationships between HbA1c, body mass index (BMI) and LV diastolic dysfunction, we performed this interaction analysis in patients with type 2 diabetes. METHODS: Total 925 type 2 diabetes patients were selected from the patients who were diagnosed and treated at the First Affiliated Hospital of Shenzhen University. Patients' BMI levels were defined as normal (BMI < 24 kg/m2) and overweight /obese (BMI ≥ 24 kg/m2). Patients' HbA1c levels were grouped as HbA1c ≥ 9%、7% ≤ HbA1c < 9% and HbA1c < 7%. Logistic regression, stratified, interaction analysis, multivariate Cox regression and curve fitting analysis were performed to investigate the correlations and interactions between HbA1c and BMI with LV diastolic dysfunction. RESULTS: The BMI levels were significantly associated with LV diastolic dysfunction in the patients with type 2 diabetes [adjusted model: 1.12 (1.05, 1.20), P = 0.001]. While HbA1c levels had association with LV diastolic dysfunction only in normal BMI group patients [adjusted model: 1.14 (1.01, 1.30), P = 0.0394] and curve correlation was observed. There was a significant interaction between BMI and HbA1c to affect LV diastolic dysfunction (P = 0.0335). Cox regression model analysis showed that the risk of LV diastolic dysfunction was a U type correlation with HbA1c levels in the normal weight group and the turning point was HbA1c at 10%. HbA1c level was not found to have a significant association with LV diastolic dysfunction in overweight/obese group. CONCLUSIONS: In patients with type 2 diabetes, correlation between LV diastolic dysfunction and HbA1c was interactively affected by BMI. Glycemic control is beneficial to the heart function in normal body weight patients. For overweight/obese patients, the risk of LV diastolic dysfunction was not determined by the HbA1c level, indicating it may be affected by other confounding factors.