Dataset Information

A non-invasive risk score for predicting incident diabetes among rural Chinese people: A village-based cohort study.

ABSTRACT: To develop a new non-invasive risk score for predicting incident diabetes in a rural Chinese population.Data from the Handan Eye Study conducted from 2006-2013 were utilized as part of this analysis. The present study utilized data generated from 4132 participants who were ?30 years of age. A non-invasive risk model was derived using two-thirds of the sample cohort (selected randomly) using stepwise logistic regression. The model was subsequently validated using data from individuals from the final third of the sample cohort. In addition, a simple point system for incident diabetes was generated according to the procedures described in the Framingham Study. Incident diabetes was defined as follows: (1) fasting plasma glucose (FPG) ? 7.0 mmol/L; or (2) hemoglobin A1c (HbA1c) ? 6.5%; or (3) self-reported diagnosis of diabetes or use of anti-diabetic medications during the follow-up period.The simple non-invasive risk score included age (8 points), Body mass index (BMI) (3 points), waist circumference (WC) (7 points), and family history of diabetes (9 points). The score ranged from 0 to 27 and the area under the receiver operating curve (AUC) of the score was 0.686 in the validation sample. At the optimal cutoff value (which was 9), the sensitivity and specificity were 74.32% and 58.82%, respectively.Using information based upon age, BMI, WC, and family history of diabetes, we developed a simple new non-invasive risk score for predicting diabetes onset in a rural Chinese population, using information from individuals aged 30 years of age and older. The new risk score proved to be more optimal in the prediction of incident diabetes than most of the existing risk scores developed in Western and Asian countries. This score system will aid in the identification of individuals who are at risk of developing incident diabetes in rural China.

SUBMITTER: Wen J

PROVIDER: S-EPMC5667808 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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A non-invasive risk score for predicting incident diabetes among rural Chinese people: A village-based cohort study.

Wen Jiangping J Hao Jie J Liang Yuanbo Y Li Sizhen S Cao Kai K Lu Xilin X Lu Xinxin X Wang Ningli N

PloS one 20171102 11

<h4>Objective</h4>To develop a new non-invasive risk score for predicting incident diabetes in a rural Chinese population.<h4>Methods</h4>Data from the Handan Eye Study conducted from 2006-2013 were utilized as part of this analysis. The present study utilized data generated from 4132 participants who were ≥30 years of age. A non-invasive risk model was derived using two-thirds of the sample cohort (selected randomly) using stepwise logistic regression. The model was subsequently validated using ...[more]

PMID: 29095851

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Project description:ObjectiveThere is still limited evidence regarding the relationship between the estimated glomerular filtration rate (eGFR) and pre-diabetes. For that reason, our research aims to survey the association of eGFR with pre-diabetes.MethodsThis study was a retrospective cohort study, which consecutively and non-selectively collected a total of 173301 participants from Rich Healthcare Group in China from January 2010 to 2016. We then used the Cox proportional-hazards regression model to explore the relationship between baseline eGFR and pre-diabetes risk. Using a Cox proportional hazards regression with cubic spline function and smooth curve fitting (cubical spline smoothing), we were able to determine the non-linear relationship between eGFR and pre-diabetes. Additionally, we also conducted a series of sensitivity analyses and subgroup analyses. The DATADRYAD website was updated with data.ResultsThe mean age of the included individuals was 40.95 ± 11.94 years old, and 92318 (53.27%) were male. The mean baseline eGFR was 111.40 ± 14.77 ml/min per 1.73 m2. During a median follow-up time of 3.0 years, 18333 (10.58%) people experienced pre-diabetes. As a result of adjusting for covariates, eGFR had a negative association with incident pre-diabetes (HR=0.993, 95%CI: 0.992-0.995). There was also a U-shaped curve relationship between eGFR and pre-diabetes, and the inflection point of eGFR was 129.793 ml/min per 1.73 m2. HRs on the left and right sides of the inflection point were respectively 0.993 (0.991-0.994) and 1.023 (1.010- 1.037). Our results were robust in the sensitivity analysis. Subgroup analyses indicated that eGFR was strongly associated with the risk of pre-diabetes among participants who were younger than 30 years and 40-70 years, as well as among those who had never smoked. In contrast, the association of eGFR with the risk of pre-diabetes was attenuated among participants who were 30-40 years of age and 70 years of age or older, and among those who currently smoked.ConclusionThis study demonstrates a negative and U-shaped curve association between eGFR and the risk of pre-diabetes among the general Chinese population. Either reduced renal function or glomerular hyperperfusion status may be associated with an increased risk of prediabetes.

Project description: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.

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Dataset Information

A non-invasive risk score for predicting incident diabetes among rural Chinese people: A village-based cohort study.

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A non-invasive risk score for predicting incident diabetes among rural Chinese people: A village-based cohort study.

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