Risk Score for Detecting Dysglycemia: A Cross-Sectional Study of a Working-Age Population in an Oil Field in China

Abstract

BackgroundDysglycemia (pre-diabetes or diabetes) in young adults has increased rapidly. However, the risk scores for detecting dysglycemia in oil field staff and workers in China are limited. This study developed a risk score for the early identification of dysglycemia based on epidemiological and health examination data in an oil field working-age population with increased risk of diabetes.Material/MethodsMultivariable logistic regression was used to develop the risk score model in a population-based, cross-sectional study. All subjects completed the questionnaires and underwent physical examination and oral glucose tolerance tests. The performance of the risk score models was evaluated using the area under the receiver operating characteristic curve (AUC).ResultsThe study population consisted of 1995 participants, 20–64 years old (49.4% males), with undiagnosed diabetes or pre-diabetes who underwent periodic health examinations from March 2014 to June 2015 in Dagang oil field, Tianjin, China. Age, sex, body mass index, history of high blood glucose, smoking, triglyceride, and fasting plasma glucose (FPG) constituted the Dagang dysglycemia risk score (Dagang DRS) model. The performance of Dagang DRS was superior to m-FINDRISC (AUC: 0.791; 95% confidence interval (CI), 0.773–0.809 vs. 0.633; 95% CI, 0.611–0.654). At the cut-off value of 5.6 mmol/L, the Dagang DRS (AUC: 0.616; 95% CI, 0.592–0.641) was better than the FPG value alone (AUC: 0.571; 95% CI, 0.546–0.596) in participants with FPG <6.1 mmol/L (n=1545, P=0.028).ConclusionsDagang DRS is a valuable tool for detecting dysglycemia, especially when FPG <6.1 mmol/L, in oil field workers in China.

Identifying Glucose Metabolism Status in Nondiabetic Japanese Adults Using Machine Learning Model with Simple Questionnaire

“…However, prediabetes presents overlapping pathophysiology of impaired insulin sensitivity and secretion [25,26]. Although screening tools for prediabetes have been developed [14,[16][17][18][19][20], this is the first study to develop a model to identify the glucose metabolism status of individuals without diabetes. This model encourages individuals to understand their glucose metabolism status and learn how they should change their lifestyle to prevent diabetes.…”

“…Unlike diabetics who need to take their medications, nondiabetic people have no strong motivation or coercion to take screening tests. Clinical measurement values, such as fasting plasma glucose and abdominal circumference, are valid predictors of glucose metabolism status [14,[17][18][19][20]. However, the need to link a tool to clinical laboratory data may limit their scope of use.…”

“…However, due to the aforementioned differences from previous studies, this model has its own merits. In addition, some previous tools used the history of hyperglycemia and hypertension as variables [19,[28][29][30][31]. These have a clear association with diabetes risk and may contribute to improving the model performance.…”

A Mobile App for Identifying Individuals With Undiagnosed Diabetes and Prediabetes and for Promoting Behavior Change: 2-Year Prospective Study (Preprint)

“…Some researchers have developed different sets of diabetes risk score models for the Chinese population in recent years. Tian et al developed the Dagang dysglycemia risk score model to identify UDPD for the oil field working-age population [34]. Another risk score model for detecting type 2 diabetes for a rural adult Chinese population was developed by Zhang and colleagues [35].…”