Development and Validation of a New Diabetes Risk Score in Guyana.
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ABSTRACT: Introduction
We present a new diabetes risk score developed and validated in a multi-ethnic population in Guyana, South America. Measurement of in-country diabetes prevalence is a vital epidemiologic tool to combat the pandemic. It is believed that for every person diagnosed with type 2 diabetes there is another undiagnosed. The International Diabetes Federation (IDF) recommends a two-step detection programme using a risk score questionnaire to identify high-risk individuals followed by glycaemic measure.Methods
Data on 798 persons from the 2016 STEPwise Approach to Chronic Disease Risk Factor Surveillance (STEPS) were used to correlate responses to 36 questions with glycated haemoglobin (HbA1C) and fasting plasma glucose (FPG) results. Bootstrapping was used to internally validate the derived seven-variable model. This model with the addition of family history questions was tested in a convenience sample of 659 Guyanese adults and externally validated in a cohort of another 528.Results
An 8-item Guyana Diabetes Risk Score (GDRS) was derived. The final model performed with an area under the curve (AUC) of 0.812 CONCLUSIONS: The validated eight-item Guyana Diabetes Risk Score will be extremely useful in identifying individuals at high risk of having diabetes in Caribbean, Black or East Indian populations.
SUBMITTER: Lowe J
PROVIDER: S-EPMC7136361 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:In this study, we aimed to propose a novel diabetes index for the risk classification based on machine learning techniques with a high accuracy for diabetes mellitus. Upon analyzing their demographic and biochemical data, we classified the 2013-16 Korea National Health and Nutrition Examination Survey (KNHANES), the 2017-18 KNHANES, and the Korean Genome and Epidemiology Study (KoGES), as the derivation, internal validation, and external validation sets, respectively. We constructed a new diabetes index using logistic regression (LR) and calculated the probability of diabetes in the validation sets. We used the area under the receiver operating characteristic curve (AUROC) and Cox regression analysis to measure the performance of the internal and external validation sets, respectively. We constructed a gender-specific diabetes prediction model, having a resultant AUROC of 0.93 and 0.94 for men and women, respectively. Based on this probability, we classified participants into five groups and analyzed cumulative incidence from the KoGES dataset. Group 5 demonstrated significantly worse outcomes than those in other groups. Our novel model for predicting diabetes, based on two large-scale population-based cohort studies, showed high sensitivity and selectivity. Therefore, our diabetes index can be used to classify individuals at high risk of diabetes.
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