Adaptation of finnish diabetes risk score for screening undiagnosed diabetes and hyperglycemia in Chinese adults

TL;DR

Researchers adapted a diabetes risk score originally developed in Finland to better screen for undiagnosed diabetes and high blood sugar in Chinese adults.

Contribution

The study introduces a modified diabetes risk model tailored for Chinese populations, showing improved screening performance compared to the original Finnish model.

Findings

The ModChinese model outperformed the original FINDRISC in detecting diabetes and hyperglycemia in Chinese adults.

The modified model showed higher sensitivity and specificity for diabetes screening in both training and validation datasets.

The prevalence of undiagnosed diabetes was 8.6% in the training dataset and 3.1% in the validation dataset.

Abstract

China has the largest population with diabetes globally, with over half of the cases going undiagnosed, highlighting the need for improved screening efforts. This study aimed to adapt the Finnish Diabetes Risk Score (FINDRSC), a widely used tool for assessing diabetes risk without relying on clinical indicators, for screening undiagnosed hyperglycemia and diabetes among Chinese adults.

Data from the China Health and Nutrition Survey (CHNS), collected in the 2009 wave, were utilized as the training data (n = 7277), and data from the Guangzhou Nutrition and Health Study (GNHS, n = 2970), conducted in the years 2011–2014, were used for validation. Diabetes was defined as fasting plasma glucose (FPG) ≥ 7.0 mmol/L and/or glycated hemoglobin A1c (HbA1c) ≥ 6.5%. Hyperglycemia was defined as FPG ≥ 5.6 mmol/L and/or HbA1c ≥ 5.7%. Predictors in the original FINDRISC model were adjusted according to local standards and guidelines to develop the Modified Chinese screening model (ModChinese). Coefficients and scores of the ModChinese model were estimated using logistic regression. Area under the receiver operating characteristic curve (AUC) was calculated to evaluate model performance.

The prevalence of undiagnosed diabetes and prediabetes was 8.6% and 40.1% in CHNS, and 3.1% and 27.9% in GNHS, respectively. The ModChinese demonstrated superior performance compared to the original FINDRISC, with higher AUC values for detecting both diabetes (0.707 vs. 0.681, p = 0.001) and hyperglycemia (0.680 vs. 0.661, p < 0.001) in the CHNS. Similar improvements were observed in the GNHS, where the ModChinese achieved AUC values of 0.663 for diabetes and 0.606 for hyperglycemia, compared to FINDRISC’s 0.622 and 0.593, respectively. Compared with the original FINDRISC, the ModChinese model showed improved sensitivity and specificity for screening undiagnosed diabetes and enhanced sensitivity for hyperglycemia screening in both training and validation datasets.

The ModChinese model is a simple and effective screening tool for identifying undiagnosed diabetes and hyperglycemia in Chinese adults.

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