Background: Cluster analysis provides an effective approach in stratifying prediabetes into different subgroups; however, the association of the cluster-based subgroups with prediabetes progression and regression has not been investigated. We aimed to address this issue in a Chinese population.
Methods: A total of 4,128 participants with prediabetes were included to generate cluster-based subgroups of prediabetes based on age, body mass index (BMI), triglyceride-and-glucose (TyG) index, and hemoglobin A1c (HbA1c), using a k-means clustering model. Among them, 1,554 participants were followed-up for about three years to ascertain prediabetes progression and regression. Their association with the cluster-based subgroups of prediabetes was assessed using multinomial logistic regression analyses.
Results: Three clusters of prediabetes were identified among the 4,128 participants, with cluster 0, 1 and 2 accounting for 28.0%, 31.4% and 40.6%, respectively. Participants with prediabetes were featured by the youngest age and the lowest HbA1c in cluster 0, the highest BMI and TyG index in cluster 1, and the oldest age and the lowest BMI in cluster 2. After multivariable-adjustment, both cluster 1 [odds ratio (OR) 3.31, 95% confidence interval (CI): 2.01-5.44] and cluster 2 (OR 2.58, 95% CI: 1.60-4.18) were associated with increased odds of progression to diabetes when compared with cluster 0. They were also associated with decreased odds of regression to normoglycemia (OR 0.54, and 0.56, respectively).
Conclusions: Prediabetes participants featured by older age, higher degree of insulin resistance, higher BMI and worse glycemic condition had higher probability of progression to diabetes but lower chance of regression to normoglycemia.
Keywords: Cluster analysis; Prediabetes; Progression; Regression.
© 2024. Springer-Verlag Italia S.r.l., part of Springer Nature.