AUTHOR=Jiang Yan , Xia Jinying , Che Caiyan , Wei Yongning 

TITLE=Data-driven classification of prediabetes using cardiometabolic biomarkers: Data from National Health and Nutrition Examination Survey 2007–2016

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2022.937942

DOI=10.3389/fendo.2022.937942

ISSN=1664-2392

ABSTRACT=Background Cluster analyses have proposed different prediabetes phenotypes using glycemic parameters, body fat distribution, liver fat content and insulin sensitivity. We aimed at classifying the subjects with prediabetes using cluster analysis and exploring the associations between prediabetes clusters with hypertension and kidney function.
Methods Patients with prediabetes in the National Health and Nutrition Evaluation Survey (NHANES) underwent comprehensive phenotyping and physical and laboratory variables assessment. We identified six clusters using consensus clustering analysis based on the measurements representing the body fat, glycemic status, pancreatic islet function, blood lipids and liver function. Differences between clusters in the characteristics and prevalence of hypertension, decreased estimated glomerular filtration rate (eGFR) and increased albumin-to-creatinine ratio (ACR) were compared between clusters.
Results 4385 subjects with prediabetes were classified into six clusters of distinctive patterns by manifesting higher or lower levels of certain metabolic parameters in each cluster. Prediabetes in cluster 1 had the lowest prevalence of hypertension, decreased eGFR and increased ACR, whereas those were much higher in cluster 5 and cluster 6. Except the cluster 3, all the other clusters had significantly increased odds ratio (ORs) of hypertension compared with cluster 1. Compared with cluster 1, all the other clusters presented significantly increased ORs of decreased eGFR. There were also significantly higher ORs of increased ACR for cluster 5 (OR 1.95, 95% confidence interval [CI] 1.09 - 3.51) and cluster 6 (OR 2.02, 95%CI 1.15 - 3.52) compared with those in cluster 1.
Conclusion We stratified prediabetes into six subgroups with different characteristics. With further development and validation, such approaches might guide early intervention on the risk factors for the prediabetes who would benefit most.