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Updated: Aug 29, 2025
Author Spotlight: Assessing the Cardiovascular Profile of Patients with Metabolic Syndrome
Published on: September 27, 2024
Data-driven classification of prediabetes using cardiometabolic biomarkers: Data from National Health and Nutrition
Yan Jiang1, Jinying Xia2, Caiyan Che1
1Medical Department, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China.
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 Examination Survey (NHANES) underwent comprehensive phenotyping and physical and laboratory variable 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 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:
A total of 4,385 subjects with prediabetes were classified into six clusters of distinctive patterns by manifesting higher or lower levels of certain metabolic parameters in each cluster. Subjects with prediabetes in cluster 1 had the lowest prevalence of hypertension, decreased eGFR, and increased ACR, whereas these were much higher in cluster 5 and cluster 6. Except for cluster 3, all the other clusters had significantly increased odds ratio (OR) of hypertension as 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 cluster 1.
Conclusion:
We stratified subjects with prediabetes into six subgroups with different characteristics. With further development and validation, such approaches might guide early intervention on the risk factors for the subjects with prediabetes who would benefit most.
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