Identifying Cardio-Metabolic Subtypes of Prediabetes Using Latent Class Analysis

Gulnaz Orazbekovna Nuskabayeva, Yerbolat Galimzhanuli Saruarov, Karlygash Zharylkasynovna Sadykova, Mira Zhunissova, Nursultan Seisenbaiuly Nurdinov, Kumissay Babayeva, M Li, Akbota Zhailkhan, Aida Kabibulatova, Antonio Sarría‐Santamera

2025

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.3390/medsci13040243
Data confidence 70 medium
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Abstract

Background/Objectives: Prediabetes (PreDM) is a heterogeneous condition, impacting hundreds of millions worldwide, associated with a substantially high risk of Type 2 Diabetes Mellitus (T2DM) and cardiovascular complications. Early identification of subgroups within the PreDM population may support tailored prevention strategies. Methods: We conducted a cross-sectional study using data from annual health check-ups of 419 university staff (aged 27–69) in Kazakhstan. Latent Class Analysis (LCA) was applied to identify subgroups of individuals with PreDM based on cardiovascular risk factors. Differences in glucose metabolism markers (fasting glucose, OGTT, HOMA-IR, HOMA-β) were compared across identified classes. Results: PreDM prevalence was 43.4%. LCA revealed four distinct classes: Class 1: healthy, low-risk individuals; Class 2: overweight with moderate metabolic risk; Class 3: older, overweight individuals with high cardio-metabolic risk; and Class 4: obese, middle-aged to older individuals with very high cardio-metabolic risk. Significant differences were found in glucose metabolism profiles across the classes. IFG predominated in Class 1 (95%), while Classes 3 and 4 had higher rates of β-cell dysfunction and combined IFG/IGT patterns. HOMA-β differed significantly between classes (p < 0.001), while HOMA-IR did not. Conclusions: PreDM is highly prevalent in this working-age Kazakh population and demonstrates marked heterogeneity. Based on easily obtainable cardiovascular risk factors, we have identified four subgroups with distinct glucose profiles that may inform personalized interventions. These distinct subgroups may require differentiated prevention strategies, moving beyond a one-size-fits-all approach.