Identifying Cardio-Metabolic Subtypes of Prediabetes Using Latent Class Analysis

Area of Science:

• Endocrinology and Metabolism
• Public Health
• Epidemiology

Background:

• Prediabetes affects millions globally, posing significant risks for Type 2 Diabetes Mellitus and cardiovascular disease.
• Early identification of prediabetes subgroups is crucial for developing targeted prevention strategies.
• The heterogeneity of prediabetes necessitates a move beyond generalized approaches.

Purpose of the Study:

• To identify distinct subgroups within the prediabetes population using cardiovascular risk factors.
• To compare glucose metabolism markers across identified prediabetes subgroups.
• To inform personalized intervention strategies for prediabetes.

Main Methods:

• A cross-sectional study involving 419 university staff in Kazakhstan.
• Latent Class Analysis (LCA) applied to identify prediabetes subgroups based on cardiovascular risk factors.
• Comparison of glucose metabolism markers (fasting glucose, OGTT, HOMA-IR, HOMA-β) across identified classes.

Main Results:

• Prediabetes prevalence was 43.4% in the study population.
• Four distinct prediabetes subgroups were identified: low-risk, moderate metabolic risk, high cardio-metabolic risk, and very high cardio-metabolic risk.
• Significant differences in glucose metabolism profiles, including beta-cell function (HOMA-β), were observed across the classes, with Classes 3 and 4 showing higher rates of beta-cell dysfunction.

Conclusions:

• Prediabetes is highly prevalent and heterogeneous in the working-age Kazakh population.
• Four subgroups with distinct glucose profiles can be identified using readily available cardiovascular risk factors.
• These findings support the development of differentiated, personalized prevention strategies for prediabetes.