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Volume 13
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10.3390/medsci13040243
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Article

Identifying Cardio-Metabolic Subtypes of Prediabetes Using Latent Class Analysis

by
Gulnaz Nuskabayeva
1,
Yerbolat Saruarov
1,
Karlygash Sadykova
1,
Mira Zhunissova
1,
Nursultan Nurdinov
1,
Kumissay Babayeva
2,
Mariya Li
3,
Akbota Zhailkhan
3,
Aida Kabibulatova
3 and
Antonio Sarria-Santamera
3,*
1
Department of Special Clinical Disciplines, Medical Faculty, Khoja Akhmet Yassawi International Kazakh-Turkish University, Bekzat Sattarkhanov Street No.29, Turkistan 161200, Kazakhstan
2
Department of Fundamental Sciences, Medical Faculty, Khoja Akhmet Yassawi International Kazakh-Turkish University, Bekzat Sattarkhanov Street No.29, Turkistan 161200, Kazakhstan
3
Department of Biomedical Sciences, Nazarbayev University School of Medicine, Astana 010000, Kazakhstan
*
Author to whom correspondence should be addressed.
Med. Sci. 2025, 13(4), 243; https://doi.org/10.3390/medsci13040243 (registering DOI)
Submission received: 9 September 2025 / Revised: 22 October 2025 / Accepted: 23 October 2025 / Published: 25 October 2025
(This article belongs to the Section Endocrinology and Metabolic Diseases)
Versions Notes

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.
Keywords: prediabetes; glucose metabolism; cardiovascular risk; latent class analysis; Kazakhstan; insulin resistance; β-cell function prediabetes; glucose metabolism; cardiovascular risk; latent class analysis; Kazakhstan; insulin resistance; β-cell function

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MDPI and ACS Style

Nuskabayeva, G.; Saruarov, Y.; Sadykova, K.; Zhunissova, M.; Nurdinov, N.; Babayeva, K.; Li, M.; Zhailkhan, A.; Kabibulatova, A.; Sarria-Santamera, A. Identifying Cardio-Metabolic Subtypes of Prediabetes Using Latent Class Analysis. Med. Sci. 2025, 13, 243. https://doi.org/10.3390/medsci13040243

AMA Style

Nuskabayeva G, Saruarov Y, Sadykova K, Zhunissova M, Nurdinov N, Babayeva K, Li M, Zhailkhan A, Kabibulatova A, Sarria-Santamera A. Identifying Cardio-Metabolic Subtypes of Prediabetes Using Latent Class Analysis. Medical Sciences. 2025; 13(4):243. https://doi.org/10.3390/medsci13040243

Chicago/Turabian Style

Nuskabayeva, Gulnaz, Yerbolat Saruarov, Karlygash Sadykova, Mira Zhunissova, Nursultan Nurdinov, Kumissay Babayeva, Mariya Li, Akbota Zhailkhan, Aida Kabibulatova, and Antonio Sarria-Santamera. 2025. "Identifying Cardio-Metabolic Subtypes of Prediabetes Using Latent Class Analysis" Medical Sciences 13, no. 4: 243. https://doi.org/10.3390/medsci13040243

APA Style

Nuskabayeva, G., Saruarov, Y., Sadykova, K., Zhunissova, M., Nurdinov, N., Babayeva, K., Li, M., Zhailkhan, A., Kabibulatova, A., & Sarria-Santamera, A. (2025). Identifying Cardio-Metabolic Subtypes of Prediabetes Using Latent Class Analysis. Medical Sciences, 13(4), 243. https://doi.org/10.3390/medsci13040243

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