Growth trajectories in lipid profile and fasting blood sugar in prediabetic people over a 16- year follow-up and future risk of type2 diabetes mellitus: A latent growth modeling approach. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Feizi, Awat; Haghighatdoost, Fahimeh; Zakeri, Parisa; Aminorroaya, Ashraf; Amini, Masoud
• Is Part Of: Alexandria journal of medicine. Volume 58:Issue 1(2022)
• Page Start: 52
• Page End: 59
• Publication Date: 2022-12-31
• Subjects: Serum lipids -- fasting blood sugar -- prediabetes -- type 2 diabetes mellitus -- latent class growth model; Medicine -- Periodicals; 610.5
• DOI: 10.1080/20905068.2022.2062958
• Languages: English
• ISSNs: 2090-5068

Abstract

ABSTRACT: Introduction: The current study aimed to investigate whether the pattern of changes in lipid profile and fasting blood sugar (FBS) can predict the future risk of type 2 diabetes mellitus (T2DM) incidence in prediabetic people. Methods: In a prospective cohort study, 1228 prediabetic patients were followed from 2003 until 2019 and longitudinal data on lipid indices (CHOL: cholesterol; HDL: high-density lipoprotein; LDL: low-density lipoprotein; TG: triglyceride) and fasting blood sugar (FBS) were recorded. The latent class growth model (LCGM) was used to estimate growth trajectories and to determine distinct subgroups (latent class) with a similar trajectory for lipid profile and FBS over time. The incidence of T2DM in extracted latent classes was compared. Results: Finally, 946 people with complete data were included in statistical analysis. Two latent classes were identified based on the change in FBS including high-risk and low-risk classes (class size: 5.2% vs. 94.8%) with T2DM incidence rates 100% and 35.9%, respectively (P < 0.001). Two distinct subgroups were identified based on changes in lipid profile. Latent classes with abnormal TG, CHOL, HDL, and LDL included 18.8%, 21.8%, 38.8%, and 24% of study participants, respectively. The incidence rates of T2DM and remaining prediabetic in abnormal TG latent class were 57.2% and 30.8%, and in abnormal HDL latent class were 41.5% and 31.7% were significantly different from normal latent classes (P < 0.001). While in the extracted latent classes based on CHOL and LDL the incidence rates were not statistically significant differences (P > 0.05). Conclusions: We identified two subgroups with high and low risk of future T2DM based on the changes in FBS and lipid profile by applying LCGM. The incidence of T2DM in extracted latent classes was significantly different. LCGM is a reliable approach for predicting the risk of T2DM incidence based on trajectories of risk factors.