Background: The present study aimed to identify individuals with different personalities using latent class analysis and further distinguish those with a high risk of diabetes among different clusters.
Methods: Data were utilized from a large-scale, cross-sectional epidemiological survey conducted in 2018 across 23 provinces in China, employing a multi-stage, stratified sampling technique. Latent class cluster analysis was performed to identify distinct personality clusters based on a series of variables concerning life attitudes. Logistic regression was used to calculate adjusted odds ratios (AORs) after controlling for potential confounding variables, including age, gender, body mass index, smoking status, alcohol consumption, hypertension, and physical activity levels, to determine the association between these groups and diabetes.
Results: Four distinct personality clusters were identified, namely the energy-poor (2.0%), self-domination (61.3%), optimistic (21.3%), and irritable (15.4%) groups. The prevalence of diabetes in these groups was 14.6%, 9.7%, 9.3%, and 11.6%, respectively. After adjusting for potential confounders, the "energy-poor group" exhibited more odds of having diabetes as compared to the "optimistic group" (AOR 1.683, 95%CI: 1.052-2.693; P=0.030).
Conclusion: This study identified an energy-poor group of individuals with a high risk of diabetes. Targeted interventions should consider the emotional and personality characteristics of the elderly.
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