Affiliation:
1. Zhanggong District Center for Disease Control and Prevention, Ganzhou,
Jiangxi, Peoples R China
2. Cancer Hospital of Shantou University Medical College, Shantou,
Guangdong, Peoples R China
3. Shenzhen Cancer Hospital, Shenzhen, Guangdong, Peoples R
China
4. Shaoxing Center for Disease Control and Prevention, Shaoxing, Zhejiang,
Peoples R China
Abstract
Abstract
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.
Subject
Endocrinology,General Medicine,Endocrinology, Diabetes and Metabolism,Internal Medicine