Background: Traditional Chinese medicine (TCM) has certain advantages in treating diabetes via TCM syndromes differentiation, and health-related behaviors can regulate TCM syndromes. This study aimed to identify the clusters of TCM syndromes in type 2 diabetes mellitus (T2DM) patients and to explore the association between health-related behaviors and those TCM syndromes clusters.
Methods: This was a cross-sectional study of 1761 T2DM patients from the Ningxia Province. The TCM syndromes (11 TCM syndromes in total) scale was used to collect the syndrome information. Health-related behaviors, including smoking, alcohol use, tea drinking, the intensity of physical activity, sleep quality, and sleep duration, were collected via a face-to-face interview questionnaire. Latent profile analysis was employed to identify clusters of 11 TCM syndromes. Multinomial logistic regression was employed to examine the relationships between health-related behaviors and clusters of TCM syndromes.
Results: TCM syndromes in T2DM patients were classified into three profiles using latent profile analysis: light, moderate, and heavy. Participants with poor health-related behaviors were more likely to have heavy 1.49 (95% CI: 1.12, 1.99) or moderate 1.75 (95% CI: 1.10, 2.79) profiles than those with good health-related habits. Smokers, tea drinkers, and those with poor sleep quality were more likely to have a moderate profile and heavy profile than a light profile. Compared with heavy physical activity, moderate activity 0.24 (95% CI: 0.07, 0.88) was negatively associated with a heavy profile.
Conclusion: Results showed that most participants had light or moderate levels of TCM syndromes, and those with poor health-related behaviors were more likely to have heavy or moderate profiles. In the context of precision medicine, these results have important implications for understanding the prevention and treatment of diabetes via changing lifestyles and behaviors to regulate TCM syndromes.
Keywords: health-related behaviors, traditional Chinese medicine syndromes, latent profile analysis, T2DM patients