Identification of Cold-Heat Symptom Profiles using Latent Profile Analysis and Their Association with Mental Health

Objectives: Cold-heat pattern identification is a core diagnostic framework in Korean medicine and has been linked to mental health. This study aimed to identify latent subtypes of cold-heat patterns using latent profile analysis (LPA) and to examine their associations with mental health outcomes.Methods: LPA was conducted separately for women and men based on cold-heat symptom responses. Sensitivity analysis using K-means clustering was performed, and agreement between methods was assessed using the adjusted Rand index (ARI). Associations between identified profiles and mental health indicators—including depression-related disorders, Mibyeong (subthreshold) depression, anxiety, anger, and mental component summary (MCS) scores—were evaluated. Heart rate variability (HRV) indices were also analyzed.Results: Four profiles were identified in women (cold-dominant, heat-dominant, mixed sensation, and low-symptom profiles) and three in men (cold-dominant, heat-dominant, and low-symptom profiles). Clustering results showed good agreement between LPA and K-means (ARI=0.74 in women, 0.80 in men). In women, the mixed sensation profile showed higher risks of depression-related disorders, Mibyeong depression, and anxiety. The heat-dominant profile showed elevated risks of Mibyeong depression and anxiety and lower MCS scores, while the cold-dominant profile showed increased Mibyeong anxiety. In men, the heat-dominant profile showed higher risks of Mibyeong anger and anxiety and lower MCS scores. HRV analysis indicated autonomic imbalance in women with mixed sensation and heat-dominant profiles.Conclusions: Data-driven cold-heat pattern subtypes were differentially associated with mental health, supporting the utility of LPA-based classification in understanding their psychological and physiological characteristics.