Premenstrual syndrome (PMS) has a wide variety of symptoms. The classification of these symptoms into several patterns is useful for more effective tailor-made treatment. Therefore, our study aimed to examine the patterns of PMS by analyzing multiple factors to identify the characteristics of each pattern. This is a cross-sectional study. A total of 165 women (18.9 ± 1.0 years) were investigated by using of questionnaire about PMS, nutrition, physical activity, and other lifestyle traits. Then, the factor analysis was performed to classify the premenstrual symptoms, that is, the pattern of PMS. Additionally, logistic regression analysis was performed to identify the characteristics of each pattern, adjusted for age, body mass index, sleep duration, and caffeine intake. As the result, PMS was classified into 3 patterns. The type related to psychological symptoms such as depression, physiological symptoms, such as abdominal pain, and intermingled type, were labeled as affected, somatic, and mixed types, respectively. From the result of logistic regression analysis, self-rating depression scale scoring was marginally associated with affective type (odds ratio [OR]: 1.08, 95% confidence interval [CI]: 0.99-1.16), physical activity was significantly associated with the mixed type (OR: 1.13, 95% CI: 1.00-1.28), and physical activity (OR: 1.20, 95% CI: 1.05-1.36) and some nutrients (OR: 0.56-1.00) were significantly associated with the somatic type. Understanding PMS and management of these complicated symptoms has been difficult. From the results of this study, the complicated symptoms were categorized into simpler patterns. Our findings may contribute to the understanding and possible management adjusted for each categorized case of PMS.
Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc.