Latent Profile Analysis for Classification of Psychosomatic Symptoms in Perimenopausal Women
Na Yeon Kim1, Seo-Koo Yoo2, Jae-Chan Jin2
1Department of Psychiatry, College of Medicine, Chung-Ang University, Seoul, Republic of Korea.
Background:
Over 80% of perimenopausal women experience various physical and neuropsychiatric symptoms due to changes in hormones. However, accurate evaluation and treatment of menopausal symptoms are lacking due to limited evidence regarding customized treatment for each symptom type.
Objective:
This study aimed to identify and cluster psychosomatic symptoms in Korean perimenopausal women and to investigate the characteristics of each cluster type.
Methods:
This Web-based survey assessed 1060 Korean perimenopausal women recruited from nationally representative panels available from a commercial research company. Menopausal symptoms were evaluated using the Menopause Rating Scale, Patient Health Questionnaire-15, Patient Health Questionnaire-9, Insomnia Severity Index, Subjective Memory Complaints Questionnaire, and Overactive Bladder Symptom Score. Data were analyzed using latent profile analysis and multinomial logistic regression.
Results:
Latent profile analysis identified 4 latent classes: "all unimpaired (class 1)," "impaired sexual function, while others are unimpaired (class 2)," "unimpaired sexual function, while others are impaired (class 3)," and "all impaired (class 4)." Classes 3 and 4 were significantly more likely to have a family history of perimenopausal syndrome and a history of other mental disorders, postpartum depression, and postpartum psychosis than classes 1 and 2.
Conclusions:
Psychosomatic symptoms appear during perimenopause along with depression, somatic symptoms, insomnia, impaired memory, and overactive bladder symptoms. Individuals with these symptoms are more likely to have a history of postpartum depression, psychosis, and other psychiatric disorders. Therefore, sexual dysfunction must be independently treated. Further studies are needed to develop and verify individualized treatments according to symptom class.
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