The relationship between menopausal symptoms and metabolic syndrome in postmenopausal women.
Hüseyin CengizCihan KayaIsmail AlayPublished in: Journal of obstetrics and gynaecology : the journal of the Institute of Obstetrics and Gynaecology (2019)
The most common symptoms during menopausal transition and menopause are vasomotor symptoms. We aimed to investigate the relationship between menopausal symptoms and metabolic syndrome (MetS) in postmenopausal women. Two hundred and fifty-four and 317 postmenopausal women were in the MetS and non-MetS groups, respectively. The total menopause rating scale and psychological subscale scores were higher in the MetS group than the non-MetS group, and the differences were significant (p < .05). A positive correlation was found between the abdominal circumferences, systolic-diastolic blood pressures, triglycerides and total MRS scores. However, a significant positive correlation was found between the abdominal circumference and total urogenital scores (p = .008). Impact statement What is already known on this subject? MetS and its dominant component (abdominal obesity) significantly increase the prevalence and severity of menopausal symptoms. Data regarding the relationship between metabolic syndrome (MetS) and vasomotor symptoms remain limited. What do the results of this study add? We showed that sleeping problems, depressive symptoms and bladder problems were more frequently encountered in the MetS group than in the non-MetS group (p<0.05). What are the implications of these findings for clinical practice and/or further research? There is a need for more randomised controlled studies to demonstrate the relationship between MetS and the severity of menopausal symptoms.
Keyphrases
- postmenopausal women
- metabolic syndrome
- bone mineral density
- sleep quality
- depressive symptoms
- insulin resistance
- mental health
- blood pressure
- clinical practice
- uric acid
- type diabetes
- clinical trial
- study protocol
- machine learning
- physical activity
- adipose tissue
- weight loss
- electronic health record
- atrial fibrillation
- artificial intelligence
- deep learning
- weight gain