Objective: To develop a model to predict the age at natural menopause and the risk for premenopausal hysterectomy.
Design: Cross-sectional study.
Setting: Multicenter study.
Patient(s): A total of 1,345 white women.
Intervention(s): Ten single nucleotide polymorphisms (SNPs) of seven estrogen (E)-metabolizing genes (i.e., catechol-O-methyltransferase, 17-beta-hydroxysteroid dehydrogenase type 1, cytochrome P-450 [CYP] 17, CYP1A1, CYP1B1, CYP19, and E receptor [ER] alpha) were analyzed by sequencing-on-chip-technology.
Main outcome measure(s): Patients' reproductive and medical histories were ascertained and correlated to genotypes.
Result(s): The model incorporates the number of full term pregnancies, the body mass index (BMI), a history of breast surgery, and the presence of CYP17 and CYP1B1-4 polymorphisms as well as the BMI to predict age at natural menopause and the risk for undergoing premenopausal hysterectomy.
Conclusion(s): We present the first model to date, which can predict age at natural menopause and the risk for undergoing premenopausal hysterectomy based on genotype information and personal history.