A model for predicting age at menopause in white women

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): Tell 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-clip-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.