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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- methylt- cytochrome P- 450 [CYP] 17, CYP1A1, CYP1B1, CYP19, and E receptor [ER]- α ) 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.
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 (ie, catechol- O-methylt- cytochrome P- 450 [CYP] 17, CYP1A1, CYP1B1, CYP19, and E receptor [ER] Results (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 under going premenopausal hysterectomy based on genotype information and personal history.