A model for predicting age at menopause in white women

Hefler, Lukas A.; Grimm, Christoph; Bentz, Eva-Katrin; Reinthaller, Alexander; Heinze, Georg; Tempfer, Clemens B.

doi:10.1016/j.fertnstert.2005.07.1300

« Previous Next »Fertility and Sterility
Volume 85, Issue 2 , Pages 451-454, February 2006

A model for predicting age at menopause in white women

Lukas A. Hefler, M.D.
- Department of Obstetrics and Gynecology, Medical University of Vienna, Vienna, Austria
- Reprints requests: Lukas Hefler, M.D., Department of Obstetrics and Gynecology, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria (FAX: 43-1-40400-2911).
Christoph Grimm, M.D.
- Department of Obstetrics and Gynecology, Medical University of Vienna, Vienna, Austria
Eva-Katrin Bentz, M.D.
- Department of Obstetrics and Gynecology, Medical University of Vienna, Vienna, Austria
Alexander Reinthaller, M.D.
- Department of Obstetrics and Gynecology, Medical University of Vienna, Vienna, Austria
Georg Heinze, Ph.D.
- Core Unit for Medical Statistics and Informatics, Medical University of Vienna, Vienna, Austria
Clemens B. Tempfer, M.D.
- Department of Obstetrics and Gynecology, Medical University of Vienna, Vienna, Austria

Received 9 April 2005; received in revised form 19 July 2005; accepted 19 July 2005.

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-β-hydroxysteroid dehydrogenase type 1, 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.

Key Words: Menopause , hysterectomy , gene , polymorphism , age , timing , estrogen