Kristine E Lynch1, Sunni L Mumford2, Karen C Schliep2, Brian W Whitcomb3, Shvetha M Zarek4, Anna Z Pollack5, Elizabeth R Bertone-Johnson3, Michelle Danaher2, Jean Wactawski-Wende6, Audrey J Gaskins7, Enrique F Schisterman8. 1. Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, US Department of Health & Human Services, Bethesda, Maryland; Department of Public Health and Nursing, Westminster College, Salt Lake City, Utah. 2. Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, US Department of Health & Human Services, Bethesda, Maryland. 3. Division of Biostatistics and Epidemiology, University of Massachusetts School of Public Health and Health Sciences, Amherst, Massachusetts. 4. Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, US Department of Health & Human Services, Bethesda, Maryland; Program of Reproductive and Adult Endocrinology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, US Department of Health & Human Services, Bethesda, Maryland. 5. Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, US Department of Health & Human Services, Bethesda, Maryland; Department of Global and Community Health, George Mason University, Fairfax, Virginia. 6. Department of Social and Preventive Medicine, University at Buffalo, Buffalo, New York. 7. Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, US Department of Health & Human Services, Bethesda, Maryland; Department of Nutrition and Epidemiology, Harvard School of Public Health, Boston, Massachusetts. 8. Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, US Department of Health & Human Services, Bethesda, Maryland. Electronic address: schistee@mail.nih.gov.
Abstract
OBJECTIVE: To compare previously used algorithms to identify anovulatory menstrual cycles in women self-reporting regular menses. DESIGN: Prospective cohort study. SETTING: Western New York. PATIENT(S): Two hundred fifty-nine healthy, regularly menstruating women followed for one (n=9) or two (n=250) menstrual cycles (2005-2007). INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Prevalence of sporadic anovulatory cycles identified using 11 previously defined algorithms that use E2, P, and LH concentrations. RESULT(S): Algorithms based on serum LH, E2, and P levels detected a prevalence of anovulation across the study period of 5.5%-12.8% (concordant classification for 91.7%-97.4% of cycles). The prevalence of anovulatory cycles varied from 3.4% to 18.6% using algorithms based on urinary LH alone or with the primary E2 metabolite, estrone-3-glucuronide, levels. CONCLUSION(S): The prevalence of anovulatory cycles among healthy women varied by algorithm. Mid-cycle LH surge urine-based algorithms used in over-the-counter fertility monitors tended to classify a higher proportion of anovulatory cycles compared with luteal-phase P serum-based algorithms. Our study demonstrates that algorithms based on the LH surge, or in conjunction with estrone-3-glucuronide, potentially estimate a higher percentage of anovulatory episodes. Addition of measurements of postovulatory serum P or urine pregnanediol may aid in detecting ovulation. Published by Elsevier Inc.
OBJECTIVE: To compare previously used algorithms to identify anovulatory menstrual cycles in women self-reporting regular menses. DESIGN: Prospective cohort study. SETTING: Western New York. PATIENT(S): Two hundred fifty-nine healthy, regularly menstruating women followed for one (n=9) or two (n=250) menstrual cycles (2005-2007). INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Prevalence of sporadic anovulatory cycles identified using 11 previously defined algorithms that use E2, P, and LH concentrations. RESULT(S): Algorithms based on serum LH, E2, and P levels detected a prevalence of anovulation across the study period of 5.5%-12.8% (concordant classification for 91.7%-97.4% of cycles). The prevalence of anovulatory cycles varied from 3.4% to 18.6% using algorithms based on urinary LH alone or with the primary E2 metabolite, estrone-3-glucuronide, levels. CONCLUSION(S): The prevalence of anovulatory cycles among healthy women varied by algorithm. Mid-cycle LH surge urine-based algorithms used in over-the-counter fertility monitors tended to classify a higher proportion of anovulatory cycles compared with luteal-phase P serum-based algorithms. Our study demonstrates that algorithms based on the LH surge, or in conjunction with estrone-3-glucuronide, potentially estimate a higher percentage of anovulatory episodes. Addition of measurements of postovulatory serum P or urine pregnanediol may aid in detecting ovulation. Published by Elsevier Inc.
Authors: K A O'Connor; E Brindle; R C Miller; J B Shofer; R J Ferrell; N A Klein; M R Soules; D J Holman; P K Mansfield; J W Wood Journal: Hum Reprod Date: 2006-01-26 Impact factor: 6.918
Authors: N Santoro; S L Crawford; J E Allsworth; E B Gold; G A Greendale; S Korenman; B L Lasley; D McConnell; P McGaffigan; R Midgely; M Schocken; M Sowers; G Weiss Journal: Am J Physiol Endocrinol Metab Date: 2002-11-19 Impact factor: 4.310
Authors: Rose G Radin; Lindsey A Sjaarda; Robert M Silver; Carrie J Nobles; Sunni L Mumford; Neil J Perkins; Brian D Wilcox; Anna Z Pollack; Karen C Schliep; Torie C Plowden; Enrique F Schisterman Journal: Fertil Steril Date: 2018-01-06 Impact factor: 7.329
Authors: Keewan Kim; James L Mills; Kara A Michels; Ellen N Chaljub; Jean Wactawski-Wende; Torie C Plowden; Sunni L Mumford Journal: J Acad Nutr Diet Date: 2019-12-23 Impact factor: 4.910
Authors: Anna M Gorczyca; Lindsey A Sjaarda; Emily M Mitchell; Neil J Perkins; Karen C Schliep; Jean Wactawski-Wende; Sunni L Mumford Journal: Eur J Nutr Date: 2015-06-05 Impact factor: 5.614
Authors: Kara A Michels; Pauline Mendola; Karen C Schliep; Edwina H Yeung; Aijun Ye; Galit L Dunietz; Jean Wactawski-Wende; Keewan Kim; Joshua R Freeman; Enrique F Schisterman; Sunni L Mumford Journal: Chronobiol Int Date: 2019-11-28 Impact factor: 2.877
Authors: Rose G Radin; Lindsey A Sjaarda; Neil J Perkins; Robert M Silver; Zhen Chen; Laurie L Lesher; Noya Galai; Jean Wactawski-Wende; Sunni L Mumford; Enrique F Schisterman Journal: J Clin Endocrinol Metab Date: 2017-01-01 Impact factor: 5.958
Authors: Anna Z Pollack; Neil J Perkins; Lindsey Sjaarda; Sunni L Mumford; Kurunthachalam Kannan; Claire Philippat; Jean Wactawski-Wende; Enrique F Schisterman Journal: Environ Res Date: 2016-08-25 Impact factor: 6.498
Authors: Karen C Schliep; Shvetha M Zarek; Enrique F Schisterman; Jean Wactawski-Wende; Maurizio Trevisan; Lindsey A Sjaarda; Neil J Perkins; Sunni L Mumford Journal: Am J Clin Nutr Date: 2015-08-19 Impact factor: 7.045
Authors: Karen C Schliep; Sunni L Mumford; Catherine J Vladutiu; Katherine A Ahrens; Neil J Perkins; Lindsey A Sjaarda; Kerri A Kissell; Ankita Prasad; Jean Wactawski-Wende; Enrique F Schisterman Journal: Epidemiology Date: 2015-03 Impact factor: 4.822
Authors: Kara A Michels; Jean Wactawski-Wende; James L Mills; Karen C Schliep; Audrey J Gaskins; Edwina H Yeung; Keewan Kim; Torie C Plowden; Lindsey A Sjaarda; Ellen N Chaljub; Sunni L Mumford Journal: Hum Reprod Date: 2017-08-01 Impact factor: 6.918