Menstrual Cycle Heat Maps: Visualising menstrual cycle variability using hormone heat map arrays referenced to the ultrasound day of ovulation
DOI:
https://doi.org/10.63264/qk8aw674Keywords:
menstrual cycle, luteinizing hormone, estrone-3-glucuronide, pregnanediol-3-glucuronide, ovulation, heat mapAbstract
Objective: There is considerable individual day-to-day variation within the menstrual cycle and between cycles in women. Average hormone curves inadequately describe the individual hormone patterns experienced by women. The present study applies a novel application of a statistical array (heat map) to demonstrate both individual and group menstrual cycle hormone variability.
Design: Using pre-existing datasets, two cohorts of women were analysed using a statistical method to visualise quantitative hormonal variation.
Subjects: In one cohort, 107 women contributed a total of 283 menstrual cycles and in the second cohort, 21 women contributed a total of 62 menstrual cycles.
Exposure: Women collected first morning urine samples for analysis of estrone-3-glucuronide (E1G) and luteinizing hormone (LH) in both datasets. In the larger dataset, pregnanediol-3-alpha-glucuronide (PDG) and follicle-stimulating hormone (FSH) were also collected. Serial ultrasounds identified the precise day of ovulation in the larger dataset. In the smaller dataset, peak LH was used to identify the estimated day of ovulation.
Outcome measure: The main outcome measure was identifying hormonal variability using hormone array heat maps.
Conclusion: Heat maps were able to quickly show clustering of hormone patterns in the fertile window and on the day of ovulation. Individual differences were identified in rows on the heat map relative to the day of ovulation. This new tool to visually represent hormonal changes with heat maps identifies both individual and group variability of menstrual cycle hormones.
References
1. Brown JB. Types of ovarian activity in women and their significance: the continuum (a reinterpretation of early findings). Hum Reprod Update. 2011 Mar-Apr;17(2):141-58. doi: 10.1093/humupd/dmq040. Epub 2010 Oct 5.
2. Blackwell LF, Vigil P, Gross B, d'Arcangues C, Cooke DG, Brown JB. Monitoring of ovarian activity by measurement of urinary excretion rates of estrone glucuronide and pregnanediol glucuronide using the Ovarian Monitor, Part II: reliability of home testing. Hum Reprod. 2012 Feb;27(2):550-7. doi: 10.1093/humrep/der409
3. Schliep KC, Mumford SL, Hammoud AO, Stanford JB, Kissell KA, Sjaarda LA, Perkins NJ, Ahrens KA, Wactawski-Wende J, Mendola P, Schisterman EF. Luteal phase deficiency in regularly menstruating women: prevalence and overlap in identification based on clinical and biochemical diagnostic criteria. J Clin Endocrinol Metab. 2014 Jun;99(6):E1007-14. doi: 10.1210/jc.2013-3534
4. Alliende ME. Mean versus individual hormonal profiles in the menstrual cycle. Fertil Steril. 2002 Jul;78(1):90-5. doi: 10.1016/s0015-0282(02)03167-9
5. Park SJ, Goldsmith LT, Skurnick JH, Wojtczuk A, Weiss G. Characteristics of the urinary luteinizing hormone surge in young ovulatory women. Fertil Steril. 2007 Sep;88(3):684-90. doi: 10.1016/j.fertnstert.2007.01.045
6. Ecochard R, Bouchard T, Leiva R, Abdulla S, Dupuis O, Duterque O, Garmier Billard M, Boehringer H, Genolini C. Characterization of hormonal profiles during the luteal phase in regularly menstruating women. Fertil Steril. 2017 Jul;108(1):175-182.e1. doi: 10.1016/j.fertnstert.2017.05.012
7. Bouchard TP, Fehring RJ, Mu Q. Quantitative Versus Qualitative Estrogen and Luteinizing Hormone Testing for Personal Fertility Monitoring. Expert Rev Mol Diagn 2021;21:1349–60. doi: 10.1080/14737159.2021.2000393
8. Pattnaik S, Das D, Venkatesan VA, Rai A. Predicting serum hormone concentration by estimation of urinary hormones through a home-use device. Hum Reproduction Open 2022;2023:hoac058. doi: 10.1093/hropen/hoac058
9. Wegrzynowicz AK, Beckley A, Eyvazzadeh A, Levy G, Park J, Klein J. Complete Cycle Mapping Using a Quantitative At-Home Hormone Monitoring System in Prediction of Fertile Days, Confirmation of Ovulation, and Screening for Ovulation Issues Preventing Conception. Medicina (Kaunas). 2022 Dec 15;58(12):1853. doi: 10.3390/medicina58121853
10. Cromack SC, Walter JR. Consumer wearables and personal devices for tracking the fertile window. Am J Obstet Gynecol. 2024 Nov;231(5):516-523. doi: 10.1016/j.ajog.2024.05.028.
11. Hills E, Woodland MB, Divaraniya A. Using Hormone Data and Age to Pinpoint Cycle Day within the Menstrual Cycle. Medicina (Kaunas). 2023 Jul 23;59(7):1348. doi: 10.3390/medicina59071348
12. Abdullah S, Bouchard T, Leiva R, Boehringer H, Iwaz J, Ecochard R. Distinct urinary progesterone metabolite profiles during the luteal phase. Horm Mol Biol Clin Investig. 2022 Dec 30;44(2):137-144. doi: 10.1515/hmbci-2022-0065
13. Direito A, Bailly S, Mariani A, Ecochard R. Relationships between the luteinizing hormone surge and other characteristics of the menstrual cycle in normally ovulating women. Fertil Steril. 2013 Jan;99(1):279-285.e3. doi: 10.1016/j.fertnstert.2012.08.047
14. Abdullah S, Bouchard T, Klich A, Leiva R, Pyper C, Genolini C, et al. A Quadriparametric Model to Describe the Diversity of Waves Applied to Hormonal Data. Methods of Information in Medicine 2018;57:101–110. doi: 10.3414/ME17-01-0102
15. Huo Y, Ji S, Yang H, Wu W, Yu L, Ren Y, Wang F. Differential expression of microRNA in the serum of patients with polycystic ovary syndrome with insulin resistance. Ann Transl Med. 2022 Jul;10(14):762. doi: 10.21037/atm-22-2941
16. Gu Z, Hübschmann D. Make Interactive Complex Heatmaps in R. Bioinformatics. 2022 Feb 7;38(5):1460-1462. doi: 10.1093/bioinformatics/btab806
17. Lin W, Yang H, Lin J, Yang X, Liao Z, Zheng Y, Luo P, Liu C. OralExplorer: a web server for exploring the mechanisms of oral inflammatory diseases. J Transl Med. 2024 Mar 15;22(1):282. doi: 10.1186/s12967-024-05019-8
18. Dapas M, Dunaif A. Deconstructing a Syndrome: Genomic Insights Into PCOS Causal Mechanisms and Classification. Endocr Rev. 2022 Nov 25;43(6):927-965. doi: 10.1210/endrev/bnac001
19. Ecochard R, Boehringer H, Rabilloud M, Marret H. Chronological aspects of ultrasonic, hormonal, and other indirect indices of ovulation. BJOG. 2001 Aug;108(8):822-829. doi: 10.1111/j.1471-0528.2001.00194.x
20. Bouchard T, Yong P, Doyle-Baker P. Establishing a Gold Standard for Quantitative Menstrual Cycle Monitoring. Medicina (Kaunas). 2023 Aug 23;59(9):1513. doi: 10.3390/medicina59091513
21. Behre HM, Kuhlage J, Gassner C, Sonntag B, Schem C, Schneider HP, Nieschlag E. Prediction of ovulation by urinary hormone measurements with the home use ClearPlan Fertility Monitor: comparison with transvaginal ultrasound scans and serum hormone measurements. Hum Reprod. 2000 Dec;15(12):2478-82 doi: 10.1093/humrep/15.12.2478
22. Ecochard R, Guillerm A, Leiva R, Bouchard T, Direito A, Boehringer H. Characterization of follicle stimulating hormone profiles in normal ovulating women. Fertil Steril. 2014 Jul;102(1):237-243.e5. doi: 10.1016/j.fertnstert.2014.03.034
23. Wilcox AJ, Weinberg CR, Baird DD. Timing of sexual intercourse in relation to ovulation. Effects on the probability of conception, survival of the pregnancy, and sex of the baby. N Engl J Med. 1995 Dec 7;333(23):1517-21. doi: 10.1056/NEJM199512073332301
24. Usala SJ, Trindade AA. A Novel Fertility Indicator Equation Using Estradiol Levels for Assessment of Phase of the Menstrual Cycle. Medicina (Kaunas, Lithuania) 2020;56:555. doi: 10.3390/medicina56110555
25. Ecochard R, Duterque O, Leiva R, Bouchard T, Vigil P. Self-identification of the clinical fertile window and the ovulation period. Fertil Steril. 2015 May;103(5):1319-25.e3. doi: 10.1016/j.fertnstert.2015.01.031
26. Bouchard TP, Schweinsberg K, Smith A, Schneider M. Using Quantitative Hormone Monitoring to Identify the Postpartum Return of Fertility. Medicina (Kaunas). 2023 Nov 15;59(11):2008. doi: 10.3390/medicina59112008
27. Abdulla SH, Bouchard TP, Leiva RA, Boyle P, Iwaz J, Ecochard R. Hormonal Predictors of Abnormal Luteal Phases in Normally Cycling Women. Front Public Health. 2018 May 24;6:144. doi: 10.3389/fpubh.2018.00144
28. Baerwald AR, Adams GP, Pierson RA. Ovarian antral folliculogenesis during the human menstrual cycle: a review. Hum Reprod Update. 2012 Jan-Feb;18(1):73-91. doi: 10.1093/humupd/dmr039
29. Sharma A, López Y, Tsunoda T. Divisive hierarchical maximum likelihood clustering. BMC Bioinformatics. 2017 Dec 28;18(Suppl 16):546. doi: 10.1186/s12859-017-1965-5
30. Genolini C, Falissard B. KmL: a package to cluster longitudinal data. Comput Methods Programs Biomed. 2011 Dec;104(3):e112-21. doi: 10.1016/j.cmpb.2011.05.008
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