A Quadriparametric Model to Describe the Diversity of Waves Applied to Hormonal Data

Abdullah, Saman; Bouchard, Thomas; Klich, Amna; Leiva, René; Pyper, Cecilia; Génolini, Christophe; Subtil, Fabien; Iwaz, Jean; Écochard, René

doi:10.3414/me17-01-0102

Cited by 2 publications

(1 citation statement)

References 20 publications

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“…Daily means, SDs, and CIs of hormone values across the study sample will be used to describe the variability in the population, as was done in the pilot study [ 34 ], and individual (personalized) hormone patterns will also be considered as we have presented in previous studies [ 51 ]. Day-to-day hormone variability will require complex modelling of hormonal patterns, which we have used in previous studies [ 53 ], using R -software ( R Foundation for Statistical Computing, Vienna, Austria). The specific statistical methods involve creating best-fit distributions for wave patterns of the hormones using the mean square of residuals as the loss function, with the goal to minimize the loss function to improve the fit of the distribution based on the density of the hormone wave patterns in the population; this specific method has helped to identify distinct hormone patterns that may represent different physiologic mechanisms, but it must be emphasized that these are hypothesis-generating rather than primary endpoints of this proposal.…”

Section: Results (Planned)mentioning

confidence: 99%

Establishing a Gold Standard for Quantitative Menstrual Cycle Monitoring

Bouchard,

Yong,

Doyle-Baker

2023

Medicina

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Background and Objectives: The Quantum Menstrual Health Monitoring Study will measure four key reproductive hormones in the urine (follicle-stimulating hormone, FSH; estrone-3-glucuronide, E13G; luteinizing hormone, LH; and pregnanediol glucuronide, PDG) to characterize patterns that predict and confirm ovulation, referenced to serum hormones and the gold standard of the ultrasound day of ovulation in participants with regular cycles. These normal cycles will provide a reference for comparison to irregular cycles in subjects with polycystic ovarian syndrome (PCOS) and athletes. Materials and Methods: Participants will track their menstrual cycles for 3 months and be provided with an at-home urine hormone monitor (Mira monitor) to predict ovulation. The day of ovulation will be confirmed with serial ultrasounds completed in a community clinic. Urine results will be compared to serum hormone values. Other markers of menstrual health, such as bleeding patterns and temperature changes, will be determined using a customized app. Three groups will be recruited. Group 1 will include those with consistent regular cycle lengths (between 24–38 days), and will be compared to two groups with irregular cycle lengths (with increased cycle length variability and longer cycles). Group 2 will include those with polycystic ovarian syndrome (PCOS) with irregular cycles and Group 3 will include individuals participating in high levels of exercise with irregular cycles. Hypothesis: The Mira monitor quantitative urine hormone pattern will accurately correlate with serum hormonal levels and will predict (with LH) and confirm (with PDG) the ultrasound day of ovulation in those with regular cycles as well as those with irregular cycles. Rationale: Once the ultrasound validation is complete, tools like the Mira monitor with a customized app may become a new standard for at-home and remote clinical monitoring of the menstrual cycle without having to use labor-intensive follicular-tracking ultrasound or follow serum hormone changes. Conclusions: Precision monitoring of the menstrual cycle is expected to impact individuals who want to increase their menstrual health literacy and guide decisions about fertility.

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Section: Results (Planned)mentioning

confidence: 99%

Establishing a Gold Standard for Quantitative Menstrual Cycle Monitoring

Bouchard,

Yong,

Doyle-Baker

2023

Medicina

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Distinct urinary progesterone metabolite profiles during the luteal phase

Abdullah

Bouchard

Leiva

et al. 2022

Hormone Molecular Biology and Clinical Investigation

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Objectives During normal menstrual cycles, serum levels of progesterone vary widely between cycles of same woman and between women. This study investigated the profiles of pregnanediol during the luteal phase. Methods Data stemmed from a previous multicenter prospective observational study and concerned 107 women (who contributed 326 menstrual cycles). The study analyzed changes in observed cervical mucus discharge, various hormones in first morning urine, and serum progesterone. Transvaginal ultrasonography and cervical mucus helped identifying the day of ovulation. Changes in pregnanediol glucuronide levels during the luteal phase were examined and classified according to the length of that phase, a location parameter, and a scale parameter. Associations between nine pregnanediol glucuronide profiles and other hormone profiles were examined. Results Low periovulatory pregnanediol glucuronide levels and low periovulatory luteinizing hormone levels were associated with delayed increases in pregnanediol glucuronide after ovulation. That ‘delayed increase profile’ was more frequently associated with cycles with prolonged high LH levels than in cycles with rapid pregnanediol glucuronide increases. A ‘plateau-like profile’ during the luteal phase was associated with longer cycles, cycles with higher estrone-3-glucuronide and pregnanediol glucuronide during the preovulatory phase, and cycles with higher periovulatory pregnanediol glucuronide levels. Conclusions Distinct profiles of urinary progesterone levels are displayed during the luteal phase. These profiles relate to early hormone changes during the menstrual cycle. In everyday clinical practice, these findings provide further evidence for recommending progesterone test seven days after the mucus peak day. The search for other correlations and associations is underway.

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A Quadriparametric Model to Describe the Diversity of Waves Applied to Hormonal Data

Cited by 2 publications

References 20 publications

Establishing a Gold Standard for Quantitative Menstrual Cycle Monitoring

Establishing a Gold Standard for Quantitative Menstrual Cycle Monitoring

Distinct urinary progesterone metabolite profiles during the luteal phase

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