Does Molimina Indicate Ovulation? Prospective Data in a Hormonally Documented Single-Cycle in Spontaneously Menstruating Women.
Jerilynn C PriorChiaki KonishiChristine L HitchcockElaine KingwellPatti JanssenAnthony P CheungNichole FairbrotherAzita GoshtasebiPublished in: International journal of environmental research and public health (2018)
Approximately 33% of normal-length (21⁻35 days) cycles have subclinical ovulatory disturbances and lack sufficient progesterone, although their normal length ensures enough estrogen. Subclinical ovulatory disturbances are related to significant premenopausal spine bone loss (-0.86%/year). Molimina, non-distressing premenstrual experiences, may detect ovulation within normal-length cycles. This prospective study assessed the relationship between molimina and ovulation. After 1-cycle of daily diary and first morning urine collections, women answered the Molimina Question (MQ): "Can you tell by the way you feel that your period is coming?" and were invited to share (a) predictive premenstrual experience(s). A 3-fold increase in follicular-luteal pregnanediol levels confirmed ovulation. In 610 spontaneously menstruating women (not on hormonal contraception, mean age 31.5 ± 5.3, menarche age 12.7 ± 1.5, cycle length [CL] 29 days, MQ positive in 89%), reported premenstrual experiences which included negative moods (62%), cramps (48%), bloating (39%), and front (26%) or axillary (25%) breast tenderness. Of 432 women with pregnanediol-documented cycles, 398 (92%) were ovulatory (CL: 29 ± 5) and 34 (8%) had ovulatory disturbances (CL: 32 ± 14). Women with/without ovulatory cycles were similar in parity, body mass index, smoking, dietary restraint and the MQ; ovulatory-disturbed cycles were longer. Molimina did not confirm ovulation. A non-invasive, inexpensive ovulation indicator is needed to prevent osteoporosis.
Keyphrases
- polycystic ovary syndrome
- insulin resistance
- body mass index
- bone loss
- postmenopausal women
- mental health
- physical activity
- metabolic syndrome
- adipose tissue
- skeletal muscle
- smoking cessation
- estrogen receptor
- squamous cell carcinoma
- electronic health record
- machine learning
- artificial intelligence
- big data
- stress induced