scholarly journals Changes in Concentration of Creatine Kinase, Body Composition and Lipoprotein during Menstrual Cycle

2018 ◽  
Vol 2 (77) ◽  
Author(s):  
Laura Daniusevičiūtė ◽  
Marius Brazaitis ◽  
Albertas Skurvydas ◽  
Saulė Sipavičienė ◽  
Vitas Linonis ◽  
...  

The aim of our study was to establish the dependence of changes in the concentration of creatine kinase, body composition and lipoprotein on the follicular phase and ovulation. The subjects were healthy and physically active women (n = 9) with normal menstrual cycle, whose age was 19—23 years, body weight — 58.2 ± 6.1 kg, height — 168.4 ± 5.6 cm. All the participants had not used oral contraceptives for 6 months and had regular menstrual cycles. Ethical approval was obtained from Kaunas Regional Biomedical Research Ethics Committee (Report Number BE-2-24). Each subject measured her basal body temperature every morning 3 months before the experiment. The basal body temperature increased approximately by 0.3 °C after ovulation, which is sustained throughout the luteal phase. By the basal body temperature we estimated the approximate day of ovulation, and thus the relative length of follicular and luteal phases. We performed two experiments with each participant: in the follicular phase and ovulation. The days of experiment were chosen considering the duration of the menstrual cycle and the ovulation day of each participant. At the beginning of every experiment the body composition values: weight, BMI, body fat mass (%), body fat mass (kg), muscle mass (kg), water amount (kg) were estimated. The samples of 5 ml and 2 ml venum blood were taken toestablish the amount of estradio17β-estradiol, serum total cholesterol, high density lipoprotein cholesterol, triglyceride and creatine kinase concentration. Creatine kinase concentration was measured 24, 48, 72 hours after the load (100 jumps on the vertical jump force plate from a 75 cm stage). After 10—15 min of not intensive warming-up (slow pedaling veloergometer, heart rate 120—130 b / min) 100 jumps on the vertical jump force plate from a 75 cm stage were performed, with the knee joints flexed up to the angle of 90° (hands on loin). Hormonal analysis confirmed that the subjects were in the correct estrogen status, but no significant change was observed in the body composition and triglyceride values over the menstrual cycle. High density lipoprotein cholesterol and serum total cholesterol values significantly differed in ovulation compared to the values in the follicular phase. Due to the small sample size CK concentration did not significantly differ during the menstrual cycle, but the amount of CK concentration was lager in the follicular phase than in ovulation.Keywords: lipoprotein contentration, creatin kinase, body composition, follicular phase, ovulation.

2021 ◽  
Vol 17 ◽  
pp. 174550652110499
Author(s):  
Lauren Worsfold ◽  
Lorrae Marriott ◽  
Sarah Johnson ◽  
Joyce C Harper

Background: Period tracking applications (apps) allow women to track their menstrual cycles and receive a prediction for their period dates. The majority of apps also provide predictions of ovulation day and the fertile window. Research indicates apps are basing predictions on assuming women undergo a textbook 28-day cycle with ovulation occurring on day 14 and a fertile window between days 10 and 16. Objective: To determine how the information period tracker apps give women on their period dates, ovulation day and fertile window compares to expected results from big data. Methods: Five women’s profiles for 6 menstrual cycles were created and entered into 10 apps. Cycle length and ovulation day for the sixth cycle were Woman 1—Constant 28 day cycle length, ovulation day 16; Woman 2—Average 23 day cycle length, ovulation day 13; Woman 3—Average 28 day cycle length, ovulation day 17; Woman 4—Average 33 day cycle length, ovulation day 20; and Woman 5—Irregular, average 31 day cycle length, ovulation day 14. Results: The 10 period tracker apps examined gave conflicting information on period dates, ovulation day and the fertile window. For cycle length, the apps all predicted woman 1’s cycles correctly but for women 2–5, the apps predicted 0 to 8 days shorter or longer than expected. For day of ovulation, for women 1–4, of the 36 predictions, 3 (8%) were exactly correct, 9 predicted 1 day too early (25%) and 67% of predictions were 2–9 days early. For woman 5, most of the apps predicted a later day of ovulation. Conclusion: Period tracker apps should ensure they only give women accurate information, especially for the day of ovulation and the fertile window which can only be predicted if using a marker of ovulation, such as basal body temperature, ovulation sticks or cervical mucus.


2002 ◽  
Vol 92 (4) ◽  
pp. 1684-1691 ◽  
Author(s):  
Fiona C. Baker ◽  
Helen S. Driver ◽  
Janice Paiker ◽  
Geoffrey G. Rogers ◽  
Duncan Mitchell

Body temperature and sleep change in association with increased progesterone in the luteal phase of the menstrual cycle in young women. The mechanism by which progesterone raises body temperature is not known but may involve prostaglandins, inducing a thermoregulatory adjustment similar to that of fever. Prostaglandins also are involved in sleep regulation and potentially could mediate changes in sleep during the menstrual cycle. We investigated the possible role of central prostaglandins in mediating menstrual-associated 24-h temperature and sleep changes by inhibiting prostaglandin synthesis with a therapeutic dose of the centrally acting cyclooxygenase inhibitor acetaminophen in the luteal and follicular phases of the menstrual cycle in young women. Body temperature was raised, and nocturnal amplitude was blunted, in the luteal phase compared with the follicular phase. Acetaminophen had no effect on the body temperature profile in either menstrual cycle phase. Prostaglandins, therefore, are unlikely to mediate the upward shift of body temperature in the luteal phase. Sleep changed during the menstrual cycle: on the placebo night in the luteal phase the women had less rapid eye movement sleep and more slow-wave sleep than in the follicular phase. Acetaminophen did not alter sleep architecture or subjective sleep quality. Prostaglandin inhibition with acetaminophen, therefore, had no effect on the increase in body temperature or on sleep in the midluteal phase of the menstrual cycle in young women, making it unlikely that central prostaglandin synthesis underlies these luteal events.


1972 ◽  
Vol 54 (1) ◽  
pp. 119-123 ◽  
Author(s):  
J. WATSON

SUMMARY Plasma levels of luteinizing hormone (LH) in normally cyclic women during the menstrual cycle and in rats during the oestrous cycle were measured by bioassay. With the human subjects, it was possible to establish a mid-cycle peak of LH and correlate it with basal body temperature, while with the rats a peak of LH secretion was noted between 15.00 and 19.00 h on the day of pro-oestrus. The levels of LH in both groups of subjects were of the same order as those measured by other assay techniques.


1976 ◽  
Vol 81 (2) ◽  
pp. 548-562 ◽  
Author(s):  
Marijke Frölich ◽  
Egenius C. Brand ◽  
Eylard V. van Hall

ABSTRACT The results of daily determination of the levels of gonadotrophins, oestradiol, oestrone, progesterone, aldosterone, dehydroepiandrosterone, androstenedione, testosterone, and aetiocholanolone in the serum of 6 normal, ovulating women are reported and discussed. A pre-ovulatory aldosterone peak and rising values in the luteal phase of the cycle were found. Androstenedione, testosterone, and aetiocholanolone levels were significantly elevated from 3 days before until 3 days after ovulation. Since the mean androstenedione/aetiocholanolone ratio in the individual cycles in this period was similar to the ratio found during the rest of the cycle, we think it unlikely that aetiocholanolone is produced by the ovaries. No correlation was found between the aetiocholanolone patterns and the basal body temperature. In a case of conception followed for 20 days after ovulation, the steroid patterns remained unchanged until the presumed day of implantation, after which the aldosterone, androstenedione, testosterone, and aetiocholanolone levels started to rise. The mean androstenedione/aetiocholanolone ratio during the 10 days after implantation did not differ from the values obtained in the foregoing periods, so direct aetiocholanolone production by the ovaries after implantation seems unlikely.


1970 ◽  
Vol 2 (2) ◽  
pp. 123-132 ◽  
Author(s):  
Judith Bailey ◽  
John Marshall

SummaryThe basal body temperature was recorded by 1353 healthy fertile women aged 18 to 49 years inclusive through 12,247 cycles. The post-ovulatory (hyperthermic) phase of the cycle was significantly longer by 1·31 days in cycles with a slow or staircase rise of temperature than in cycles with an acute rise. The hyperthermic phase increased in length in a rectilinear fashion from 10 to 13 days as the total cycle length rose from 22 to 29 days; over total cycle lengths from 29 to 33 days the length of the hyperthermic phase remained around 13 days.


2020 ◽  
Vol 35 (10) ◽  
pp. 2245-2252
Author(s):  
Joseph B Stanford ◽  
Sydney K Willis ◽  
Elizabeth E Hatch ◽  
Kenneth J Rothman ◽  
Lauren A Wise

Abstract STUDY QUESTION To what extent does the use of mobile computing apps to track the menstrual cycle and the fertile window influence fecundability among women trying to conceive? SUMMARY ANSWER After adjusting for potential confounders, use of any of several different apps was associated with increased fecundability ranging from 12% to 20% per cycle of attempt. WHAT IS KNOWN ALREADY Many women are using mobile computing apps to track their menstrual cycle and the fertile window, including while trying to conceive. STUDY DESIGN, SIZE, DURATION The Pregnancy Study Online (PRESTO) is a North American prospective internet-based cohort of women who are aged 21–45 years, trying to conceive and not using contraception or fertility treatment at baseline. PARTICIPANTS/MATERIALS, SETTING, METHODS We restricted the analysis to 8363 women trying to conceive for no more than 6 months at baseline; the women were recruited from June 2013 through May 2019. Women completed questionnaires at baseline and every 2 months for up to 1 year. The main outcome was fecundability, i.e. the per-cycle probability of conception, which we assessed using self-reported data on time to pregnancy (confirmed by positive home pregnancy test) in menstrual cycles. On the baseline and follow-up questionnaires, women reported whether they used mobile computing apps to track their menstrual cycles (‘cycle apps’) and, if so, which one(s). We estimated fecundability ratios (FRs) for the use of cycle apps, adjusted for female age, race/ethnicity, prior pregnancy, BMI, income, current smoking, education, partner education, caffeine intake, use of hormonal contraceptives as the last method of contraception, hours of sleep per night, cycle regularity, use of prenatal supplements, marital status, intercourse frequency and history of subfertility. We also examined the impact of concurrent use of fertility indicators: basal body temperature, cervical fluid, cervix position and/or urine LH. MAIN RESULTS AND THE ROLE OF CHANCE Among 8363 women, 6077 (72.7%) were using one or more cycle apps at baseline. A total of 122 separate apps were reported by women. We designated five of these apps before analysis as more likely to be effective (Clue, Fertility Friend, Glow, Kindara, Ovia; hereafter referred to as ‘selected apps’). The use of any app at baseline was associated with 20% increased fecundability, with little difference between selected apps versus other apps (selected apps FR (95% CI): 1.20 (1.13, 1.28); all other apps 1.21 (1.13, 1.30)). In time-varying analyses, cycle app use was associated with 12–15% increased fecundability (selected apps FR (95% CI): 1.12 (1.04, 1.21); all other apps 1.15 (1.07, 1.24)). When apps were used at baseline with one or more fertility indicators, there was higher fecundability than without fertility indicators (selected apps with indicators FR (95% CI): 1.23 (1.14, 1.34) versus without indicators 1.17 (1.05, 1.30); other apps with indicators 1.30 (1.19, 1.43) versus without indicators 1.16 (1.06, 1.27)). In time-varying analyses, results were similar when stratified by time trying at study entry (<3 vs. 3–6 cycles) or cycle regularity. For use of the selected apps, we observed higher fecundability among women with a history of subfertility: FR 1.33 (1.05–1.67). LIMITATIONS, REASONS FOR CAUTION Neither regularity nor intensity of app use was ascertained. The prospective time-varying assessment of app use was based on questionnaires completed every 2 months, which would not capture more frequent changes. Intercourse frequency was also reported retrospectively and we do not have data on timing of intercourse relative to the fertile window. Although we controlled for a wide range of covariates, we cannot exclude the possibility of residual confounding (e.g. choosing to use an app in this observational study may be a marker for unmeasured health habits promoting fecundability). Half of the women in the study received a free premium subscription for one of the apps (Fertility Friend), which may have increased the overall prevalence of app use in the time-varying analyses, but would not affect app use at baseline. Most women in the study were college educated, which may limit application of results to other populations. WIDER IMPLICATIONS OF THE FINDINGS Use of a cycle app, especially in combination with observation of one or more fertility indicators (basal body temperature, cervical fluid, cervix position and/or urine LH), may increase fecundability (per-cycle pregnancy probability) by about 12–20% for couples trying to conceive. We did not find consistent evidence of improved fecundability resulting from use of one specific app over another. STUDY FUNDING/COMPETING INTEREST(S) This research was supported by grants, R21HD072326 and R01HD086742, from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, USA. In the last 3 years, Dr L.A.W. has served as a fibroid consultant for AbbVie.com. Dr L.A.W. has also received in-kind donations from Sandstone Diagnostics, Swiss Precision Diagnostics, FertilityFriend.com and Kindara.com for primary data collection and participant incentives in the PRESTO cohort. Dr J.B.S. reports personal fees from Swiss Precision Diagnostics, outside the submitted work. The remaining authors have nothing to declare. TRIAL REGISTRATION NUMBER N/A.


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