Menstrual cycle length and modern living: a review.
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ABSTRACT: Purpose of review
The aim of this review is to evaluate biological, life history, environmental, and lifestyle factors and exposures that cause variability in menstrual cycle length (MCL).Recent findings
Recent literature has detailed a number of factors that influence MCL, with particular emphasis placed on novel environmental exposures, such as air pollution and endocrine disrupting chemicals.Summary
MCL varies widely in response to intrinsic and extrinsic inputs and is a useful predictor of reproductive health and fecundability.Video abstract
http://links.lww.com/COE/A28.
SUBMITTER: Campbell LR
PROVIDER: S-EPMC8631146 | biostudies-literature | 2021 Dec
REPOSITORIES: biostudies-literature
Publications
Menstrual cycle length and modern living: a review.
Campbell Lily R LR Scalise Ariel L AL DiBenedictis Brett T BT Mahalingaiah Shruthi S
Current opinion in endocrinology, diabetes, and obesity 20211201 6
<h4>Purpose of review</h4>The aim of this review is to evaluate biological, life history, environmental, and lifestyle factors and exposures that cause variability in menstrual cycle length (MCL).<h4>Recent findings</h4>Recent literature has detailed a number of factors that influence MCL, with particular emphasis placed on novel environmental exposures, such as air pollution and endocrine disrupting chemicals.<h4>Summary</h4>MCL varies widely in response to intrinsic and extrinsic inputs and is ...[more]
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Project description:Menstrual cycle length (MCL) has been shown to play an important role in couple fecundity, which is the biologic capacity for reproduction irrespective of pregnancy intentions. However, a comprehensive assessment of its role requires a fecundity model that accounts for male and female attributes and the couple's intercourse pattern relative to the ovulation day. To this end, we employ a Bayesian joint model for MCL and pregnancy. MCLs follow a scale multiplied (accelerated) mixture model with Gaussian and Gumbel components; the pregnancy model includes MCL as a covariate and computes the cycle-specific probability of pregnancy in a menstrual cycle conditional on the pattern of intercourse and no previous fertilization. Day-specific fertilization probability is modeled using natural, cubic splines. We analyze data from the Longitudinal Investigation of Fertility and the Environment Study (the LIFE Study), a couple based prospective pregnancy study, and find a statistically significant quadratic relation between fecundity and menstrual cycle length, after adjustment for intercourse pattern and other attributes, including male semen quality, both partner's age, and active smoking status (determined by baseline cotinine level 100 ng/mL). We compare results to those produced by a more basic model and show the advantages of a more comprehensive approach.
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Project description:The ability to predict an individual's menstrual cycle length to a high degree of precision could help female athletes to track their period and tailor their training and nutrition correspondingly. Such individualisation is possible and necessary, given the known inter-individual variation in cycle length. To achieve this, a hybrid predictive model was built using data on 16,524 cycles collected from a sample of 2125 women (mean age 34.38 years, range 18.00-47.10, number of menstrual cycles ranging from 4 to 53). A mixed-effect state-space model was fitted to capture the within-subject temporal correlation, incorporating a Bayesian approach for process forecasting to predict the duration (in days) of the next menstrual cycle. The modelling procedure was split into three steps (1) a time trend component using a random walk with an overdispersion parameter, (2) an autocorrelation component using an autoregressive moving-average model, and (3) a linear predictor to account for covariates (e.g. injury, stomach cramps, training intensity). The inclusion of an overdispersion parameter suggested that [Formula: see text] [Formula: see text] of cycles in the sample were overdispersed. The random walk standard deviation for a non-overdispersed cycle is [Formula: see text] [1.00, 1.09] days while under an overdispersed cycle, the menstrual cycle variance increase in 4.78 [4.57, 5.00] days. To assess the performance and prediction accuracy of the model, each woman's last observation was used as test data. The root mean square error (RMSE), concordance correlation coefficient and Pearson correlation coefficient (r) between the observed and predicted values were calculated. The model had an RMSE of 1.6412 days, a precision of 0.7361 and overall accuracy of 0.9871. In conclusion, the hybrid model presented here is a helpful approach for predicting menstrual cycle length, which in turn can be used to support female athlete wellness.
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Project description:BackgroundLocal inflammation plays an important role in normal folliculogenesis and ovulation, and conditions of chronic systemic inflammation, such as obesity and polycystic ovarian syndrome, can disrupt normal follicular dynamics.ObjectiveThis study aimed to determine the association between systemic inflammation, as measured by C-reactive protein levels, and menstrual cycle length.Study designThis study was a secondary analysis using data from Time to Conceive, a prospective time-to-pregnancy cohort study. The association between cycle length and C-reactive protein was analyzed using multivariable linear mixed and marginal models adjusted for age, race, education, body mass index, time since oral contraceptive use, alcohol, smoking, caffeine consumption, and exercise. Time to Conceive enrolled women aged 30 to 44 years with no history of infertility who were attempting to conceive for <3 months. Serum C-reactive protein levels were measured on cycle day 2, 3, or 4. Participants recorded daily menstrual cycle data for ≤4 months.ResultsMain outcome measures included menstrual cycle length and follicular and luteal phase lengths. Multivariable analysis included 1409 cycles from 414 women. There was no linear association between C-reactive protein levels and menstrual cycle length. However, compared with <1 mg/L, a C-reactive protein level >10 mg/L was associated with >3 times the odds (adjusted odds ratio, 3.7; 95% confidence interval, 1.67-8.11) of long cycles (defined as ≥35 days). When evaluating follicular phase length, a C-reactive protein level of >10 mg/L was associated both with follicular phases that were 1.7 (95% confidence interval, 0.23-3.09) days longer and with >2 times the odds of a long follicular phase (adjusted odds ratio, 2.2; 95% confidence interval, 1.05-4.74).ConclusionThere is a potential pathophysiological association between systemic inflammation and menstrual cycle changes. Further studies are needed to determine if systemic inflammation alters the menstrual cycle or if long menstrual cycles are a marker for elevated systemic inflammation.
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Project description:BackgroundAge at menarche and menstrual cycle characteristics are indicators of endocrine function and may be risk factors for diseases such as reproductive cancers. The progesterone receptor gene (PGR) has been identified as a candidate gene for age at menarche and menstrual function.MethodsWomen office workers ages 19-41 self-reported age at menarche and participated in a prospective study of menstrual function and fertility. First-morning urine was used as the DNA source. 444 women were genotyped for a functional variant in PGR, rs1042838 (Val660Leu), and 264 women were also genotyped for 29 other SNPs across the extended gene region.ResultsGenetic variation across PGR was associated with age at menarche using a global score statistic (p = 0.03 among non-Hispanic whites). Women carrying two copies of the Val660Leu variant experienced menarche 1 year later than women carrying one or no copies of the variant (13.6 ± 0.5 vs. 12.6 ± 0.1; p = 0.03). The Val660Leu variant was also associated with decreased odds of short menstrual cycles (17-24 days) (OR, 95% CI: 0.54 [0.36, 0.80]; p = 0.002).ConclusionGenetic variation in PGR was associated with age at menarche and menstrual cycle length in this population. Further investigation of these associations in a replication dataset is warranted.
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Project description:BackgroundMenstrual cycle characteristics are linked to reproductive function and long-term health outcomes. This study aimed to evaluate menstrual cycle patterns, characterized by regularity and length, and associated factors among women in the Korea Nurses' Health Study.MethodsA total of 9335 premenopausal women aged 22-45 years were included in this cross-sectional study. Regularity and length of menstrual cycles were self-reported, and their associations with reproductive, lifestyle, and occupational factors were examined using binomial and multinomial logistic regression models. Adjusted least-square means of menstrual distress, depressive symptoms, stress, fatigue, anxiety, and sleep problems were estimated according to menstrual cycle characteristics using generalized linear models.ResultsTwenty-one percent of nurses reported having irregular menstrual cycles (variability > 7 days). Ten percent, 64%, and 26% had menstrual cycle length of < 26, 26-31, and 32-50 days, respectively. Variability and length of cycles decreased with age and increased with age at menarche. Parous women showed low tendency of irregular cycles. Women with body mass index (BMI) > 25 kg/m2 had higher odds of irregular (odds ratio [OR] 1.68; 95% confidence interval [CI] 1.40-2.03) and long cycles (OR 1.31; 95% CI 1.08-1.58) than those with BMI 18.5- < 23 kg/m2. Irregular cycles were less common in women performing vigorous physical activity, but more common in those with prolonged standing or frequent heavy lifting at work. Frequent rotating night shift was associated with irregular cycles among nulliparous women. Levels of menstrual and premenstrual distress, depressive symptoms, perceived stress, physical and mental fatigue, anxiety, and sleep problems were higher in women with irregular cycles than in those with regular cycles (p < 0.001, each).ConclusionsThe study suggests that irregular and long menstrual cycles are associated with reproductive, lifestyle, and occupational factors; also with menstrual distress and perceived health status. Our findings contribute to a better understanding of potential risk factors for menstrual dysfunction, and thus, may help improve women's health.
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Project description:BackgroundPerfluoroalkyl substances have been associated with changes in menstrual cycle characteristics and fecundity, when modeled separately. However, these outcomes are biologically related, and we evaluate their joint association with exposure to perfluoroalkyl substances.MethodsWe recruited 501 couples from Michigan and Texas in 2005-2009 upon their discontinuing contraception and followed them until pregnancy or 12 months of trying. Female partners provided a serum sample on enrollment and completed daily journals on menstruation, intercourse, and pregnancy test results. We measured seven perfluoroalkyl substances in serum using liquid chromatography-tandem mass spectrometry. We assessed the association between perfluoroalkyl substances and menstrual cycle length using accelerated failure time models and between perfluoroalkyl substances and fecundity using a Bayesian joint modeling approach to incorporate cycle length.ResultsMenstrual cycles were 3% longer comparing women in the second versus first tertile of perfluorodecanoate (PFDeA; acceleration factor [AF] = 1.03, 95% credible interval [CrI] = [1.00, 1.05]), but 2% shorter for women in the highest versus lowest tertile of perfluorooctanoic acid (PFOA; AF = 0.98, 95% CrI = [0.96, 1.00]). When accounting for cycle length, relevant covariates, and remaining perfluoroalkyl substances, the probability of pregnancy was lower for women in second versus first tertile of perfluorononanoate (PFNA; odds ratio [OR] = 0.6, 95% CrI = [0.4, 1.0]) although not when comparing the highest versus lowest (OR = 0.7, 95% CrI = [0.3, 1.1]) tertile.ConclusionsIn this prospective cohort study, we observed associations between two perfluoroalkyl substances and menstrual cycle length changes, and between select perfluoroalkyl substances and diminished fecundity at some (but not all) concentrations. See video abstract at, http://links.lww.com/EDE/B136.
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Project description:COVID-19 vaccination may be associated with change in menstrual cycle length following vaccination. We estimated covariate-adjusted differences in mean cycle length (MCL), measured in days, between pre-vaccination cycles, vaccination cycles, and post-vaccination cycles within vaccinated participants who met eligibility criteria in the Apple Women's Health Study, a longitudinal mobile-application-based cohort of people in the U.S. with manually logged menstrual cycles. A total of 9652 participants (8486 vaccinated; 1166 unvaccinated) contributed 128,094 cycles (median = 10 cycles per participant; inter-quartile range: 4-22). Fifty-five percent of vaccinated participants received Pfizer-BioNTech's mRNA vaccine, 37% received Moderna's mRNA vaccine, and 8% received the Johnson & Johnson/Janssen (J&J) vaccine. COVID-19 vaccination was associated with a small increase in MCL for cycles in which participants received the first dose (0.50 days, 95% CI: 0.22, 0.78) and cycles in which participants received the second dose (0.39 days, 95% CI: 0.11, 0.67) of mRNA vaccines compared with pre-vaccination cycles. Cycles in which the single dose of J&J was administered were, on average, 1.26 days longer (95% CI: 0.45, 2.07) than pre-vaccination cycles. Post-vaccination cycles returned to average pre-vaccination length. Estimated follicular phase vaccination was associated with increased MCL in cycles in which participants received the first dose (0.97 days, 95% CI: 0.53, 1.42) or the second dose (1.43 days, 95% CI: 1.06, 1.80) of mRNA vaccines or the J&J dose (2.27 days, 95% CI: 1.04, 3.50), compared with pre-vaccination cycles. Menstrual cycle change following COVID-19 vaccination appears small and temporary and should not discourage individuals from becoming vaccinated.
| S-EPMC9628464 | biostudies-literature