Dataset Information

A Bayesian joint model of menstrual cycle length and fecundity.

ABSTRACT: 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.

SUBMITTER: Lum KJ

PROVIDER: S-EPMC4761533 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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A Bayesian joint model of menstrual cycle length and fecundity.

Lum Kirsten J KJ Sundaram Rajeshwari R Buck Louis Germaine M GM Louis Thomas A TA

Biometrics 20150821 1

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 Ga ...[more]

PMID: 26295923

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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.

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.

Dataset Information

A Bayesian joint model of menstrual cycle length and fecundity.

Publications

A Bayesian joint model of menstrual cycle length and fecundity.

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