- Award ID(s):
- Publication Date:
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- Journal Name:
- Human Reproduction
- Sponsoring Org:
- National Science Foundation
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Male cellular telephone exposure, fecundability, and semen quality: results from two preconception cohort studiesAbstract STUDY QUESTION To what extent is exposure to cellular telephones associated with male fertility? SUMMARY ANSWER Overall, we found little association between carrying a cell phone in the front pants pocket and male fertility, although among leaner men (BMI <25 kg/m2), carrying a cell phone in the front pants pocket was associated with lower fecundability. WHAT IS KNOWN ALREADY Some studies have indicated that cell phone use is associated with poor semen quality, but the results are conflicting. STUDY DESIGN, SIZE, DURATION Two prospective preconception cohort studies were conducted with men in Denmark (n = 751) and in North America (n = 2349), enrolled and followed via the internet from 2012 to 2020. PARTICIPANTS/MATERIALS, SETTING, METHODS On the baseline questionnaire, males reported their hours/day of carrying a cell phone in different body locations. We ascertained time to pregnancy via bi-monthly follow-up questionnaires completed by the female partner for up to 12 months or until reported conception. We used proportional probabilities regression models to estimate fecundability ratios (FRs) and 95% confidence intervals (CIs) for the association between male cell phone habits and fecundability, focusing on front pants pocket exposure, within each cohort separately and pooling across the cohorts using a fixed-effect meta-analysis. In a subset ofmore »
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 reportedmore »
Abstract STUDY QUESTION Do daughters of older mothers have lower fecundability? SUMMARY ANSWER In this cohort study of North American pregnancy planners, there was virtually no association between maternal age ≥35 years and daughters’ fecundability. WHAT IS KNOWN ALREADY Despite suggestive evidence that daughters of older mothers may have lower fertility, only three retrospective studies have examined the association between maternal age and daughter’s fecundability. STUDY DESIGN, SIZE, DURATION Prospective cohort study of 6689 pregnancy planners enrolled between March 2016 and January 2020. PARTICIPANTS/MATERIALS, SETTING, METHODS Pregnancy Study Online (PRESTO) is an ongoing pre-conception cohort study of pregnancy planners (age, 21-45 years) from the USA and Canada. We estimated fecundability ratios (FR) for maternal age at the participant’s birth using multivariable proportional probabilities regression models. MAIN RESULTS AND THE ROLE OF CHANCE Daughters of mothers ≥30 years were less likely to have previous pregnancies (or pregnancy attempts) or risk factors for infertility, although they were more likely to report that their mother had experienced problems conceiving. The proportion of participants with prior unplanned pregnancies, a birth before age 21, ≥3 cycles of attempt at study entry or no follow-up was greater among daughters of mothers <25 years. Compared with maternal age 25–29 years, FRs (95%more »
Abstract Although electronic cigarette (e-cigarette) aerosol contains similar toxicants to combustible cigarettes, few studies have examined their influence on fecundability. We assessed the association between e-cigarette use and fecundability, overall and according to combustible cigarette smoking history, in a cohort of 4,586 North American women (aged 21–45 years) enrolled during 2017–2020 in Pregnancy Study Online, a Web-based prospective preconception study. Women reported current and former e-cigarette use on baseline and follow-up questionnaires, and they completed bimonthly follow-up questionnaires until self-reported pregnancy or censoring. Fecundability ratios and 95% confidence intervals were calculated using proportional probabilities models, controlling for potential confounders. Overall, 17% of women had ever used e-cigarettes and 4% were current users. Compared with never use of e-cigarettes, current e-cigarette use was associated with slightly lower fecundability (fecundability ratio = 0.84, 95% confidence interval (CI): 0.67, 1.06). Compared with current nonusers of e-cigarettes and combustible cigarettes, fecundability ratios were 0.83 (95% CI: 0.54, 1.29) for current dual users of e-cigarettes and combustible cigarettes, 0.91 (95% CI: 0.70, 1.18) for current e-cigarette users who were nonsmokers of combustible cigarettes, and 1.01 (95% CI: 0.85, 1.20) for nonusers of e-cigarettes who were current smokers of combustible cigarettes. Current e-cigarette use was associatedmore »
Abstract STUDY QUESTION
Can we derive adequate models to predict the probability of conception among couples actively trying to conceive?
Leveraging data collected from female participants in a North American preconception cohort study, we developed models to predict pregnancy with performance of ∼70% in the area under the receiver operating characteristic curve (AUC).
WHAT IS KNOWN ALREADY
Earlier work has focused primarily on identifying individual risk factors for infertility. Several predictive models have been developed in subfertile populations, with relatively low discrimination (AUC: 59–64%).
STUDY DESIGN, SIZE, DURATION
Study participants were female, aged 21–45 years, residents of the USA or Canada, not using fertility treatment, and actively trying to conceive at enrollment (2013–2019). Participants completed a baseline questionnaire at enrollment and follow-up questionnaires every 2 months for up to 12 months or until conception. We used data from 4133 participants with no more than one menstrual cycle of pregnancy attempt at study entry.
PARTICIPANTS/MATERIALS, SETTING, METHODS
On the baseline questionnaire, participants reported data on sociodemographic factors, lifestyle and behavioral factors, diet quality, medical history and selected male partner characteristics. A total of 163 predictors were considered in this study. We implemented regularized logistic regression, support vector machines, neural networks and gradient boosted decisionmore »
MAIN RESULTS AND THE ROLE OF CHANCE
Model I and II AUCs were 70% and 66%, respectively, in parsimonious models, and the concordance index for Model III was 63%. The predictors that were positively associated with pregnancy in all models were: having previously breastfed an infant and using multivitamins or folic acid supplements. The predictors that were inversely associated with pregnancy in all models were: female age, female BMI and history of infertility. Among nulligravid women with no history of infertility, the most important predictors were: female age, female BMI, male BMI, use of a fertility app, attempt time at study entry and perceived stress.
LIMITATIONS, REASONS FOR CAUTION
Reliance on self-reported predictor data could have introduced misclassification, which would likely be non-differential with respect to the pregnancy outcome given the prospective design. In addition, we cannot be certain that all relevant predictor variables were considered. Finally, though we validated the models using split-sample replication techniques, we did not conduct an external validation study.
WIDER IMPLICATIONS OF THE FINDINGS
Given a wide range of predictor data, machine learning algorithms can be leveraged to analyze epidemiologic data and predict the probability of conception with discrimination that exceeds earlier work.
STUDY FUNDING/COMPETING INTEREST(S)
The research was partially supported by the U.S. National Science Foundation (under grants DMS-1664644, CNS-1645681 and IIS-1914792) and the National Institutes for Health (under grants R01 GM135930 and UL54 TR004130). In the last 3 years, L.A.W. has received in-kind donations for primary data collection in PRESTO from FertilityFriend.com, Kindara.com, Sandstone Diagnostics and Swiss Precision Diagnostics. L.A.W. also serves as a fibroid consultant to AbbVie, Inc. The other authors declare no competing interests.
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