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Title: Antibiotics and fecundability among female pregnancy planners: a prospective cohort study
Abstract STUDY QUESTION To what extent is female preconception antibiotic use associated with fecundability? SUMMARY ANSWER Preconception antibiotic use overall was not appreciably associated with fecundability. WHAT IS KNOWN ALREADY Antibiotics are commonly used by women and are generally thought to be safe for use during pregnancy. However, little is known about possible effects of antibiotic use on fecundability, the per-cycle probability of conception. Previous research on this question has been limited to occupational rather than therapeutic exposure. STUDY DESIGN, SIZE, DURATION We analyzed data from an Internet-based preconception cohort study of 9524 female pregnancy planners aged 21–45 years residing in the USA and Canada who had been attempting to conceive for six or fewer cycles at study entry. Participants enrolled between June 2013 and September 2020 and completed baseline and bimonthly follow-up questionnaires for up to 12 months or until a reported pregnancy, whichever came first. The questions pertaining to antibiotic type and indication were added to the PRESTO questionnaires in March 2016. PARTICIPANTS/MATERIALS, SETTING, METHODS We assessed antibiotic use in the previous 4 weeks at baseline and on each follow-up questionnaire. Participants provided the name of the specific antibiotic and the indication for use. Antibiotics were classified based on active ingredient more » (penicillins, macrolides, nitrofurantoin, nitroimidazole, cephalosporins, sulfonamides, quinolones, tetracyclines, lincosamides), and indications were classified by type of infection (respiratory, urinary tract, skin, vaginal, pelvic, and surgical). Participants reported pregnancy status on follow-up questionnaires. We used proportional probabilities regression to estimate fecundability ratios (FR), the per-cycle probability of conception comparing exposed with unexposed individuals, and 95% confidence intervals (CI), adjusting for sociodemographics, lifestyle factors, and reproductive history. MAIN RESULTS AND THE ROLE OF CHANCE Overall, women who used antibiotics in the past 4 weeks at baseline had similar fecundability to those who had not used antibiotics (FR: 0.98, 95% CI: 0.89–1.07). Sulfonamides and lincosamides were associated with slightly increased fecundability (FR: 1.39, 95% CI: 0.90–2.15, and FR: 1.58 95% CI: 0.96–2.60, respectively), while macrolides were associated with slightly reduced fecundability (FR: 0.70, 95% CI: 0.47–1.04). Analyses of the indication for antibiotic use suggest that there is likely some confounding by indication. LIMITATIONS, REASONS FOR CAUTION Findings were imprecise for some antibiotic classes and indications for use owing to small numbers of antibiotic users in these categories. There are likely heterogeneous effects of different combinations of indications and treatments, which may be obscured in the overall null results, but cannot be further elucidated in this analysis. WIDER IMPLICATIONS OF THE FINDINGS There is little evidence that use of most antibiotics is associated with reduced fecundability. Antibiotics and the infections they treat are likely associated with fecundability through differing mechanisms, resulting in their association with increased fecundability in some circumstances and decreased fecundability in others. STUDY FUNDING/COMPETING INTEREST(S) This study was supported through funds provided by the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health (R01-HD086742, R21-HD072326). L.A.W. has received in-kind donations from Swiss Precision Diagnostics, Sandstone Diagnostics, Fertility Friend, and Kindara for primary data collection in PRESTO. The other authors have no conflicts of interest to disclose. TRIAL REGISTRATION NUMBER N/A. « less
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Human Reproduction
Sponsoring Org:
National Science Foundation
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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 »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. 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  3. 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 »CI) for maternal age <20, 20–24, 30–34, and ≥35 were 0.72 (0.61, 0.84), 0.92 (0.85, 1.00), 1.08 (1.00, 1.17), and 1.00 (0.89, 1.12), respectively. LIMITATIONS, REASONS FOR CAUTION Although the examined covariates did not meaningfully affect the associations, we had limited information on the participants’ mother. Differences by maternal age in reproductive history, infertility risk factors and loss to follow-up suggest that selection bias may partly explain our results. WIDER IMPLICATIONS OF THE FINDINGS Our finding that maternal age 35 years or older was not associated with daughter’s fecundability is reassuring, considering the trend towards delayed childbirth. However, having been born to a young mother may be a marker of low fecundability among pregnancy planners. STUDY FUNDING/COMPETING INTEREST(S) PRESTO was funded by NICHD Grants (R21-HD072326 and R01-HD086742) and has received in-kind donations from Swiss Precision Diagnostics,,, and Sandstone Diagnostics. Dr Wise is a fibroid consultant for AbbVie, Inc. TRIAL REGISTRATION NUMBER n/a« less
  4. 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 »with slightly reduced fecundability, but estimates of its independent and joint associations with combustible cigarette smoking were inconsistent and imprecise.« less
  5. 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).


    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 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.


    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 »trees to derive models predicting the probability of pregnancy: (i) within fewer than 12 menstrual cycles of pregnancy attempt time (Model I), and (ii) within 6 menstrual cycles of pregnancy attempt time (Model II). Cox models were used to predict the probability of pregnancy within each menstrual cycle for up to 12 cycles of follow-up (Model III). We assessed model performance using the AUC and the weighted-F1 score for Models I and II, and the concordance index for Model III.


    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.


    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.


    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.


    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,, 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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