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Study Question: To what extent is dietary folate intake and total folate intake (dietary and supplemental intakes) associated with fecundability, the per cycle probability of conception?Summary Answer: Preconception dietary folate intake was positively associated with fecundability in a monotonic pattern.What Is Known Already: Supplemental folic acid has been associated with improved fertility, but little is known about the relation between dietary folate and fecundability.Study Design, Size, Duration: A prospective cohort study including 9559 women trying to conceive without fertility treatment and enrolled in the period 2013-2020.Participants/materials, Setting, Methods: We used data from two internet-based prospective cohort studies of pregnancy planners from Denmark, where folic acid fortification is not performed (SnartForældre.dk (SF); n = 3755) and North America, where the food supply is fortified with folic acid (Pregnancy Study Online (PRESTO); n = 5804). Women contributed menstrual cycles at risk until they reported conception or experienced a censoring event. We used proportional probabilities regression models to compute fecundability ratios (FRs) and 95% CI, adjusting for potential confounders.Main Results and the Role Of Chance: Compared with a dietary folate intake ≥400 µg/day, the adjusted FRs for women in SF were 0.92 (95% CI: 0.85-0.99) for intake 250-399 µg/day, and 0.80 (95% CI: 0.68-0.94) for intake of <250 µg/day. The corresponding FRs in PRESTO were 0.95 (95% CI: 0.89-1.01) and 0.81 (95% CI: 0.65-1.00). Compared with the highest level of total folate intake (diet folate ≥400 µg/day plus folic acid supplementation), in both cohorts fecundability was lowest among women with the lowest dietary intake <250 µg/day dietary folate and no supplementation (FR: 0.76, 95% CI: 0.59-0.98 [SF] and 0.49, 95% CI: 0.31-0.77 [PRESTO]). Further, total intake dietary folate <250 µg/day plus supplementation was associated with reduced fecundability for SF participants (FR; 0.79, 95% CI: 0.65-0.98) and for PRESTO participants (FR; 0.92, 95% CI: 0.72-1.16).Limitations, Reasons For Caution: It is unknown whether dietary folate and folic acid intake affect fecundability on its own or if there is an interaction with other micronutrients provided in healthy diet. Thus, the observed associations may not reflect dietary folate intake alone, but overall healthy diet.Wider Implications Of the Findings: Recommendations for preconception dietary folate intake and folic acid supplementation are of importance not only to prevent neural tube defects but also to enhance fecundability.Study Funding/competing Interest(s): The study was supported by the National Institute of Child Health and Human Development (R01-HD086742). The authors report no competing interests.Trial Registration Number: N/A. [ABSTRACT FROM AUTHOR]

Study Question: Can we derive adequate models to predict the probability of conception among couples actively trying to conceive?Summary Answer: 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 decision 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.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.Trial Registration Number: N/A. [ABSTRACT FROM AUTHOR]

Study Question: To what extent are ambient concentrations of particulate matter <2.5 microns (PM2.5), nitrogen dioxide (NO2) and ozone (O3) associated with risk of self-reported physician-diagnosed uterine leiomyomata (UL)?Summary Answer: In this large prospective cohort study of Black women, ambient concentrations of O3, but not PM2.5 or NO2, were associated with increased risk of UL.What Is Known Already: UL are benign tumors of the myometrium that are the leading cause of gynecologic inpatient care among reproductive-aged women. Black women are clinically diagnosed at two to three times the rate of white women and tend to exhibit earlier onset and more severe disease. Two epidemiologic studies have found positive associations between air pollution exposure and UL risk, but neither included large numbers of Black women.Study Design, Size, Duration: We conducted a prospective cohort study of 21 998 premenopausal Black women residing in 56 US metropolitan areas from 1997 to 2011.Participants/material, Setting, Methods: Women reported incident UL diagnosis and method of confirmation (i.e. ultrasound, surgery) on biennial follow-up questionnaires. We modeled annual residential concentrations of PM2.5, NO2 and O3 throughout the study period. We used Cox proportional hazards regression models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for a one-interquartile range (IQR) increase in air pollutant concentrations, adjusting for confounders and co-pollutants.Main Results and the Role Of Chance: During 196 685 person-years of follow-up, 6238 participants (28.4%) reported physician-diagnosed UL confirmed by ultrasound or surgery. Although concentrations of PM2.5 and NO2 were not appreciably associated with UL (HRs for a one-IQR increase: 1.01 (95% CI: 0.93, 1.10) and 1.05 (95% CI: 0.95, 1.16), respectively), O3 concentrations were associated with increased UL risk (HR for a one-IQR increase: 1.19, 95% CI: 1.07, 1.32). The association was stronger among women age <35 years (HR: 1.26, 95% CI: 0.98, 1.62) and parous women (HR: 1.28, 95% CI: 1.11, 1.48).Limitations, Reasons For Caution: Our measurement of air pollution is subject to misclassification, as monitoring data are not equally spatially distributed and we did not account for time-activity patterns. Our outcome measure was based on self-report of a physician diagnosis, likely resulting in under-ascertainment of UL. Although we controlled for several individual- and neighborhood-level confounding variables, residual confounding remains a possibility.Wider Implications Of the Findings: Inequitable burden of air pollution exposure has important implications for racial health disparities, and may be related to disparities in UL. Our results emphasize the need for additional research focused on environmental causes of UL.Study Funding/competing Interest(s): This research was funded by the National Cancer Institute (U01-CAA164974) and the National Institute of Environmental Health Sciences (R01-ES019573). L.A.W. is a fibroid consultant for AbbVie, Inc. and accepts in-kind donations from Swiss Precision Diagnostics, Sandstone Diagnostics, FertilityFriend.com and Kindara.com for primary data collection in Pregnancy Study Online (PRESTO). M.J. declares consultancy fees from the Health Effects Institute (as a member of the review committee). The remaining authors declare they have no actual or potential competing financial interests.Trial Registration Number: N/A. [ABSTRACT FROM AUTHOR]