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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 does fecundability vary across seasons?Summary Answer: After accounting for seasonal patterns in pregnancy planning, we observed higher fecundability in the fall and lower fecundability in the spring, particularly at lower latitudes.What Is Known Already: In human populations, there are strong seasonal patterns of births that vary across geographic regions and time periods. However, previous studies of seasonality and fecundity are limited because they examine season of birth rather than season of conception and therefore neglect to account for seasonal variation in initiating attempts to conceive or pregnancy loss or differences in gestational length.Study Design, Size, Duration: We conducted a preconception cohort study of 14 331 women residing in North America (June 2013-May 2018: n = 5827) and Denmark (June 2007-May 2018: n = 8504). Participants were attempting to conceive without fertility treatment and had been attempting pregnancy for ≤6 menstrual cycles at enrolment.Participants/material, Setting, Methods: We collected information on season of each pregnancy attempt using last menstrual period dates over the study period. Pregnancy was reported on female bi-monthly follow-up questionnaires. We fit log-binomial models with trigonometric regression to examine periodic variation in fecundability. We accounted for seasonal variation in initiation of pregnancy attempts by including indicator variables for menstrual cycle of attempt in the regression models.Main Results and the Role Of Chance: Initiation of pregnancy attempts peaked in September, with stronger seasonality in North America than in Denmark (48 vs. 16% higher probability initiating attempts in September compared with March). After accounting for seasonal variation in initiation of pregnancy attempts, we observed modest seasonal variation in fecundability, with a peak in the late fall and early winter in both cohorts, but stronger peak/low ratios in North America (1.16; 95% confidence interval [CI]: 1.05, 1.28) than in Denmark (1.08; 95% CI: 1.00, 1.16). When we stratified the North American data by latitude, we observed the strongest seasonal variation in the southern USA (peak/low ratio of 1.45 [95% CI: 1.14, 1.84]), with peak fecundability in late November.Limitations, Reasons For Caution: We estimated menstrual cycle dates between follow-up questionnaires, which may have introduced exposure misclassification, particularly when women skipped follow-up questionnaires. We were unable to measure seasonally varying factors that may have influenced fecundability, including ambient temperature, vitamin D levels or infectious disease.Wider Implications Of the Findings: An understanding of how fecundability varies across seasons could help identify factors that can impair reproductive function. Neglecting to account for seasonal variation in initiation of pregnancy attempts could bias estimates of seasonal patterns in fecundability. This is the first preconception cohort study to examine seasonal variation in fecundability after accounting for seasonality in initiation of pregnancy attempts. Fecundability was highest in the fall and lowest in the spring, with stronger effects in southern latitudes of North America, suggesting that seasonal exposures may affect fecundity.Study Funding/competing Interest(s): This research was funded by the Eunice K. Shriver National Institute of Child Health and Human Development (R21-050264, R01-HD060680, R21-HD072326 and R01-HD086742) and the Danish Medical Research Council (271-07-0338). The authors declare no conflicts of interest. [ABSTRACT FROM AUTHOR]

Background: Infertility is an important public health problem with few known modifiable risk factors. Dietary factors including folic acid have been associated with improved fertility, but the association between iron and fertility is understudied. One study among US nurses found a 40% lower risk of ovulatory infertility with higher intake of nonheme iron and iron supplements.Objectives: The aim of this study was to determine the extent to which iron intake from diet and supplements reported on structured questionnaires is associated with fecundability.Methods: We conducted parallel analyses that used data from 2 prospective cohort studies of pregnancy planners from Denmark (Snart Foraeldre; n = 1693) and North America (PRESTO; n = 2969) during 2013-2018. Follow-up comprised menstrual cycles at risk until pregnancy or censoring for fertility treatment, stopped trying to conceive, withdrawal, loss to follow-up, or 12 cycles of attempt. We used proportional probabilities regression models to estimate fecundability ratios (FRs) and 95% CIs, adjusting for confounders.Results: We found little association between dietary heme iron intake and fecundability in either cohort. The FR for nonheme iron intake (≥11 mg/d compared with <9 mg/day) was 1.11 for Snart Foraeldre participants (95% CI: 0.92, 1.34) and 1.01 for PRESTO participants (95% CI: 0.89, 1.14). The FR for iron-containing supplements was 1.01 in Snart Foraeldre (95% CI: 0.90, 1.13) and 1.19 in PRESTO (95% CI: 1.03, 1.38). In PRESTO, but not Snart Foraeldre, stronger positive associations were found for nonheme iron intake and iron supplement use among women with heavy menses or short menstrual cycles.Conclusions: Overall, dietary intake of iron was not consistently associated with fecundability, although there was some evidence for a positive association among women with risk factors for iron deficiency. We also found a small positive association between supplemental iron intake and fecundability among North American, but not Danish, pregnancy planners. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to evaluate the association between menstrual cycle characteristics in early life and adulthood and fecundability.Methods: Pregnancy Study Online (PRESTO) is an Internet-based preconception cohort study of pregnancy planners from the United States and Canada. During the preconception period, we enrolled 2189 female pregnancy planners aged 21-45 years who had been attempting conception for ≤6 cycles. Women self-reported menstrual cycle characteristics via an online baseline questionnaire, and pregnancy status was ascertained through bimonthly follow-up questionnaires. Proportional probabilities models were used to estimate fecundability ratios (FRs) and 95% confidence intervals (CIs), adjusting for potential confounders.Results: Compared with usual menstrual cycle lengths of 27-29 days, cycle lengths of <25 (FR = 0.81, 95% CI: 0.54-1.22) and 25-26 days (FR = 0.92, 95% CI: 0.75-1.14) were associated with reduced fecundability. Compared with women who reached menarche at the age of 12-13 years, those who reached menarche at <12 years had reduced fecundability (FR = 0.87, 95% CI: 0.76-0.99). Women whose cycles never regularized after menarche (FR = 0.93, 95% CI: 0.81-1.06) had slightly reduced fecundability compared with women whose cycles regularized within 2 years of menarche. Bleed length and heaviness of bleeding were not appreciably associated with fecundability.Conclusions: Menstrual cycle characteristics, specifically cycle length and age at menarche, may act as markers of fertility potential among pregnancy planners. [ABSTRACT FROM AUTHOR]