More Like this

1. Male cellular telephone exposure, fecundability, and semen quality: results from two preconception cohort studies

   https://doi.org/10.1093/humrep/deab001  

   Hatch, E E ; Willis, S K ; Wesselink, A K ; Mikkelsen, E M ; Eisenberg, M L ; Sommer, G J ; Sorensen, H T ; Rothman, K J ; Wise, L A ( February 2021 , Human Reproduction)

   Abstract 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 »participants, we examined selected semen parameters (semen volume, sperm concentration and sperm motility) using a home-based semen testing kit. MAIN RESULTS AND THE ROLE OF CHANCE There was little overall association between carrying a cell phone in a front pants pocket and fecundability: the FR for any front pants pocket exposure versus none was 0.94 (95% CI: 0.0.83–1.05). We observed an inverse association between any front pants pocket exposure and fecundability among men whose BMI was <25 kg/m2 (FR = 0.72, 95% CI: 0.59–0.88) but little association among men whose BMI was ≥25 kg/m2 (FR = 1.05, 95% CI: 0.90–1.22). There were few consistent associations between cell phone exposure and semen volume, sperm concentration, or sperm motility. LIMITATIONS, REASONS FOR CAUTION Exposure to radiofrequency radiation from cell phones is subject to considerable non-differential misclassification, which would tend to attenuate the estimates for dichotomous comparisons and extreme exposure categories (e.g. exposure 8 vs. 0 h/day). Residual confounding by occupation or other unknown or poorly measured factors may also have affected the results. WIDER IMPLICATIONS OF THE FINDINGS Overall, there was little association between carrying one’s phone in the front pants pocket and fecundability. There was a moderate inverse association between front pants pocket cell phone exposure and fecundability among men with BMI <25 kg/m2, but not among men with BMI ≥25 kg/m2. Although several previous studies have indicated associations between cell phone exposure and lower sperm motility, we found few consistent associations with any semen quality parameters. STUDY FUNDING/COMPETING INTEREST(S) The study was funded by the National Institutes of Health, grant number R03HD090315. In the last 3 years, PRESTO has received in-kind donations from Sandstone Diagnostics (for semen kits), Swiss Precision Diagnostics (home pregnancy tests), Kindara.com (fertility app), and FertilityFriend.com (fertility app). Dr. L.A.W. is a fibroid consultant for AbbVie, Inc. Dr. H.T.S. reports that the Department of Clinical Epidemiology is involved in studies with funding from various companies as research grants to and administered by Aarhus University. None of these studies are related to the current study. Dr. M.L.E. is an advisor to Sandstone Diagnostics, Ro, Dadi, Hannah, and Underdog. Dr. G.J.S. holds ownership in Sandstone Diagnostics Inc., developers of the Trak Male Fertility Testing System. In addition, Dr. G.J.S. has a patent pending related to Trak Male Fertility Testing System issued. TRIAL REGISTRATION NUMBER N/A« less
2. Fecundability in relation to use of mobile computing apps to track the menstrual cycle

   https://doi.org/10.1093/humrep/deaa176  

   Stanford, Joseph B ; Willis, Sydney K ; Hatch, Elizabeth E ; Rothman, Kenneth J ; Wise, Lauren A ( September 2020 , Human Reproduction)

   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 »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. We also examined the impact of concurrent use of fertility indicators: basal body temperature, cervical fluid, cervix position and/or urine LH. MAIN RESULTS AND THE ROLE OF CHANCE Among 8363 women, 6077 (72.7%) were using one or more cycle apps at baseline. A total of 122 separate apps were reported by women. We designated five of these apps before analysis as more likely to be effective (Clue, Fertility Friend, Glow, Kindara, Ovia; hereafter referred to as ‘selected apps’). The use of any app at baseline was associated with 20% increased fecundability, with little difference between selected apps versus other apps (selected apps FR (95% CI): 1.20 (1.13, 1.28); all other apps 1.21 (1.13, 1.30)). In time-varying analyses, cycle app use was associated with 12–15% increased fecundability (selected apps FR (95% CI): 1.12 (1.04, 1.21); all other apps 1.15 (1.07, 1.24)). When apps were used at baseline with one or more fertility indicators, there was higher fecundability than without fertility indicators (selected apps with indicators FR (95% CI): 1.23 (1.14, 1.34) versus without indicators 1.17 (1.05, 1.30); other apps with indicators 1.30 (1.19, 1.43) versus without indicators 1.16 (1.06, 1.27)). In time-varying analyses, results were similar when stratified by time trying at study entry (<3 vs. 3–6 cycles) or cycle regularity. For use of the selected apps, we observed higher fecundability among women with a history of subfertility: FR 1.33 (1.05–1.67). LIMITATIONS, REASONS FOR CAUTION Neither regularity nor intensity of app use was ascertained. The prospective time-varying assessment of app use was based on questionnaires completed every 2 months, which would not capture more frequent changes. Intercourse frequency was also reported retrospectively and we do not have data on timing of intercourse relative to the fertile window. Although we controlled for a wide range of covariates, we cannot exclude the possibility of residual confounding (e.g. choosing to use an app in this observational study may be a marker for unmeasured health habits promoting fecundability). Half of the women in the study received a free premium subscription for one of the apps (Fertility Friend), which may have increased the overall prevalence of app use in the time-varying analyses, but would not affect app use at baseline. Most women in the study were college educated, which may limit application of results to other populations. WIDER IMPLICATIONS OF THE FINDINGS Use of a cycle app, especially in combination with observation of one or more fertility indicators (basal body temperature, cervical fluid, cervix position and/or urine LH), may increase fecundability (per-cycle pregnancy probability) by about 12–20% for couples trying to conceive. We did not find consistent evidence of improved fecundability resulting from use of one specific app over another. STUDY FUNDING/COMPETING INTEREST(S) This research was supported by grants, R21HD072326 and R01HD086742, from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, USA. In the last 3 years, Dr L.A.W. has served as a fibroid consultant for AbbVie.com. Dr L.A.W. has also received in-kind donations from Sandstone Diagnostics, Swiss Precision Diagnostics, FertilityFriend.com and Kindara.com for primary data collection and participant incentives in the PRESTO cohort. Dr J.B.S. reports personal fees from Swiss Precision Diagnostics, outside the submitted work. The remaining authors have nothing to declare. TRIAL REGISTRATION NUMBER N/A.« less
3. Maternal age at birth and daughter’s fecundability

   https://doi.org/10.1093/humrep/deab057  

   Basso, Olga ; Willis, Sydney K ; Hatch, Elizabeth E ; Mikkelsen, Ellen M ; Rothman, Kenneth J ; Wise, Lauren A ( April 2021 , Human Reproduction)

   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, FertilityFriend.com, Kindara.com, and Sandstone Diagnostics. Dr Wise is a fibroid consultant for AbbVie, Inc. TRIAL REGISTRATION NUMBER n/a« less
4. Electronic Cigarettes and Fecundability: Results From a Prospective Preconception Cohort Study

   https://doi.org/10.1093/aje/kwaa067  

   Harlow, Alyssa F ; Hatch, Elizabeth E ; Wesselink, Amelia K ; Rothman, Kenneth J ; Wise, Lauren A ( May 2020 , American Journal of Epidemiology)

   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. Predictive models of pregnancy based on data from a preconception cohort study

   https://doi.org/10.1093/humrep/deab280  

   Yland, Jennifer J. ; Wang, Taiyao ; Zad, Zahra ; Willis, Sydney K. ; Wang, Tanran R. ; Wesselink, Amelia K. ; Jiang, Tammy ; Hatch, Elizabeth E. ; Wise, Lauren A. ; Paschalidis, Ioannis Ch ( January 2022 , Human Reproduction)

   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 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.

   N/A.

   « less