Lynch, K. E., Mumford, S. L., Schliep, K. C., Whitcomb, B. W., Zarek, S. M., Pollack, A. Z., Bertone-Johnson, E. R., Danaher, M., Wactawski-Wende, J., Gaskins, A. J., & Schisterman, E. F. (2014). Assessment of anovulation in eumenorrheic women: comparison of ovulation detection algorithms. Fertility and sterility, 102(2), 511-518.e2. https://doi.org/10.1016/j.fertnstert.2014.04.035
Lynch KE, Mumford SL, Schliep KC, Whitcomb BW, Zarek SM, Pollack AZ, et al. Assessment of anovulation in eumenorrheic women: comparison of ovulation detection algorithms. Fertil Steril. 2014;102(2):511-518.e2. doi:10.1016/j.fertnstert.2014.04.035
Lynch, K. E., et al. "Assessment of anovulation in eumenorrheic women: comparison of ovulation detection algorithms." Fertility and sterility, vol. 102, no. 2, 2014, pp. 511-518.e2.
Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, Georgia. Electronic address: Audrey.jane.gaskins@emory.edu.03czfpz43
Department of Epidemiology and Environmental Health, School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY.01y64my43
Linked to ROR, the Research Organization Registry
RRM Academy Synopsis
Regular cycles without ovulation range from 3.4% to 18.6% by algorithm
Among 259 healthy women with regular periods, 3.4% to 18.6% of cycles were labeled as having no ovulation, depending on the hormone algorithm used. In this cohort, urine LH surge algorithms tended to label more cycles this way than blood progesterone algorithms. Women were followed for one or two cycles.
Key Findings
Six algorithms using blood tests labeled 5.5% to 12.8% of 509 cycles as anovulatory. Five algorithms using urine tests labeled 3.4% to 18.6% of 445 cycles as anovulatory.
The six blood-test algorithms gave the same classification for 91.7% to 97.4% of cycles, averaging 94.8%.
Twelve cycles (2.4%) were anovulatory by every blood-test algorithm. Four of those 12 were also anovulatory by at least one urine algorithm. No cycle was anovulatory by all five urine algorithms.
The five urine algorithms agreed on average for 80.1% of cycles (range 73.0% to 86.0%), with kappa values from −0.11 to 0.49.
Interpretation
This prospective cohort followed healthy, regularly cycling women forward in time and compares algorithms with one another. No algorithm was checked against transvaginal ultrasound, the gold standard, so the study does not establish which label is accurate. Blood was drawn at up to eight visits per cycle, and two algorithms relied on estimated values between visits. Results apply to healthy women with regular cycles and may differ in women at higher risk of chronic anovulation. The authors call for ultrasound validation.
RRM Context
Cycle-charting-informed RRM care reads ovulation from several signals across the whole cycle. The authors suggest that progesterone measured after ovulation may help detect it. In this cohort, different markers disagreed about the same cycles.
Our editorial summary of this paper, not the article's abstract.
Abstract
Objective
To compare previously used algorithms to identify anovulatory menstrual cycles in women self-reporting regular menses.
Design
Prospective cohort study.
Setting
Western New York.
Patients
Two hundred fifty-nine healthy, regularly menstruating women followed for one (n=9) or two (n=250) menstrual cycles (2005-2007).
Interventions
None.
Main Outcome Measures
Prevalence of sporadic anovulatory cycles identified using 11 previously defined algorithms that use E2, P, and LH concentrations.
Results
Algorithms based on serum LH, E2, and P levels detected a prevalence of anovulation across the study period of 5.5%-12.8% (concordant classification for 91.7%-97.4% of cycles). The prevalence of anovulatory cycles varied from 3.4% to 18.6% using algorithms based on urinary LH alone or with the primary E2 metabolite, estrone-3-glucuronide, levels.
Conclusions
The prevalence of anovulatory cycles among healthy women varied by algorithm. Mid-cycle LH surge urine-based algorithms used in over-the-counter fertility monitors tended to classify a higher proportion of anovulatory cycles compared with luteal-phase P serum-based algorithms. Our study demonstrates that algorithms based on the LH surge, or in conjunction with estrone-3-glucuronide, potentially estimate a higher percentage of anovulatory episodes. Addition of measurements of postovulatory serum P or urine pregnanediol may aid in detecting ovulation.
Schliep KC et al., 2026·J Gynecol Obstet Hum Reprod·Free full text on PubMed Central
Endometriosis has been linked to cardiometabolic alterations, but whether these associations vary by disease severity or phenotype is unclear. We examined lipid profiles across endometriosis diagnosis, stage, and typology. Data came from 476 women in the NICHD ENDO cohort. Endometriosis was confirmed laparoscopically and staged using the rASRM criteria (I-IV). Typology was categorized as superficial endometriosis (SE), ovarian endometrioma (OE), deep infiltrating endometriosis (DE), and OE+DE. We compared endometriosis status, stage (I/II vs III/IV), and typology to no endometriosis using adverse lipid thresholds (total cholesterol ≥200 mg/dL, HDL <50 mg/dL, LDL ≥100 mg/dL, triglycerides ≥175 mg/dL, non-HDL ≥130 mg/dL, VLDL ≥30 mg/dL, ApoA1 <125 mg/dL, and ApoB ≥120 mg/dL). Adjusted prevalence ratios (aPR) and 95 % CIs were estimated via generalized linear models, controlling for age, race/ethnicity, BMI, income, marital status, and serum cotinine. Endometriosis diagnosis alone was not associated with adverse lipid profiles. In contrast, moderate/severe disease showed higher prevalence of elevated triglycerides (aPR= 2.27; 95 % CI: 1.18,4.35) and VLDL (aPR= 2.41; 95 % CI: 1.50, 3.85). Typology revealed stronger patterns: OE and OE+DE were associated with adverse profiles across multiple markers (aPRs 1.59-4.09), particularly ApoB and triglycerides. Minimal/mild disease and SE were not associated. The metabolic signal was phenotype-driven rather than diagnosis-driven, with severe stage and OE/OE+DE showing clear associations with adverse lipid profiles. These findings suggest lipid profiles may serve as markers of phenotype severity or shared biological milieu. Replication in larger cohorts is needed.
Reeder MR et al., 2026·Fertil Steril·Free full text on PubMed Central
To examine birth outcomes between children conceived with in vitro fertilization (IVF) or intrauterine insemination (IUI) and sibling births from unassisted conceptions. Retrospective sibling cohort. Live born children conceived via IVF, with or without intracytoplasmic sperm injection, or IUI at the Utah Center for Reproductive Medicine, 1999-2018, and sibling births from unassisted conceptions (born 1985-2018). The main analysis included singleton births (460 IVF, 666 IUI, and 1,579 unassisted siblings). Exposure: In vitro fertilization, with or without intracytoplasmic sperm injection, or IUI. Preterm birth, low birth weight, small for gestational age, large for gestational age (LGA), and major congenital anomalies. Compared with unassisted siblings, singleton children conceived via IVF had gestational ages shorter by nearly half a week (95% confidence interval [CI], -0.6 to -0.3), birth weights of 72.1 g lower (95% CI, -118.8 to -25.4), and higher proportions of preterm birth (IVF, 11.1%; IUI, 8.7%; unassisted siblings, 6.8%), LGA (IVF, 9.1%; IUI, 4.5%; unassisted siblings, 5.9%), and major congenital anomalies (IVF, 3.7%; IUI, 2.0%; unassisted siblings, 1.4%). Models adjusted for maternal age, infant sex, infant birth year, previous pregnancy, and birth order showed that children conceived via IVF were more likely to be preterm (adjusted risk ratio [aRR], 1.6; 95% CI, 1.2-2.2; absolute difference, 4.3%) and LGA (aRR, 1.8; 95% CI, 1.2-2.5; absolute difference, 3.2%). Children conceived via IVF had a higher risk of major congenital anomalies than unassisted siblings adjusted for maternal age, infant sex, and birth order (aRR, 1.9; 95% CI, 1.0-3.8; absolute difference, 2.3%). Children conceived via IUI had birth weights 55.8 g lower (95% CI, -95.6 to -15.9) than unassisted siblings. We observed an increased risk of preterm birth, low birth weight, LGA, and major congenital anomalies among singleton children conceived with IVF compared with that among unassisted siblings; however, absolute differences remain small. For children conceived via IUI, lower birth weights were observed. These results suggest that treatment-related factors in addition to underlying subfertility may contribute to adverse birth outcomes.
Valenti M et al., 2026·Am J Obstet Gynecol·Free full text on PubMed Central
Endometriosis is a chronic, gynecologic condition in which tissue similar to the lining of the uterus implants throughout the body. Women with endometriosis have a higher prevalence of infertility and a greater risk of early natural menopause compared to those without endometriosis. This study aimed to evaluate preoperative serum AMH levels among women with and without incident endometriosis and to assess whether levels differ by surgical staging and typology. The ENDO (Endometriosis: Natural History, Diagnosis, and Outcomes) study was conducted between 2007 and 2009. The ENDO study consisted of an operative and population cohort (n=600). Only those in the ENDO operative cohort from the Utah site were used for this analysis, and included women aged 18 to 44 years who were scheduled for gynecologic surgery, irrespective of clinical indication (n=476). AMH levels were measured from stored serum collected before surgery using a quantitative enzyme-linked immunosorbent assay. After excluding participants with missing outcome data (n=51), unilateral oophorectomy (n=8), or those within the population cohort (n=69), 348 participants remained for the analysis. Surgically confirmed endometriosis diagnosis, staging (American Society for Reproductive Medicine I-IV), and typology (superficial, deep, ovarian) were ascertained by the operative report. Outliers for AMH (>14.0 ng/mL) were excluded from the analyses and AMH values were log-transformed. Multivariable linear regression models adjusted for age (squared and continuous), body mass index, serum cotinine levels, and exogenous hormonal contraceptive use were conducted. Percentage differences in AMH were calculated as (exp[β]-1)×100, and 95% confidence intervals were reported. Compared with no endometriosis, incident endometriosis diagnosis was associated with lower AMH levels (-19.8%; 95% confidence interval, -37.0 to 1.0); however, this association was not statistically significant. Stage III to IV disease was associated with 40.1% lower AMH levels (95% confidence interval, -58.9 to -12.7). Ovarian endometriomas were most strongly associated with lower AMH levels (-54.3%; 95% confidence interval, -69.4 to -31.8), with a more pronounced association among those with infertility (-72.6%; 95% confidence interval, -85.4 to -48.5). Deep (-24.1%; 95% confidence interval, -48.2 to 11.0) and superficial (-15.5%; 95% confidence interval, -34.6 to 9.3) endometriosis also showed a trend toward lower AMH levels, but these findings were not statistically significant. Compared with a postoperative diagnosis of a normal pelvis, incident endometriosis was associated with 26.8% lower AMH levels (95% confidence interval, -44.6 to -3.4). Stage III to IV disease was associated with 47.8% lower AMH levels (95% confidence interval, -65.8 to -23.2), and all subtypes of endometriosis were statistically significantly associated with lower levels of AMH compared with a postoperative diagnosis of a normal pelvis (ovarian: -60.8%; 95% confidence interval, -74.4 to -39.9; deep: -34.3%; 95% confidence interval, -56.2 to -1.4; superficial: -24.8%; 95% confidence interval, -43.9 to -0.8). Ovarian and moderate to severe (stage III-IV) endometriosis were associated with markedly lower AMH levels compared with no endometriosis. Compared with a postoperative diagnosis of a normal pelvis, incident endometriosis and moderate to severe stages (stage III-IV) were associated with statistically significantly lower AMH levels. Additionally, typology (deep, ovarian, or superficial) was associated with statistically significantly lower AMH levels. However, this association was likely driven by the presence of ovarian endometriomas across all subtypes. These findings are consistent with previous studies and demonstrate that endometriosis lesions themselves, independent of surgical intervention, influence AMH levels.
Disordered eating behaviors may impact the gynecologic health of adolescents through effects on menstrual cycle function and body size; however, few studies have evaluated these associations. This study aimed to prospectively investigate the associations between individual disordered eating behaviors during adolescence and the risk of subsequent endometriosis diagnosis. Prospective, longitudinal cohort (1996-2021). Female participants (n = 11,773) from the Growing Up Today Study. Exposure: Frequency of binge eating, laxative use, and self-induced vomiting over the past year was self-reported on repeated questionnaires during follow-up. Physician-diagnosed endometriosis was reported on repeated questionnaires during follow-up. Multivariable logistic regression models with generalized estimating equations were used to calculate adjusted odds ratios (aORs) and 95% confidence intervals (CIs). Over 25 years of follow-up, we identified 269 incident cases of endometriosis (2.3%), 190 of which were reported as laparoscopically confirmed. A total of 32% of girls reported ever binge eating, 14% reported self-induced vomiting to lose weight, and 9% reported ever using laxatives to lose weight. The odds of a laparoscopically-confirmed endometriosis diagnosis were more than three-fold higher (aOR = 3.07; 95% CI 1.74, 5.40) for girls who cumulatively reported self-induced vomiting more than monthly during follow-up, compared with girls who never reported self-induced vomiting. Cumulative exposure to binge eating during follow-up was not associated with diagnosis of laparoscopically-confirmed endometriosis; however, girls who reported the highest ever engagement in binge eating of weekly or more had 52% lower (aOR = 0.47; 95% CI 0.25, 0.90) odds of laparoscopically-confirmed endometriosis, compared with girls who reported less than weekly binge eating. Laxative use was not strongly associated with endometriosis diagnosis, although estimates were imprecise. Females with a greater frequency of self-induced vomiting were more likely to be diagnosed with endometriosis during follow-up, whereas girls with a history of frequent binge eating had a lower likelihood of endometriosis diagnosis. We found no association between laxative use and endometriosis.
Kicińska AM et al., 2023·Healthcare (Basel)·Free full text on PubMed Central
Polycystic ovary syndrome (PCOS) is the most common cause of anovulatory infertility. Absent, impaired, or rare ovulation induces progesterone deficiency in the luteal phase, which is a critical problem in PCOS. A usual pattern of progesterone administration from a fixed and arbitrary pre-determined day of a menstrual cycle may preserve infertility but can easily be avoided. We present the case of a 29-year-old infertile woman who had been ineffectively treated for over two years. We introduced a line of therapy that was suited to her individual menstrual cycle by implementing biomarker recording. Supplementation based on a standardized observation of the basal body temperature (BBT) and cervical mucus stopped the vicious circle of absent ovulation and hyperandrogenism, restoring regular bleeding, ovulation cycles, and fertility. The implementation of a reliable fertility awareness method (FAM), accompanied by a standardized teaching methodology and periodic review of the observations recorded by the patient, validated through an ultrasound examination and plasma gonadotropins, estrogens, and progesterone concentrations, is key to achieving therapeutic success. The presented case is an example of a clinical vignette for many patients who have successfully managed to improve their fertility and pregnancy outcomes by applying the principles of a personalized treatment approach together with gestagens by recording their fertility biomarkers.
Mu Q et al., 2023·Medicina (Kaunas)·Free full text on PubMed Central
Background and
Accuracy in detecting ovulation and estimating the fertile window in the menstrual cycle is essential for women to avoid or achieve pregnancy. There has been a rapid growth in fertility apps and home ovulation testing kits in recent years. Nevertheless, there lacks information on how well these apps perform in helping users understand their fertility in the menstrual cycle. This pilot study aimed to evaluate and compare the beginning, peak, and length of the fertile window as determined by a new luteinizing hormone (LH) fertility tracking app with the Clearblue Fertility Monitor (CBFM). A total of 30 women were randomized into either a quantitative Premom or a qualitative Easy@Home (EAH) LH testing system. The results of the two testing systems were compared with the results from the CBFM over three menstrual cycles of use. Potential LH levels for estimating the beginning of the fertile window were calculated along with user acceptability and satisfaction. The estimates of peak fertility by the Premom and EAH LH testing were highly correlated with the CBFM peak results (R = 0.99, p < 0.001). The participants had higher satisfaction and ease-of-use ratings with the CBFM compared to the Premom and EAH LH testing systems. LH 95% confidence levels for estimating the beginning of the fertile window were provided for both the Premom and EAH LH testing results. Our pilot study findings suggest that the Premom and EAH LH fertility testing app can accurately detect impending ovulation for women and are easy to use at home. However, successful utilization of these low-cost LH testing tools and apps for fertility self-monitoring and family planning needs further evaluation with a large and more diverse population.
To characterize how commercially available fertility-tracking devices and wearables define ovulation and anovulation, evaluate available comparator evidence, and assess user burden across 19 selected technologies.
We conducted a structured landscape analysis of 19 fertility-tracking devices and wearables available in the United States. Technologies were evaluated for biomarker type, operational definitions of ovulation and anovulation, comparator evidence, intended use, regulatory status, and user burden, among other variables.
Technologies clustered into four major categories: luteinizing hormone (LH)-only devices, multi-hormone devices, basal body temperature (BBT)-based wearables, and BBT-based thermometers. LH-only devices generally defined ovulation by detecting an LH surge or peak; multi-hormone devices incorporated LH and/or pregnanediol-3-glucuronide (PdG) measurements; and BBT-based technologies relied on thermal shifts. Explicit definitions of anovulation were uncommon and were frequently inferred from the absence of ovulation-associated biomarkers. The existence and design of comparator evidence varied substantially across technologies. Most comparator studies relied on surrogate measures such as urinary or serum hormones, whereas few technologies had comparator evidence against physiological reference standards such as transvaginal ultrasound (TVUS). Comparator studies in irregular-cycle populations were uncommon despite frequent marketing of these devices as appropriate for users with irregular cycles. User burden varied substantially across technologies.
Fertility-tracking technologies demonstrate substantial heterogeneity in operational definitions, comparator evidence, and user burden. Greater transparency regarding ovulation and anovulation definitions, clearer reporting of comparator evidence, and more representative evaluation in irregular-cycle populations are needed to support accurate interpretation and integration into reproductive healthcare.
Bouchard TP et al., 2026·Reproductive biomedicine online·Free to read
Do quantitative urinary hormone measurements on the Mira monitor predict and confirm ovulation accurately compared with ultrasound in women with regular menstrual cycles? Do Mira urine hormones correlate with serum hormones?
This was a prospective, single-centre, blinded diagnostic accuracy study with 52 women aged 19-44 years with regular cycles (24-38 days) who tracked 153 cycles over 18 months. Daily first-morning urine was tested with the Mira monitor for follicle stimulating hormone (FSH), oestrone-3-glucuronide (E13G), luteinizing hormone (LH) and pregnanediol glucuronide (PDG). Serial transvaginal ultrasounds (890 scans) confirmed the day of ovulation. Serum hormones were measured twice per cycle. The 121 ovulatory cycles from 49 participants with sufficient index test and reference standard data were included in the final analysis.
The Mira LH peak day strongly predicted ultrasound-confirmed ovulation (R² = 0.96, P < 0.001; intraclass correlation coefficient = 0.971), with 96% of ovulations occurring within ±1 day. The Mira PDG increase was also strongly associated with ultrasound-confirmed day of ovulation (R² = 0.87, P < 0.001). First-morning urine hormones were significantly associated with serum hormones when collected within 90 min (LH: R² = 0.92; E13G: R² = 0.73; R² = 0.61; R² = 0.75). Anovulatory cycles were identified in 11% of regularly cycling participants.
Quantitative urinary hormone monitoring with the Mira monitor provides accurate prediction and confirmation of ovulation, with strong urine-serum associations supporting reduced reliance on serial serum draws in select patients. These findings support clinical adoption of quantitative urinary fertility monitoring.
Kristine E Lynch, Sunni L Mumford, Karen C Schliep, Brian W Whitcomb, Anna Z Pollack, Audrey J Gaskins, Enrique F Schisterman, Michelle Danaher, Jean Wactawski-Wende, Shvetha M Zarek
K Lynch, S Mumford, K Schliep, B Whitcomb, A Pollack, A Gaskins, E Schisterman, M Danaher, J Wactawski-Wende, S Zarek
PMID 24875398 24875398 DOI 10.1016/j.fertnstert.2014.04.035 10.1016/j.fertnstert.2014.04.035 Lynch et al. 2014, Lynch 2014
Cite this article
Lynch, K. E., Mumford, S. L., Schliep, K. C., Whitcomb, B. W., Zarek, S. M., Pollack, A. Z., Bertone-Johnson, E. R., Danaher, M., Wactawski-Wende, J., Gaskins, A. J., & Schisterman, E. F. (2014). Assessment of anovulation in eumenorrheic women: comparison of ovulation detection algorithms. Fertility and sterility, 102(2), 511-518.e2. https://doi.org/10.1016/j.fertnstert.2014.04.035
Lynch KE, Mumford SL, Schliep KC, Whitcomb BW, Zarek SM, Pollack AZ, et al. Assessment of anovulation in eumenorrheic women: comparison of ovulation detection algorithms. Fertil Steril. 2014;102(2):511-518.e2. doi:10.1016/j.fertnstert.2014.04.035
Lynch, K. E., et al. "Assessment of anovulation in eumenorrheic women: comparison of ovulation detection algorithms." Fertility and sterility, vol. 102, no. 2, 2014, pp. 511-518.e2.
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