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Cognitive Behaviour Therapy May 2019Fluctuations in ovarian hormones over the menstrual cycle contribute to cigarette reward, however less is known about menstrual cycle influences on emotional distress...
Fluctuations in ovarian hormones over the menstrual cycle contribute to cigarette reward, however less is known about menstrual cycle influences on emotional distress in female smokers. We examined between-group differences in emotional distress (negative affectivity, emotion dysregulation, distress intolerance) and hypothetical cigarette purchasing (i.e. tobacco demand) among female smokers at three different menstrual stages. Women (n = 32) were non-treatment seeking daily smokers not on hormonal contraceptive, and were currently in their follicular (estradiol-dominant; n = 10), early-mid luteal (progesterone-dominant; n = 15), and late-luteal phase (decreasing progesterone/estradiol; n = 7). Effect sizes are reported given the small sample. Women in the late-luteal phase, relative to the follicular and early-mid luteal phases, reported higher levels of negative affectivity (d = 0.69), emotion dysregulation (d = 1.03), and distress intolerance (d = -0.86). Compared to the early-mid luteal and late-luteal phases, women in the follicular phase reported the highest hypothetical cigarette consumption when cigarettes were free (d = 0.71) and made the largest maximum expenditures on cigarettes (d = 0.74). Findings offer preliminary evidence that the late-luteal phase is characterized by emotional distress, and the follicular phase is associated with elevated tobacco demand, which if replicated could implicate ovarian hormones in emotion-focused smoking.
Topics: Craving; Emotional Regulation; Female; Follicular Phase; Humans; Luteal Phase; Menstrual Cycle; Psychological Distress; Smokers
PubMed: 30064348
DOI: 10.1080/16506073.2018.1494208 -
Medicina (Kaunas, Lithuania) Jul 2023: Menstrual cycle tracking is essential for reproductive health and overall well-being. However, there is still an over-reliance on estimations that standard cycles are...
: Menstrual cycle tracking is essential for reproductive health and overall well-being. However, there is still an over-reliance on estimations that standard cycles are 28 days long, divided evenly between the follicular and luteal phases. Due to the variability of cycle length and cycle phase lengths, common methods of identifying where an individual is in their cycle are often inaccurate. This study used daily hormone monitoring obtained through a remote hormone-monitoring platform to evaluate hormone levels across a menstrual cycle to identify nuances in the follicular and luteal phases in individuals of different age groups. : This study used a remote fertility testing system that quantitatively tracks luteinizing hormone (LH) and pregnanediol-3-glucuronide (PdG) through urine tests read by an AI-powered smartphone app. The study analyzed cycle data from 1233 users with a total of 4123 evaluated cycles. Daily levels for LH and PdG were monitored across multiple cycles. : This study determined that calculated cycle lengths tended to be shorter than user-reported cycle lengths. Significant differences were observed in cycle phase lengths between age groups, indicating that follicular phase length declines with age while luteal phase length increases. Finally, the study found that if an individual's age, first cycle day, and current hormone levels are known, population-level hormone data can be used to pinpoint which cycle phase and cycle day they are in with 95% confidence. : At-home hormone monitoring technologies can allow patients and clinicians to track their cycles with greater precision than when relying on textbook estimations. The study's findings have implications for fertility planning, clinical management, and general health monitoring. Prior to this study, no standard existed for pinpointing where a person was in their cycle through only one measure of LH and PdG. These findings have the potential to fill significant gaps within reproductive healthcare and beyond.
Topics: Female; Humans; Menstrual Cycle; Luteinizing Hormone; Luteal Phase; Follicular Phase
PubMed: 37512159
DOI: 10.3390/medicina59071348 -
Journal of Medical Internet Research Oct 2023Menstrual cycle tracking apps (MCTAs) have potential in epidemiological studies of women's health, facilitating real-time tracking of bleeding days and...
BACKGROUND
Menstrual cycle tracking apps (MCTAs) have potential in epidemiological studies of women's health, facilitating real-time tracking of bleeding days and menstrual-associated signs and symptoms. However, information regarding the characteristics of MCTA users versus cycle nontrackers is limited, which may inform generalizability.
OBJECTIVE
We compared characteristics among individuals using MCTAs (app users), individuals who do not track their cycles (nontrackers), and those who used other forms of menstrual tracking (other trackers).
METHODS
The Ovulation and Menstruation Health Pilot Study tested the feasibility of a digitally enabled evaluation of menstrual health. Recruitment occurred between September 2017 and March 2018. Menstrual cycle tracking behavior, demographic, and general and reproductive health history data were collected from eligible individuals (females aged 18-45 years, comfortable communicating in English). Menstrual cycle tracking behavior was categorized in 3 ways: menstrual cycle tracking via app usage, that via other methods, and nontracking. Demographic factors, health conditions, and menstrual cycle characteristics were compared across the menstrual tracking method (app users vs nontrackers, app users vs other trackers, and other trackers vs nontrackers) were assessed using chi-square or Fisher exact tests.
RESULTS
In total, 263 participants met the eligibility criteria and completed the digital survey. Most of the cohort (n=191, 72.6%) was 18-29 years old, predominantly White (n=170, 64.6%), had attained 4 years of college education or higher (n= 209, 79.5%), and had a household income below US $50,000 (n=123, 46.8%). Among all participants, 103 (39%) were MCTA users (app users), 97 (37%) did not engage in any tracking (nontrackers), and 63 (24%) used other forms of tracking (other trackers). Across all groups, no meaningful differences existed in race and ethnicity, household income, and education level. The proportion of ever-use of hormonal contraceptives was lower (n=74, 71.8% vs n=87, 90%, P=.001), lifetime smoking status was lower (n=6, 6% vs n=15, 17%, P=.04), and diagnosis rate of gastrointestinal reflux disease (GERD) was higher (n=25, 24.3% vs n=12, 12.4%, P=.04) in app users than in nontrackers. The proportions of hormonal contraceptives ever used and lifetime smoking status were both lower (n=74, 71.8% vs n=56, 88.9%, P=.01; n=6, 6% vs n=11, 17.5%, P=.02) in app users than in other trackers. Other trackers had lower proportions of ever-use of hormonal contraceptives (n=130, 78.3% vs n=87, 89.7%, P=.02) and higher diagnostic rates of heartburn or GERD (n=39, 23.5% vs n=12, 12.4%, P.03) and anxiety or panic disorder (n=64, 38.6% vs n=25, 25.8%, P=.04) than nontrackers. Menstrual cycle characteristics did not differ across all groups.
CONCLUSIONS
Our results suggest that app users, other trackers, and nontrackers are largely comparable in demographic and menstrual cycle characteristics. Future studies should determine reasons for tracking and tracking-related behaviors to further understand whether individuals who use MCTAs are comparable to nontrackers.
Topics: Humans; Female; Adolescent; Young Adult; Adult; Menstruation; Cross-Sectional Studies; Pilot Projects; Mobile Applications; Menstrual Cycle; Ovulation; Gastrointestinal Diseases; Contraceptive Agents; Gastroesophageal Reflux
PubMed: 37889545
DOI: 10.2196/42164 -
Scientific Reports Aug 2021The ability to predict an individual's menstrual cycle length to a high degree of precision could help female athletes to track their period and tailor their training...
The ability to predict an individual's menstrual cycle length to a high degree of precision could help female athletes to track their period and tailor their training and nutrition correspondingly. Such individualisation is possible and necessary, given the known inter-individual variation in cycle length. To achieve this, a hybrid predictive model was built using data on 16,524 cycles collected from a sample of 2125 women (mean age 34.38 years, range 18.00-47.10, number of menstrual cycles ranging from 4 to 53). A mixed-effect state-space model was fitted to capture the within-subject temporal correlation, incorporating a Bayesian approach for process forecasting to predict the duration (in days) of the next menstrual cycle. The modelling procedure was split into three steps (1) a time trend component using a random walk with an overdispersion parameter, (2) an autocorrelation component using an autoregressive moving-average model, and (3) a linear predictor to account for covariates (e.g. injury, stomach cramps, training intensity). The inclusion of an overdispersion parameter suggested that [Formula: see text] [Formula: see text] of cycles in the sample were overdispersed. The random walk standard deviation for a non-overdispersed cycle is [Formula: see text] [1.00, 1.09] days while under an overdispersed cycle, the menstrual cycle variance increase in 4.78 [4.57, 5.00] days. To assess the performance and prediction accuracy of the model, each woman's last observation was used as test data. The root mean square error (RMSE), concordance correlation coefficient and Pearson correlation coefficient (r) between the observed and predicted values were calculated. The model had an RMSE of 1.6412 days, a precision of 0.7361 and overall accuracy of 0.9871. In conclusion, the hybrid model presented here is a helpful approach for predicting menstrual cycle length, which in turn can be used to support female athlete wellness.
Topics: Adolescent; Adult; Age Factors; Athletes; Bayes Theorem; Body Mass Index; Female; Humans; Markov Chains; Menstrual Cycle; Middle Aged; Models, Biological; Young Adult
PubMed: 34417493
DOI: 10.1038/s41598-021-95960-1 -
Current Psychiatry Reports Oct 2021In contrast to premenstrual dysphoric disorder (PMDD), premenstrual exacerbations (PMEs) of ongoing mood disorders are understudied. The aim of this review is to... (Review)
Review
PURPOSE OF REVIEW
In contrast to premenstrual dysphoric disorder (PMDD), premenstrual exacerbations (PMEs) of ongoing mood disorders are understudied. The aim of this review is to describe diagnostic issues, epidemiology, underlying mechanisms, and treatment for PME in unipolar depression and bipolar disorder, and to discuss clinical and research implications.
RECENT FINDINGS
Community-based and clinical studies estimate that in women with mood disorders around 60% report PME, while some women with bipolar disorder also show symptom exacerbations around ovulation. In general, PME predicts a more severe illness course and an increased burden. While heightened sensitivity to fluctuations of sex hormone levels across the menstrual cycle appears to contribute to PME and PMDD, the overlap of their underlying biological mechanisms remains unclear. Beneficial treatments for PMDD show less or no efficacy in PME. Pharmacological treatments for PME in mood disorders predominantly seem to profit from adjustable augmentation of treatment dosages during the luteal phase for the underlying disorder. However, the evidence is sparse and mainly based on earlier small studies and case reports. Previous research is mainly limited by the lack of a clear differentiation between PME and PMDD comorbidity with mood disorders. More systematic research with uniformly defined and prospectively assessed subgroups of PME in larger epidemiological and clinical samples is needed to receive reliable prevalence estimates and information on the clinical impact of PME of mood disorders, and to uncover underlying mechanisms. In addition, larger randomized controlled trials are warranted to identify efficacious pharmacological and psychotherapeutic treatments for affected women.
Topics: Female; Humans; Luteal Phase; Menstrual Cycle; Mood Disorders; Premenstrual Dysphoric Disorder; Premenstrual Syndrome
PubMed: 34626258
DOI: 10.1007/s11920-021-01286-0 -
Fertility and Sterility Oct 2017The first live birth after IVF was achieved in a purely natural cycle. Because early attempts at IVF were associated with low efficiency, ovarian stimulation was added... (Review)
Review
The first live birth after IVF was achieved in a purely natural cycle. Because early attempts at IVF were associated with low efficiency, ovarian stimulation was added to achieve a greater margin for error in oocyte retrieval, fertilization, and thus, overall pregnancy success. As technology improved, the intuitive appeal of the natural cycle led investigators to once again attempt IVF without antecedent gonadotropin stimulation. Triggering of ovulation with hCG was added to allow for accurate scheduling of oocyte retrieval and thus increased oocyte yield. When GnRH antagonists became available, premature ovulations could be prevented, albeit at the cost of adding some form of ovarian stimulation to continue follicle development until ovulation triggering. This type of cycle came to be known as the "modified natural cycle." These modified natural IVF cycles are associated with decreased medication costs, they produce acceptable pregnancy rates, and they may be particularly appropriate for patients at increased risk of ovarian hyperstimulation syndrome, poor responders, and those wishing to avoid supernumerary embryo production.
Topics: Female; Fertilization in Vitro; Humans; Menstrual Cycle; Oocyte Retrieval; Ovarian Hyperstimulation Syndrome; Ovulation Induction; Pregnancy; Pregnancy Rate
PubMed: 28965551
DOI: 10.1016/j.fertnstert.2017.08.021 -
Physiology & Behavior Oct 2017Sex differences and menstrual cycle influences have been investigated in a variety of cognitive abilities, but results regarding attention are comparably sparse. In the...
Sex differences and menstrual cycle influences have been investigated in a variety of cognitive abilities, but results regarding attention are comparably sparse. In the present study, 35 men and 32 naturally cycling women completed three attention tasks, which are commonly used in neuropsychological assessment situations. All participants completed two sessions, which were time-locked to the follicular (low progesterone) and luteal cycle phase (high progesterone) in women. The results reveal higher operation speed during sustained attention in men, but no sex differences in selected and divided attention. Menstrual cycle influences were observed on accuracy in all three tasks. During divided and sustained attention, for which a male advantage was previously reported, accuracy was higher during the early follicular compared to the mid-luteal cycle phase. Furthermore, during selected and sustained attention the learning effect from the first to the second test session was higher in women who started the experiment in their luteal cycle phase. These results suggest a possible role of progesterone in modulating the ability to focus on certain stimulus aspects, while inhibiting others and to sustain attention over a longer period of time.
Topics: Attention; Female; Humans; Learning; Male; Menstrual Cycle; Neuropsychological Tests; Sex Characteristics; Young Adult
PubMed: 28694156
DOI: 10.1016/j.physbeh.2017.07.012 -
European Radiology May 2022Quantitative computed tomography (qCT) is being increasingly incorporated in research studies and clinical trials aimed at understanding lung disease risk, progression,...
OBJECTIVE
Quantitative computed tomography (qCT) is being increasingly incorporated in research studies and clinical trials aimed at understanding lung disease risk, progression, exacerbations, and intervention response. Menstrual cycle-based changes in lung function are recognized; however, the impact on qCT measures is currently unknown. We hypothesize that the menstrual cycle impacts qCT-derived measures of lung structure in healthy women and that the degree of measurement change may be mitigated in subjects on cyclic hormonal birth control.
METHODS
Thirty-one non-smoking, healthy women with regular menstrual cycles (16 of which were on cyclic hormonal birth control) underwent pulmonary function testing and qCT imaging at both menses and early luteal phase time points. Data were evaluated to identify lung measurements which changed significantly across the two key time points and to compare degree of change across metrics for the sub-cohort with versus without birth control.
RESULTS
The segmental airway measurements were larger and mean lung density was higher at menses compared to the early luteal phase. The sub-cohort with cyclic hormonal birth control did not have less evidence of measurement difference over the menstrual cycle compared to the sub-cohort without hormonal birth control.
CONCLUSIONS
This study provides evidence that qCT-derived measures from the lung are impacted by the female menstrual cycle. This indicates studies seeking to use qCT as a more sensitive measure of cross-sectional differences or longitudinal changes in these derived lung measurements should consider acquiring data at a consistent time in the menstrual cycle for pre-menopausal women and warrants further exploration.
KEY POINTS
• Lung measurements from chest computed tomography are used in multicenter studies exploring lung disease progression and treatment response. • The menstrual cycle impacts lung structure measurements. • Cyclic variability should be considered when evaluating longitudinal change with CT in menstruating women.
Topics: Cross-Sectional Studies; Female; Humans; Lung; Menstrual Cycle; Respiratory Function Tests; Tomography, X-Ray Computed
PubMed: 34928413
DOI: 10.1007/s00330-021-08404-9 -
Brain Research Jan 2019The female reproductive hormones progesterone and estrogen regulate network excitability. Fluctuations in the circulating levels of these hormones during the menstrual... (Review)
Review
The female reproductive hormones progesterone and estrogen regulate network excitability. Fluctuations in the circulating levels of these hormones during the menstrual cycle cause frequent seizures during certain phases of the cycle in women with epilepsy. This seizure exacerbation, called catamenial epilepsy, is a dominant form of drug-refractory epilepsy in women of reproductive age. Progesterone, through its neurosteroid derivative allopregnanolone, increases γ-aminobutyric acid type-A receptor (GABAR)-mediated inhibition in the brain and keeps seizures under control. Catamenial seizures are believed to be a neurosteroid withdrawal symptom, and it was hypothesized that exogenous administration of progesterone to maintain its levels high during luteal phase will treat catamenial seizures. However, in a multicenter, double-blind, phase III clinical trial, progesterone treatment did not suppress catamenial seizures. The expression of GABARs with reduced neurosteroid sensitivity in epileptic animals may explain the failure of the progesterone clinical trial. The expression of neurosteroid-sensitive δ subunit-containing GABARs is reduced, and the expression of α4γ2 subunit-containing GABARs is upregulated, which alters the inhibition of dentate granule cells in epilepsy. These changes reduce the endogenous neurosteroid control of seizures and contribute to catamenial seizures.
Topics: Epilepsy; Estrogens; Female; Humans; Menstrual Cycle; Neurons; Neurosteroids; Pregnanolone; Progesterone; Receptors, GABA; Receptors, GABA-A; Seizures
PubMed: 29481795
DOI: 10.1016/j.brainres.2018.02.031 -
Biology of Reproduction May 2020Here we have summarized what is currently known about menstruating animal species with special emphasis on non-primate species: length of their menstrual cycle,... (Review)
Review
Here we have summarized what is currently known about menstruating animal species with special emphasis on non-primate species: length of their menstrual cycle, ovulation, implantation, placentation, decidualization, and endometrial characteristics. Having an overview of all the possible animal models that can be used to study menstruation and the menstrual cycle could be useful to select the one that better matches the needs of the individual research projects. The most promising species to study menstruation seems to be the spiny mouse Acomys cahirinus. It is a rodent that could be easily held in the existing laboratory facilities for rats and mice but with the great advantage of having spontaneous menstruation and several human-like menstrual cycle characteristics. Among the species of menstruating bats, the black mastiff bat Molossus ater and wild fulvous fruit bat Rousettus leschenaultii are the ones presenting the most human-like characteristics. The elephant shrew seems to be the less suitable species among the ones analyzed. The induced mouse model of menstruation is also presented as an adaptable alternative to study menstruation.
Topics: Animals; Endometrium; Female; Humans; Menstrual Cycle; Menstruation; Models, Animal
PubMed: 32129461
DOI: 10.1093/biolre/ioaa029