생리불순

The use of apps that record detailed menstrual cycle data presents a new opportunity to study the menstrual cycle. The aim of this study is to describe menstrual cycle characteristics observed from a large database of cycles collected through an app and investigate associations of menstrual cycle characteristics with cycle length, age and body mass index (BMI). Menstrual cycle parameters, including menstruation, basal body temperature (BBT) and luteinising hormone (LH) tests as well as age and BMI were collected anonymously from real-world users of the Natural Cycles app. We analysed 612,613 ovulatory cycles with a mean length of 29.3 days from 124,648 users. The mean follicular phase length was 16.9 days (95% CI: 10–30) and mean luteal phase length was 12.4 days (95% CI: 7–17). Mean cycle length decreased by 0.18 days (95% CI: 0.17–0.18, R2 = 0.99) and mean follicular phase length decreased by 0.19 days (95% CI: 0.19–0.20, R2 = 0.99) per year of age from 25 to 45 years. Mean variation of cycle length per woman was 0.4 days or 14% higher in women with a BMI of over 35 relative to women with a BMI of 18.5–25. This analysis details variations in menstrual cycle characteristics that are not widely known yet have significant implications for health and well-being. Clinically, women who wish to plan a pregnancy need to have intercourse on their fertile days. In order to identify the fertile period it is important to track physiological parameters such as basal body temperature and not just cycle length.

Study population: Totally, 17.4 million non-deviating BBT measurements and 1.4 million cycles were recorded by 124,648 anonymised users of the app. They were mostly residents of Sweden, the UK and USA. Users had a mean age of 30.3 (range: 18–45) and mean BMI of 23.6 (range: 15–50). 80% started using the app with the stated intention to prevent a pregnancy and recorded an average of 8.5 cycles (5.0 ovulatory) in the study. In all, 20% started using the app to plan a pregnancy and they recorded an average of 9.0 cycles (5.6 ovulatory) in the study.

Cycle selection: Out of 1.4 million cycles recorded by all eligible users, 3182 cycles were excluded due to pregnancy and 1886 were excluded due to being outside the 10–90 day accepted length range. Less than 1% of cycles were longer than 50 days in length. Totally, 665,603 cycles in which ovulation was not detected were excluded, of which 75% had valid temperatures entered on less than 50% of the days, which partly explains why the algorithm was unable to assign an estimated day of ovulation (EDO). An EDO was assigned in 724,134 cycles, of which 612,613 (85%) were included in the study due to having valid temperature entries entered on at least 50% of the days.

Validation of estimated day of ovulation: The distributions of the follicular and luteal phase lengths across the study population are used to validate the app’s algorithm EDO since there is good clinical data on the expected distributions of both. We compared the distribution of follicular and luteal phase lengths in our sample of 612,613 cycles to two reference data sets: a sample of 688 cycles obtained by Baird et al. and a sample of 327 cycles obtained by Lenton et al. (Supplementary Results). The adjusted phase length distribution is a close fit to that of Baird et al. and has a slightly higher fraction of short luteal phases than that of Lenton et al.

Cycle characteristics by cycle length: Table 1 lists the mean cycle lengths, follicular phase lengths, luteal phase lengths and bleed lengths with 95% confidence intervals (CI) in cohorts of cycles by cycle length (n = 612,613). Totally, 81,605 cycles (13%) were 28 days long and these cycles had mean follicular and luteal phase lengths of 15.4 and 12.6 days, respectively. Compared to the 560,078 normal length cycles (21–35 days), very short cycles had shorter bleed lengths by 0.5 days or 12% (95% CI: 0.4–0.5 days). Very long cycles had longer bleed lengths by 0.2 days or 6% (95% CI: 0.2–0.3 days). The very short cycles had shorter follicular phase by 5.4 days or 34% (95% CI: 5.3–5.5 days) and shorter luteal phases by 4.4 days or 35% (95% CI: 4.3–4.5 days). The very long cycles had longer follicular phase by 11.0 days or 66% (95% CI: 10.9–11.0 days) and longer luteal phases by 0.6 days or 5% (95% CI: 0.5–0.6 days). Less than 1% of cycles were longer than 50 days.

Cycle characteristics by user age: Table 2 lists the mean cycle length, follicular phase length, luteal phase length, bleed length and per-user cycle length variation in cohorts of cycles by user age (n = 612,613). Cycle length decreased with increasing age with a mean difference of 2.9 days or 10% (95% CI: 2.9–3.0) between the youngest and oldest cohorts. The bleed length reduced slightly with age with a mean difference of 0.5 days or 12% (95% CI: 0.4–0.5) between the youngest and oldest cohorts. Per-user cycle length variation reduced by 0.5 days or 20% (95% CI: 0.4–0.6 days) between the youngest and oldest cohorts. The follicular phase length became shorter with age with a mean difference of 3.2 days or 20% (95% CI: 3.2–3.3 days) between the youngest and oldest cohorts. The luteal phase length varied very little between age cohorts.

Figures 1–4, respectively, show mean cycle length, follicular phase length, luteal phase length and per-user cycle length variation against age with error bars corresponding to 95% CI. Each point is the mean value for cycles from users of equal age and the points are labelled with the number of users. In Figs 1 and 2, linear regressions are fitted in the age range 25–45 years with 95% CI shaded in pink. Age is negatively correlated with cycle length (slope = −0.176, 95% CI: −0.168 to −0.183, R2 = 0.994) and follicular phase length (slope = −0.194, 95% CI: −0.186 to −0.202, R2 = 0.987). Mean cycle length and follicular phase length in cycles from users aged 18–24 did not fit the linear regressions. In Fig. 3, there were no significant differences between luteal phase length with age. In Fig. 4, a linear regression is fitted to the per-user cycle length variation in the age range 25–40 years with 95% CI shaded in pink. Age is negatively correlated with the per-user cycle length variation (slope = −0.060, 95% CI: −0.055 to −0.066, R2 = 0.975). Below age 25 there is little change in mean cycle length variation, but the large confidence intervals suggest that some users have a large cycle length variation. Above age 40 the variation increased markedly to its highest level of 3.1 days at age 45.

Cycle characteristics by user BMI: Table 3 lists the mean cycle length, follicular phase length, luteal phase length and bleed length in cohorts of cycles by user BMI (n = 612,613). The median cohort was cycles from women with normal BMI (18.5–25). Underweight women (BMI 15–18.5) had a longer mean bleed length by 0.2 days or 5% (95% CI: 0.18–0.22 days) and morbidly obese women (BMI over 35) had higher cycle length variation by 0.4 days or 14% (95% CI: 0.3–0.5 days) and longer follicular phase length by 0.9 days or 5% (95% CI: 0.8–1.0 days), than women in the ‘healthy weight’ range (BMI 18.5–25). No other clinically significant differences between BMI cohorts were observed. Figure 5 shows the mean per-user cycle length variation against BMI with error bars corresponding to 95% CI. In the BMI range 18–35 the cycle length variation is flat and above a BMI of 35 it increases although the confidence intervals are very large.

The regularity of menstrual cycles is considered an indicator of women’s reproductive health. Previous studies with a cross-sectional design have documented the relationship between menstrual cycle irregularities, insulin-resistance and the future risks for metabolic disorders. Limited data documented by prospective studies can lead to premature conclusions regarding the relationship between menstrual cycle irregularities and other conditions influencing women’s health. The present study therefore, using a prospective design aimed to assess the risk of metabolic disorders in women with a history of irregular menstrual cycles, was based on the data gathered from the Tehran Lipid and Glucose study (TLGS) an ongoing prospective cohort study initiated in 1999. Participants of the current study were 2128 women, aged between 18–49 years, followed for 15 years. Based on their menstrual cycles, the women were divided into two groups: (i) women with regular menstrual cycles (n = 1749), and (ii) those with irregular menstrual cycles (n = 379). The proportional COX regression model was used to compare hazard ratios (HRs) between the groups for the proposed events, including diabetes mellitus (DM), pre-diabetes (pre-DM), hypertension (HTN), pre-hypertension (pre-HTN) and dyslipidemia. It was found that during a 15-year follow up, there were 123 cases of DM, 456 cases of pre-DM, 290 cases of HTN, 481 cases of pre-HTN, and 402 cases of dyslipidemia. Compared to those with regular cycles, women with irregular menstrual cycles were found to have an increased risk for DM2 (age adjusted Hazard Ratios (HRs), 2.01; 95% confidence intervals (CI:1.59–3.50), the increased risk for DM, associated with irregular cycles remained significant after the adjustment for Body Mass Index (BMI), fasting blood sugar (FBS), family history of diabetes, and parity (HRS, 1.73; 95% CI: 1.14–2.64). There was no significant difference in the increased risk for pre-DM between the groups (age adjusted HRs, 1.33, 95% CI: 1.05–1.69). However, after the adjustment of BMI, FBS and family history of pre-DM, compared to those with regular menstrual cycles, irregular menstrual cycles showed an increased risk for pre-DM (HRs, 1.33; 95% CI: 1.05–1.69). No statistically significant difference was found in the increasing risk for other proposed events between the groups demonstrating that menstrual cycle irregularities could be considered a marker of metabolic disorders and a predisposing factor of the increased risk for diabetes mellitus and pre-diabetes in women with irregular menstrual cycles.

The median duration of the follow up for cohort members was 12.7 years. We documented proposed events in this population, including 123 cases of DM, 290 cases of HTN, 402 cases of dyslipidemia, 456 cases of pre-diabetes and 481 cases of pre-HTN. Incidence rates for events are displayed in Fig 1.

Participants were 2128 women. Ofwhom,1749 women had regular menstrual cycles and 379 had irregular menstrual cycles; mean (±SD) age of participants was 29.45 ± 9.16 years and 27.02 ± 8.62 years, respectively (Table 1).

Group1 Participants were more educated (p<0.01), were mostly married and had a higher mean parity (p<0.01), compared to Group 2.; women with irregular menstrual cycles showed a higher triglyceride levels and a higher prevalence of dyslipidemia at baseline, compared to those who had regular menstrual cycles(p<0.05).

Statistically significant differences based on the Kaplan-Miere survival curves between the study groups with regard to DM and pre-DM were observed (Figs 2 and 3).

Compared to women with regular menstrual cycles, the age adjusted hazard ratio for DM events in women with irregular menstrual cycles was 2.01, 95% CI = 1.59–3.50, and for pre-DM events this was 1.24, 95% CI =.099–1.57.

Multivariate-adjusted HRs for DM and pre-DM were 1.73, 95% CI = 1.14–2.64 and 1.33, 95% CI = 1.05–1.69, respectively. After multivariate adjustment, the hazard ratio of DM between the groups decreased, but remained significant (p<0.05) (Table 2).

There was no statistically significant difference between HRs of pre-DM between the groups before incorporation of the adjustment model, whereas these HRs of pre-DM significantly increased in women with irregular menstrual cycles after following it.

Incidence rates of dyslipidemia, HTN, and pre-HTN did not differ significantly between the groups; older women with higher levels of BMI and SBP were mainly at risk for future hypertension (p<0.001). However, menstrual cycle status had no effects on risk of future hypertension (Table 2). It was also shown that the women with higher levels of TG, LDL, and cholesterol had higher risk for dyslipidemia, whereas menstrual cycle status had no effects on the risk of future dyslipidemia; in addition, parity did not increase the risk for proposed events.