Timing of Menopause and Patterns of Menstrual Bleeding

M. Weinstein1 , T. Gorrindo2, A. Riley1, J. Mormino1, J. Niedfeldt1, B. Singer3, G. Rodríguez3, J. Simon4 and S. Pincus5

1 Center for Population and Health, Graduate School of Arts and Sciences, Georgetown University, Washington, DC.
2 Vanderbilt University School of Medicine, Nashville, TN.
3 Office of Population Research, Princeton University, Princeton, NJ.
4 Department of Obstetrics and Gynecology, George Washington University School of Medicine, Washington, DC.
5 Mathematician, Guilford, CT.

Received for publication January 10, 2003; accepted for publication April 28, 2003.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Age at menopause is associated with a variety of health outcomes. Menstrual histories, both as markers of physiologic function and through their potential association with age at menopause, have also been investigated for their links to health outcomes. This study used data from a cohort of women from the United States who provided prospectively recorded data on their menstrual cycles for many years. Dr. Alan Treloar (University of Minnesota) originally recruited the women in the 1930s; the authors used data reported by these women from 1930 through 1977. They identified nuanced characteristics of menstrual histories that were strongly predictive of the onset of menopause, focusing on measures of central tendency (the mean), variability (standard deviation), and serial irregularity (approximate entropy), the last of which quantifies a continuum that ranges from totally ordered to completely random. They controlled for other characteristics known to affect age at menopause, including use of exogenous hormones, number of births, and extent of breastfeeding. Although cycle length and variability increased with the approach of menopause, the authors found that serial irregularity decreased and was a strong predictor of its onset. This finding constitutes an important piece of information not attainable with conventional statistical summaries of menstrual histories.

age of onset; menopause; menstrual cycle

Abbreviations: Abbreviation: ApEn, approximate entropy.


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Over the past two decades, an expanding body of literature has documented links between a woman’s age at menopause and her health. Menstrual cycle patterns, as markers of physiologic function and through their potential association with age at menopause, have also been investigated for their links to health outcomes. The purpose of this study was to identify more highly nuanced features of menstrual histories than have been examined previously and to demonstrate their relation to age at menopause. These linkages are an important step toward a deeper understanding of the precursors of a broad range of health outcomes.

Early menopause has been tied to a number of negative outcomes, including a higher risk of cardiovascular disease (1, 2), osteoporosis (2, 3), and higher all-cause mortality (46). Late age at menopause has been found to be associated with a higher risk of breast (2, 7) and endometrial (2, 8) cancers. Menstrual cycle characteristics, most notably length of the intermenstrual interval, have also been identified as risk factors for a variety of health conditions including polycystic ovary syndrome (9), coronary heart disease (10), and risk of fracture (11). Some investigators (1214) have also found a link between diabetes and cycle characteristics, although recent work (15) found no association between adult-onset diabetes and mean cycle length, number of long cycles, or cycle variability.

Along with parity (4, 16), use of oral contraceptives (16), and age at menarche (17), menstrual cycle characteristics also appear to be associated with age at menopause. While results across studies appear to show consistent links with respect to cycle length, findings about cycle variability are mixed. Late age at menopause is associated with longer mean cycle length (18, 19); similarly, short cycle lengths are associated with early onset of menopause (2, 16). However, den Tonkelaar et al. (18) and Whelan et al. (2) found no significant relation between cycle length variability and age at menopause.

We examined the link between bleeding patterns throughout a woman’s reproductive lifetime and her age at menopause; characteristics of the full menstrual history were considered. We identified characteristics of bleeding patterns that may provide prospective criteria for identifying the menopausal transition. Prior studies of cycle characteristics have focused on first- and second-order descriptive statistics (measures of central tendency and spread). As described below, we used a measure of approximate entropy (ApEn) to add an important dimension to earlier work. Whereas a measure of spread such as the standard deviation or range captures the variability of cycles, ApEn is a measure that enabled us to quantify their serial irregularity. Because common parlance with regard to menstrual cycles uses "regularity" to refer to statistical variability (women typically report having regular or irregular cycles, meaning that cycle lengths are not very variable or highly variable, respectively), we paid particular attention to characterizing the distinction between these two concepts. We also emphasized the new information captured by ApEn and described how it augments standard descriptive statistics in identifying features of menstrual histories linked to age at menopause.

We performed two sets of complementary analyses. First, we examined survival models following women from age 40 years forward (although we included information on events that occurred before, as well as after, age 40 years) and examined how cycle characteristics influence the age-related hazard of menopause. Second, we looked backward from menopause and explored diagnostic characteristics of perimenopausal menstrual patterns. Thus, we explored both prospective and retrospective links between cycle characteristics and menopause.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Dr. Alan Treloar (University of Minnesota) initiated the Menstruation and Reproductive History Study in 1934. Subsequently renamed "The Tremin Trust" and deeded to the University of Utah, it was directed by Dr. Ann Voda until 1998, when the study moved to The Pennsylvania State University under the direction of Dr. Phyllis Mansfield. The original goal was "to define quantitatively the rhythm pattern of the human menstrual cycle through current recording of dates of onset and cessation of flow" (20, p. 78). Over time, in addition to prospectively recorded data on onset and duration of successive bleeds, women contributed data relating to their use of exogenous hormones, marital and reproductive histories, surgical interventions, and a wide range of other information. A second cohort of women was recruited during the 1960s, but our study used data from a subset of women drawn from the original recruits (born between 1908 and 1922) who were still enrolled in the study at age 40 years. The institutional review board at Georgetown University (Washington, DC) approved this project.

We first used proportional hazards models to examine the relation between age at menopause and a set of covariates. Women entered exposure to the risk of menopause at age 40 years and exited because they were censored (either temporarily or permanently) or reached menopause. We assumed that exposure began at age 40 years because none of the women we observed experienced menopause before that age. Our sample was limited to 444 women who contributed data before age 40 years. Reasons for exclusion prior to age 40 years included death, loss to follow-up, withdrawal from the study, sterilization, and use of exogenous hormones throughout their history or without a defined endpoint. Because we used cycle characteristics prior to age 40 years as predictors of onset of menopause, we also excluded three women all of whose observed cycles prior to age 40 years were affected by pregnancy or breastfeeding. Data for the 444 women were incorporated into our models until the women reached menopause or were (permanently) censored owing to death, loss to follow-up, or sterilization. Data on surgical sterilization were not complete for some women: type of procedure was not always reported. In some cases, we knew only that a subject was "sterilized" and the date of the procedure. It is possible that fallopian tubes were interrupted without interfering with the uterus or ovaries or that only one ovary was removed (25 women reported unilateral oophorectomies). However, we had too few cases (20 unspecified "sterilizations") to estimate such an effect in our models. Therefore, we censored all women (those who reported a hysterectomy, unilateral or bilateral oophorectomy, or unspecified sterilization) at the time of surgery.

Table 1 shows the number of women, by category, excluded from the analysis. More than half were excluded because of only partial information on use of exogenous hormones. In principle, women recorded start and stop dates for all sustained treatments (including oral contraceptives); however, many stop dates were missing, so we used a conservative approach in our analysis, excluding spells for which we were uncertain of hormonal status.


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TABLE 1. Subjects excluded prior to age 40 years, by cause, from the analysis of data from the Menstruation and Reproductive History Study (The Tremin Trust), United States, 1930–1977
 
Observations could also be (temporarily) censored for a variety of reasons: hormonal treatments, pregnancy, breastfeeding, and breaks in reporting. For hormonal treatments, we censored data beginning with onset of treatment and extending 4 months following the end of treatment to allow for hormonal washout. Similarly, women contributed data 4 months after they reported the end of pregnancy or breastfeeding.

We calculated the overall hazard of menopause by using Kaplan-Meier estimates; we used Cox survival models to adjust for covariates (21). We calculated robust standard errors of the hazard ratios that adjusted for clustering attributable to multiple observations per subject. Although our primary interest lay in the relation between age at menopause and cycle patterns, we controlled for other factors known or hypothesized to be associated with age at menopause: age at menarche, number of pregnancies, breastfeeding, and use of exogenous hormones. We examined the effect of cycle patterns after age 40 years, net of these other factors, by using nested models.

Our hazard models followed each subject until she reached menopause or was permanently censored. Women were censored as discussed above or if menopause was not confirmed or was confounded by hormonal treatment. Our models included a set of variables that described each woman’s experience. These variables included fixed characteristics and several that change over time. Their distributions in our population are summarized in table 2.


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TABLE 2. Distribution of characteristics of the sample from the Menstruation and Reproductive History Study (The Tremin Trust), United States, 1930–1977
 
Dependent variable: age at menopause
This variable was defined as age at the start of the last reported menstrual cycle prior to confirmed menopause (defined as at least a year without report of a menstrual bleed or menopause confirmed by the participant or the director of The Tremin Trust).

Menstrual cycle characteristics before age 40 years
Mean and standard deviation
For each woman, we calculated the mean and standard deviation of menstrual cycle lengths in four 5-year age groups. Models that used the median and interquartile range (data not shown) revealed no substantive differences in the results. We used only those cycles in which women were not pregnant, breastfeeding, within 4 months postpartum, or using exogenous hormones.

For some subjects, data were insufficient to calculate menstrual characteristics in one or more of the age categories. This deficiency can occur because a subject had long breaks in the information reported, but this cause was rare in our data. More commonly, women experienced cycles that were excluded owing to their use of exogenous hormones, pregnancy, or breastfeeding. Our models included a dummy variable to allow for missing data, and we assigned the mean for all women at that age as their value for the variable.

ApEn
To quantify irregularity, we used ApEn, a model-independent statistic defined by Pincus (22), with further mathematical properties and representative biologic applications given by Pincus (23), Pincus and Goldberger (24), Pincus and Huang (25), Pincus and Singer (26), and Pincus et al. (27, 28). ApEn was introduced to quantify regularity in sequences and time-series data, initially motivated by applications to relatively short, noisy data (24, 26). This statistic has been used extensively to characterize the degree of randomness in a variety of applications that explore regularity in physiologic systems, including patterns of hormonal secretion and heart rate dynamics (2731). ApEn assigns a nonnegative number to a time-series; larger values correspond to greater apparent process randomness or serial irregularity, and smaller values correspond to more instances of recognizable features of patterns in the data. The opposing extremes are perfectly regular sequences (very small ApEn) and independent sequential processes (very large ApEn). Two input parameters—a run length m and a tolerance window r—must be specified to compute ApEn, formally denoted ApEn(m,r). Briefly, ApEn measures the logarithmic likelihood that runs of patterns close (within r) for m contiguous observations remain close (within the same tolerance width r) on incremental comparisons (22). ApEn(m,r) must be considered a family of parameters; comparisons are intended with fixed m and r.

For this study, we calculated ApEn(m,r) values for all data with well-established parameter values of m = 1 and r = 20 percent of the standard deviation of the overall subject time series, applied to running blocks of 50 data points. Because r thus specified had a value of less than 1.0 and because cycle lengths were reported in integer or full-day values rather than as a continuous measurement, the choice of r for the present analysis "reduced" to an application of ApEn with m = 1 and r = 0 (exact pairwise matches in the data). For these m and r parameter values, and for series of 50 data points as studied here, ApEn ranged from a minimal value of 0 for perfectly regular series to a maximum of 1.493 for completely disordered series. Thus, a 10 percent change in ApEn corresponded to an ApEn increment of approximately 0.15 here. Multiple previous studies, including both theoretical analyses and applications (2230), have demonstrated that the choice of m = 1 and r = 20 percent of the standard deviation as parameters (as above) produces good statistical reproducibility (one ApEn standard deviation <= 0.06 under very general conditions) for ApEn for time series of the kinds of lengths we analyzed.

In discrete-state space with r = 0, asymptotically, ApEn equals the information theoretical rate of entropy (23), interpreted as the average information content per source output symbol. In other model-based settings, ApEn can be evaluated analytically by either multiple integral (continuous-state) or sum (discrete-state) expressions (22). Further technical discussion of the mathematical and statistical properties of ApEn, including robustness to noise and artifacts, relative consistency of (m,r) pair choices, asymptotic normality under general assumptions, statistical bias, and error estimation for general processes, can be found in Pincus (23), Pincus and Goldberger (24), and Pincus and Huang (25). To develop a more intuitive, physiologic understanding of the ApEn definition, a multistep description of its typical algorithmic implementation, with figures, was developed in Pincus and Goldberger.

Because "regularity" in the context of menstrual cycles is commonly used to describe cycles with low variation, we clarified how the two concepts differ. Figure 1 shows two rows of (hypothetical) data. In the top row are patterns of cycles, alternating high and low values, characterized by low serial irregularity (indeed, both are perfectly regular; ApEn = 0 for each). The graph on the left shows data that are highly variable—characterized by a high standard deviation—while the graph on the right shows data that have low variability (a low standard deviation). In the bottom row are patterns of cycles characterized by high serial irregularity (high ApEn) and high and low variability (left and right, respectively). The ApEn value for the left panel of this row is 1.31; for the right panel, it is 1.34. Thus, both of these series are at least 85 percent maximally irregular. Yet, despite the lack of obvious repetitive features in each of these bottom panels, each has an ApEn value more than 0.15 smaller than the maximal value, that is, at least 2.5 ApEn standard deviations below maximal irregularity.



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FIGURE 1. Hypothetical data comparing variability and serial irregularity (approximate entropy (ApEn)) of menstrual cycles. m, run length; r, tolerance window; N, length of the series.

 
Figure 2 shows two "typical" histories that appeared in our data. The graphs display the length in days (y-axis) of successive intermenstrual intervals (the intervals between successive onsets of menses) by age (x-axis) for two different women. Statistics for these two women (mean, standard deviation, and ApEn) are shown in figure 3. Woman 1’s history reflects fairly level variability in cycle length throughout most of her reproductive life; the variability of woman 2’s cycles was greater. Variability (and mean cycle length) for both women increased as they approached the menopausal transition. For woman 1, ApEn values were high and level until the menopausal transition and then declined sharply. Until the menopausal transition, changes in her cycle lengths appeared to be highly random (high ApEn); during the menopausal transition, her cycles were highly variable but exhibited less randomness (declining ApEn). The cycles of woman 2 were characterized by greater variation in mean, standard deviation, and ApEn statistics throughout most of her reproductive history, but, as observed for woman 1, regularity increased (ApEn declines) during the menopausal transition.



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FIGURE 2. Profiles of two women’s histories of intermenstrual intervals. Data from the Menstruation and Reproductive History Study (The Tremin Trust), United States, 1930–1977.

 


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FIGURE 3. Summary statistics for profiles of two women’s histories of intermenstrual intervals. Data from the Menstruation and Reproductive History Study (The Tremin Trust), United States, 1930–1977. Std Dev, standard deviation; ApEn, approximate entropy.

 
Menstrual cycle characteristics at age 40 years or older: mean, standard deviation, and ApEn
We included menstrual cycle information as time-varying covariates, estimated for 3-year intervals, from age 40 years forward. A dummy variable was introduced into the model to enable us to assess whether the hazard for women for whom data were missing for any interval was statistically significantly different from the hazard for those who reported data. Intervals for which data were missing were assigned the mean value at that age.

Other covariates
Age at menarche
When first recruited, subjects reported how old they were at the time of their first menses. Because most were college students at the University of Minnesota, recall of age at menarche was probably fairly accurate, but the majority reported their age only in completed years. This deficiency is likely to underestimate the true relation between menarche and menopause if it exists. As shown in table 2, mean age at menarche for these women was 12.4 years.

Number of births
This covariate was defined as total number of live- and stillbirths prior to age 40 years.

Months of breastfeeding
We defined this covariate as total months of breastfeeding for all births. As shown in table 2, breastfeeding was not widely practiced. Less than a quarter of the women breastfed at all; for those who did, average cumulative time, over all births, was less than 10 months.

Use of exogenous hormones prior to age 40 years
Although data on cycles occurring during exogenous hormone use were excluded, these substances may affect the timing of menopause later in life. Therefore, a dichotomous variable indicating whether the subject used exogenous hormones prior to age 40 years was included in the model. (We also tested variables representing hormone use within each 5-year age segment. None of these variables was significant, and we therefore simplified the model by using one dichotomous variable.)

Use of exogenous hormones after age 40 years
We incorporated a time-varying covariate (3-year intervals) that indicated whether the woman reported use of hormones after age 40 years.

Break in history after age 40 years
We used a time-varying covariate to indicate whether the woman’s cycle history included intervals during which she did not report information.

Birth after age 40 years
We included a dichotomous variable to indicate whether the subject reported a birth after age 40 years (37 women did so).

Although the Cox survival models allowed us to examine the data prospectively, we also examined the data retrospectively to explore aggregate characteristics prior to menopause in the subset of women for whom menopause was observed. For these analyses, we aligned cycle information as of the date of menopause (time 0) and followed cycle patterns back over the years preceding menopause. We examined the mean, standard deviation, and serial irregularity (ApEn).


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The overall hazard of menopause (without covariates) when Kaplan-Meier estimates were used is shown in figure 4. Consistent with earlier work on these data (2), median age at menopause was 50.5 years; 75 percent of women reached menopause by age 52.4 years and 95 percent by age 54.7 years.



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FIGURE 4. Kaplan-Meier estimate of age at menopause. Data from the Menstruation and Reproductive History Study (The Tremin Trust), United States, 1930–1977.

 
Results of the hazard modeling are shown in table 3. Model 1 included the cycle patterns and other covariates prior to age 40 years for each woman. Age at menarche and number of births were inversely related to the hazard; correspondingly, later age at menarche and a greater number of births were associated with later age at menopause. We found no statistically discernible relation between age at menopause and use of hormones prior to age 40 years or number of months of breastfeeding. Cycle characteristics prior to age 40 years were not, in general, significantly related to age at menopause, although there was a marginally significant (p = 0.059) relation between high variability in cycle length at ages 35–39 years and earlier onset of menopause.


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TABLE 3. Cox regression modeling of age at menopause on controls and cycle characteristics{dagger} for data from the Menstruation and Reproductive History Study (The Tremin Trust), United States, 1930–1977
 
Model 2 introduced time-varying covariates for the mean, standard deviation, and ApEn of cycle length after age 40 years. Relative to model 2, the improvement in fit of the model attributable to adding the cycle characteristics after age 40 years was statistically significant (p < 0.001). After age 40 years, cycle characteristics were related to onset of menopause: higher standard deviation was significantly associated (p = 0.05) with increased hazard (i.e., earlier onset). ApEn was significantly and strongly, but inversely, associated (p < 0.001) with the hazard. When the mean and standard deviation were held constant, we found that the higher the ApEn (the greater the irregularity), the lower the hazard. Lower ApEn after age 40 years—greater regularity—was strongly predictive of the onset of menopause. Overall, as cycles became longer, more variable, and yet more regular (with lower ApEn), the hazard of menopause increased. A break in history after age 40 years was associated with a significantly lower hazard of menopause; in our data, there was no discernible relation between a birth after age 40 years and the hazard.

Insight into these results was most easily gained from the "retrospective" analysis, shown in figure 5. Cycle characteristics were aligned as of the onset of menopause, with time running "backward" from 0 (age at menopause). The familiar aggregate pattern of increases in both mean and standard deviation during the menopausal transition can be seen (20). ApEn was equally striking: in the aggregate, ApEn rose slightly (irregularity increased) until about 8 years prior to menopause, when it declined dramatically. In other words, regularity increased sharply in the years preceding menopause.



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FIGURE 5. Descriptive statistics and approximate entropy measured from the time prior to the last menses. Data from the Menstruation and Reproductive History Study (The Tremin Trust), United States, 1930—1977.

 

    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Whelan et al. (2), who used these same data to explore factors associated with age at menopause, found that short cycle length between ages 20 and 35 years was associated with earlier onset of menopause, even while controlling for variability, but that the effect of high variability, which was associated with later onset, did not persist in the presence of controls for cycle length. Our study, in contrast, focused on the joint effects of cycle characteristics while simultaneously adjusting for age at menarche, months of breastfeeding, and use of exogenous hormones. Considered jointly, no statistically discernible effect of cycle characteristics (mean length, variability, and irregularity) between ages 15 and 39 years was found on age at menopause. This result is in sharp contrast to our finding that after age 40 years, both higher variability and greater regularity were significant predictors of the onset of menopause. Our findings with respect to age at menarche and number of births are consistent with those of other researchers (4).

We speculate that the underlying causal mechanism relating both breastfeeding and pregnancy to age at menopause lies in the relation between follicular atresia and estrogen. Estrogen has been found to be important in inhibiting follicular atresia (3235), and it plays a significant role in stimulating follicle growth and maturation: Atretic follicles show a decreased estrogen-to-androgen ratio, evidence for the apoptosis-inhibiting and -inducing effect of the respective hormones (36). Changes in steroid levels most likely play a role in initiation of apoptosis. When estrogen is withdrawn completely, apoptotic DNA fragmentation in the granulose cells of the ovary increases. Estrogens inhibit apoptosis in the ovary, while androgens and gonadotropin-releasing hormone agonists promote apoptosis (37). Atretic follicles show decreased estrogen production, an important factor in follicle survival (38). During pregnancy, estrogen levels are high, an environment that inhibits follicular atresia. During lactation, when estrogen levels remain low, apoptotic cell loss is promoted. Thus, pregnancy delays onset of menopause, while lactation is associated with earlier onset.

Because we expected that secular changes in nutrition, particularly in use of fats, might affect age at menopause, we tested for, but did not find, an effect associated with birth year. However, all women in the study were born within a fairly short time. Data currently being collected and entered from women who have been participating in The Tremin Trust study since the 1960s will enable us to examine whether there has been such a change over time. We also found that women who had a break in their reports after age 40 years (although they subsequently resumed data recording) were likely to have a later age at menopause compared with women who provided uninterrupted data. This result may be an artifact: the later the age at menopause, the longer the exposure to the risk of interruption.

A parallel can be drawn between the findings of this study and changes in patterns of hormonal secretion as women approach menopause. Matt et al. (31) compared healthy late premenopausal women (aged 39–49 years) with younger women (aged 26–29 years) whose menstrual cycles and body weights were normal; ApEn scores for patterns of luteinizing hormone release for late premenopausal women were lower (more regular) than for younger women (corroborated by analyses from "pulse identification" algorithms). The decrease in ApEn was followed by a pronounced increase in ApEn scores for older postmenopausal women (30). These researchers hypothesized that the decrease and nonmonotonicity in ApEn luteinizing hormone evolution that precede menopause may reflect system instability prior to dramatic state change. Our findings suggest that the decline in ApEn characterizing cycle lengths may be an indirect marker of corresponding underlying hormonal changes.

This study shows that, while cycle length and variability increase with the approach of menopause, regularity also increases and is a strong predictor of its onset—an important piece of information not attainable with conventional statistical summaries of menstrual histories. We are currently completing a study of the women recruited for the second cohort, for whom we have histories of cycle characteristics and daily morning urine specimens that are being assayed for the urinary conjugates of both estradiol and progesterone, which will enable us to examine the extent to which these characteristics reflect hormonal changes.


    ACKNOWLEDGMENTS
 
This work was supported by the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development, under grant R01-HD34159, "Biodemographic Models of Reproductive Aging," and the Graduate School of Arts and Sciences, Georgetown University.

The authors gratefully acknowledge the contributions of Reem Hassan.


    NOTES
 
Reprint requests to Dr. Maxine Weinstein, Center for Population and Health, Room 312 Healy Hall, Box 571197, Georgetown University, Washington, DC 20057-1197 (e-mail: WeinstMa{at}Georgetown.edu). Back


    REFERENCES
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. van der Schouw YT, van der Graaf Y, Steyerberg EW, et al. Age at menopause as a risk factor for cardiovascular mortality. Lancet 1996;347:714–18.[ISI][Medline]
  2. Whelan EA, Sandler DP, McConnaughey DR, et al. Menstrual and reproductive characteristics and age at natural menopause. Am J Epidemiol 1990;131:625–32.[Abstract]
  3. Lindquist O, Bengtsson C, Hansson T, et al. Age at menopause and its relation to osteoporosis. Maturitas 1979;1:175–81.[ISI][Medline]
  4. Cooper GS, Baird DD, Darden FR. Measures of menopausal status in relation to demographic, reproductive, and behavioral characteristics in a population-based study of women aged 35–49 years. Am J Epidemiol 2001;153:1159–65.[Abstract/Free Full Text]
  5. Snowden DA, Kane RL, Beeson WL, et al. Is early natural menopause a biologic marker of health and aging? Am J Public Health 1989;79:709–14.[Abstract]
  6. Cooper GS, Sandler DP. Age at natural menopause and mortality. Ann Epidemiol 1998;8:229–35.[CrossRef][ISI][Medline]
  7. Trichopoulos D, MacMahon B, Cole P. Menopause and breast cancer risk. J Natl Cancer Inst 1972;48:605–13.[ISI][Medline]
  8. Kelsey JL, LiVolsi VA, Holford TR, et al. A case-control study of cancer of the endometrium. Am J Epidemiol 1982;116:333–42.[Abstract]
  9. Cheung AP. Ultrasound and menstrual history in predicting endometrial hyperplasia in polycstic ovary syndrome. Obstet Gynecol 2001;98:325–31.[Abstract/Free Full Text]
  10. Seltzer VL, Benjamin F, Deutsch S. Perimenopausal bleeding patterns and pathologic findings. J Am Med Womens Assoc 1990;45:132–4.[Medline]
  11. Cooper GS, Sandler DP. Long-term effects of reproductive-age menstrual cycle patterns on peri- and postmenopausal fracture risk. Am J Epidemiol 1997;145:804–9.[Abstract]
  12. Solomon CG, Hu FB, Dunaif A, et al. Long or highly irregular menstrual cycles as a marker for risk of type 2 diabetes mellitus. JAMA 2001;286:2421–6.[Abstract/Free Full Text]
  13. Kjaer K, Hagen C, Sando SH, et al. Epidemiology of menarche and menstrual disturbances in an unselected group of women with insulin-dependent diabetes mellitus compared to controls. J Clin Endocrinol Metab 1992;75:524–9.[Abstract]
  14. Griffin ML, South SA, Yankov VI, et al. Insulin-dependent diabetes mellitus and menstrual dysfunction. Ann Med 1994;26:331–40.[ISI][Medline]
  15. Cooper GS, Ephross SA, Sandler DP. Menstrual patterns and risk of adult-onset diabetes mellitus. J Clin Epidemiol 2000;53:1170–3.[CrossRef][ISI][Medline]
  16. Harlow BL, Signorello LB. Factors associated with early menopause. Maturitas 2000;35:3–9.[CrossRef][ISI][Medline]
  17. Cramer DW, Xu H. Predicting age at menopause. Maturitas 1996;23:319–26.[CrossRef][ISI][Medline]
  18. den Tonkelaar I, te Velde ER, Looman CW. Menstrual cycle length preceding menopause in relation to age at menopause. Maturitas 1998;29:115–23.[CrossRef][ISI][Medline]
  19. Johannes CB, Crawford SL. Menstrual bleeding, hormones, and the menopausal transition. Semin Reprod Endocrinol 1999;17:299–309.[ISI][Medline]
  20. Treloar AE, Boynton RE, Behn GB, et al. Variation of the human menstrual cycle through reproductive life. Int J Fertil 1967;12:77–126.[ISI][Medline]
  21. StataCorp. Stata statistical software, release 7.0. College Station, TX: Stata Corporation, 2001.
  22. Pincus SM. Approximate entropy as a measure of system complexity. Proc Natl Acad Sci U S A 1991;88:2297–301.[Abstract]
  23. Pincus SM. Irregularity and asynchrony in biologic network signals. In: Johnson ML, Brand L, eds. Methods in enzymology: numerical computer methods. Part c. San Diego, CA: Academic Press, 2000;321:149–82.
  24. Pincus SM, Goldberger AL. Physiological time-series analysis: what does regularity quantify? Am J Physiol 1994;266:H1643–56.[ISI][Medline]
  25. Pincus SM, Huang WM. Approximate entropy: statistical properties and applications. Commun Statist Theory Meth 1992;21:3061–77.[ISI]
  26. Pincus S, Singer BH. Randomness and degrees of irregularity. Proc Natl Acad Sci U S A 1996;93:2083–8.[Abstract/Free Full Text]
  27. Pincus SM, Cummins TR, Haddad GG. Heart rate control in normal and aborted SIDS infants. Am J Physiol 1993;264:8638–46.
  28. Pincus SM, Mulligan T, Iranmanesh A, et al. Older males secrete luteinizing hormone (LH) and testosterone more irregularly, and jointly more asynchronously, than younger males. Proc Natl Acad Sci U S A 1996;93:14100–5.[Abstract/Free Full Text]
  29. Pincus SM, Gevers E, Robinson IC, et al. Females secrete growth hormone with more process irregularity than males in both human and rat. Am J Physiol 1996;270:E107–E115.[ISI][Medline]
  30. Pincus SM, Veldhuis JD, Mulligan T, et al. Effects of age on the irregularity of LH and FSH serum concentrations in women and men. Am J Physiol 1997;271:E989–E995.
  31. Matt DW, Kauma SW, Pincus SM, et al. Characteristics of LH secretion in younger versus older premenopausal women. Am J Obstet Gynecol 1998;178:504–10.[ISI][Medline]
  32. Andreu-Vieyra CV, Habibi HR. Factors controlling ovarian apoptosis. Can J Physiol Pharmacol 2000;78:1003–12.[CrossRef][ISI][Medline]
  33. Kiess W, Gallaher B. Hormonal control of programmed cell death/apoptosis. Eur J Endocrinol 1998;138:482–91.[ISI][Medline]
  34. Medh RD, Thompson EB. Hormonal regulation of physiological cell turnover and apoptosis. Cell Tissue Res 2000;301:101–24.[CrossRef][ISI][Medline]
  35. Amsterdam A, Dantes A, Hosokawa K, et al. Steroid regulation during apoptosis of ovarian follicular cells. Steroids 1998;63:314–18.[CrossRef][ISI][Medline]
  36. Hsueh AJW, Billig H, Tsafriri A. Ovarian follicle atresia: a hormonally controlled apoptotic process. Endocr Rev 1994;15:707–24.[ISI][Medline]
  37. Spencer SJ, Cataldo NA, Jaffe RB. Apoptosis in the human female reproductive tract. Obstet Gynecol Surv 1996;51:314–23.[CrossRef][Medline]
  38. Kaipia A, Hsueh AJW. Regulation of ovarian follicle atresia. Annu Rev Physiol 1997;59:349–63.[CrossRef][ISI][Medline]
  39. Schoenfeld D. Partial residuals for the proportional hazards regression model. Biometrica 1982;69:239–41.