1 Center for Family Studies, Department of Psychiatry and Behavioral Sciences, School of Medicine, University of Miami, Miami, FL.
2 Department of Epidemiology and Preventive Medicine, School of Medicine, University of California, Davis, CA.
3 Institute of Toxicology and Environmental Health, University of California, Davis, CA.
4 Department of Statistics, School of Medicine, University of California, Davis, CA.
Received for publication March 27, 2003; accepted for publication February 10, 2004.
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ABSTRACT |
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follicular phase; hormones; life style; linear models, statistical; luteal phase; menstrual cycle; urine
Abbreviations: Abbreviations: AOR, adjusted odds ratio; BMI, body mass index; CI, confidence interval; FSH, follicle-stimulating hormone.
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INTRODUCTION |
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However, self-reported bleeding patterns cannot distinguish ovulatory and anovulatory cycles or timing of ovulation in ovulatory cycles. Ovulatory and anovulatory menstrual intervals (3) and different phases of ovulatory cycles (6) may relate to risk factors and long-term disease risk differently. Thus, assays of prospectively collected daily urine samples for metabolites of estrogen and progesterone have been used in epidemiologic studies to assess ovulatory status and timing of ovulation. In addition, if an undetected pregnancy and loss occurs, menstrual cycle length may be misclassified if self-reported information is used alone (7). Therefore, metabolites of daily urinary human chorionic gonadotropin must also be assayed to detect early pregnancy loss (8, 9).
Previous epidemiologic studies have examined the effects of psychological stress in the workplace (10), caffeine consumption (11), smoking (12), and occupation (13) on menstrual function by using daily urinary hormone metabolites. In addition, various statistical models have been used to explore the relations among characteristics of menstrual cycles and phases (1416). However, these models have been fitted by using data lacking endocrinologic measures of ovarian function. Murphy et al. (17) examined the effects of time-varying covariates on menstrual function, incorporating characteristics of the previous cycle, by using hormone measures assayed in daily saliva samples collected for a fixed time period for all participants.
The present study examined the effects and potential interactions of lifestyle and demographic factors on menstrual cycle characteristics. We assessed prospectively collected daily diary information and assays of daily urine samples for metabolites of reproductive hormones in a non-clinic-based, free-living sample of working women expected to participate in the study for a fixed number of menstrual cycles rather than for a fixed time period.
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MATERIALS AND METHODS |
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Definition of menstrual outcomes
Each urine sample was assayed for metabolites of estrogen (estrone conjugates) and progesterone (pregnanediol-3-glucuronide) by enzyme immunoassays (19) and was adjusted for urinary dilution by dividing by creatinine concentration. Menstrual segments were identified by a recording in the daily diary of 2 or more consecutive days of bleeding or spotting (with >7 days between bleeding episodes). Determinations of ovulatory status and day of ovulation were based on algorithms by Waller et al. (20). Menstrual cycle or segment length was calculated from the first day of menses through the day before onset of the next menses. Follicular phase length was computed as the first day of menses through the day of ovulation. Luteal phase length was computed by subtracting follicular phase length from the corresponding cycle length.
Daily steroid levels were also examined by our study endocrinologist (B. L.) for computer-determined ovulatory cycles with long (>16 days) or short (<5 days) luteal phases or long (>28 days) follicular phases or cycles without a determined day of ovulation. As a result of this review, of those cycles with short or long luteal or follicular phases, 17 cycles were reclassified as anovulatory cycles, and 25 cycles appeared to be two consecutive cycles for which information was missing on menstrual bleeding for the beginning of the second cycle. Twelve cycles without a determined day of ovulation were also reclassified as anovulatory. These 54 cycles, combined with 142 computer-determined anovulatory cycles, were excluded from the present analyses of ovulatory cycles. Thus, 29 women were excluded because they did not contribute information on at least one ovulatory menstrual cycle. For the remaining 309 women (943 ovulatory cycles), day of ovulation for 36 cycles with short or long luteal or follicular phases was manually redetermined based on the hormone plots (by B. L.) to better fit the steroid patterns. The endocrinologist also assigned a day of ovulation to each of the 15 ovulatory cycles for which the computer algorithm could not determine that information because the estrogen/progesterone ratio peaks did not meet the Waller et al. (20) criteria. All such individual determinations were made without knowledge of the womans risk factor characteristics.
In addition to examining cycle characteristics as continuous variables, using definitions in the literature (1012), which identified the fifth and 95th percentiles, the following endpoints were examined as dichotomous variables: short cycle (<25 days), long cycle (>35 days), short luteal phase (<11 days), and long follicular phase (>23 days). We also defined long luteal phase (>15 days) and short follicular phase (<13 days) based on the 10th and 90th percentiles of their distributions in our data.
Statistical analyses
The linear mixed model (21) for continuous measures and the marginal logistic regression model for dichotomous outcomes, used in conjunction with generalized estimating equations (22, 23) to account for the intrawoman correlation of outcomes, are appropriate statistical tools to analyze menstrual cycle data with multiple cycles for each participant (7, 1012). Using the linear mixed model (21), we examined the effect of risk factors on cycle and phase lengths. We modeled the between-woman and within-woman variances and the intrawoman correlation for continuous outcomes by using the same model. The linear mixed models were implemented by using the SAS MIXED procedure (SAS Institute, Inc., Cary, North Carolina). Using the marginal logistic regression model, we computed adjusted odds ratios and 95 percent confidence intervals to assess the effects of risk factors on binary endpoints. Analyses were performed by using the SAS GENMOD procedure (SAS Institute, Inc.).
The linear mixed model contains fixed effects, random effects, and errors. Models are constructed as a composite of terms selected from each component (figure 1). The linear mixed model was fitted separately for each outcome. To examine the influence of the prior cycle on the subsequent cycle, final models were also fitted first by using all 943 cycles and then by using the subset of 634 cycles for women who contributed more than one cycle. Standard steps for the process of model selection and fitting were as follows: 1) selection of maximum likelihood or restricted maximum likelihood methods, 2) selection of the appropriate covariance structure for the data using the method selected, and 3) selection of covariates based on biologic and statistical considerations using the covariance structure selected.
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Age, ethnicity, body mass index (BMI), education, smoking, alcohol and caffeine consumption, and physical activity were considered potential determinants of outcomes and were analyzed as categorical variables. Model selection for fixed covariate effects was based on the p values in the type III F tests and factors that the literature indicated were potentially important. We implemented a step-forward model selection procedure to choose the variables important in our data, entering them in order of their p values, the smallest first. Significance levels of 0.25 for entering and 0.3 for staying were the criteria for including covariates; significance levels of 0.10 for entering and 0.15 for staying were the criteria for including an interaction. All two-way interactions were examined.
For cycle length, a model including age, ethnicity, alcohol consumption, and smoking was obtained by using the stepwise procedure. For follicular phase length, the model included age, ethnicity, alcohol consumption, smoking, and interactions of smoking and physical activity with age. For luteal phase length, the model included ethnicity and smoking. In the literature, BMI and physical activity were consistently shown to be associated with menstrual function (2, 6, 27). Education and caffeine consumption were neither important in the present data nor consistently demonstrated to be important in the literature. Thus, age, ethnicity, BMI, smoking, alcohol consumption, and physical activity were included in the final models to assess cycle and luteal phase length. The final model assessing follicular phase length included ethnicity, BMI, smoking, alcohol consumption, and physical activity and was fitted separately for women aged 34 years or younger and for women aged 35 years or older.
The continuous measures of menstrual outcomes were assessed without transformation since the residual terms from the models based on nontransformed measures were reasonably normally distributed and not improved by logarithmic or other transformation.
In a separate analysis, we examined the effect of length of the prior luteal phase on subsequent cycle length by adding "length of previous luteal phase" as an additional covariate to the above-described final models. Dependence between repeated observations on luteal phase length is built into the model by repeated conditioning of the current response on the previous response; thus, only the variance component within-woman covariance structure was used when luteal phase length was also assessed as the response variable. Moreover, to assess this effect, we used data on a subset of women who provided more than one cycle (239 women, 634 cycles). The linear mixed models were also fitted by using this subset of data without adding "length of previous luteal phase." The potential impact of excluding participants with only one cycle from the estimation of the effects of risk factors was investigated by comparing results.
Outcomes were also divided into categories based on the corresponding cutoffs for the endpoints; for example, cycle length was classified as short (<25 days), normal (2535 days), or long (>35 days). We used generalized estimating equations to analyze two binary logistic regression models instead of running a trinomial logistic model because the multinomial version of the generalized estimating equations method for categorical responses is not supported by SAS software, version 8.2 (SAS Institute, Inc.). Each adjusted odds ratio was computed in comparison with a referent: 2535 days for cycle length, 1323 days for follicular phase, and 1115 days for luteal phase. For comparison with results obtained from the linear mixed model, the same risk factors (i.e., age, ethnicity, BMI, smoking, alcohol consumption, and physical activity) were included in the final marginal logistic regression models.
Both linear mixed models and marginal logistic regression models handle unbalanced data, as in the case of the present data for which women contributed unequal numbers of ovulatory cycles for our analyses. In general, results based on linear mixed model and marginal logistic regression model analyses are valid if outcomes are missing at random or missing completely at random, respectively (28, 29). The strict criterion (3 days of missing data for any 5-day rolling window) used for excluding cycles is likely to make the majority of missingness of data completely at random, that is, independent of menstrual characteristics. Since missing completely at random is a special case of missing at random, estimates based on both the linear mixed model and the marginal logistic regression models were valid in the present analyses.
The differences among all linear mixed models we fitted were relatively minor, and the form of all linear mixed models was equally complex. Two simple principles were used in our model-building process: 1) model fit, evaluated by using the likelihood ratio test or information-based criteria, for example, the Akaike Information Criterion; and 2) biologic plausibility. Consideration of robustness of results to correlation presumed in the linear mixed model motivated our use of the marginal logistic regression model. The generalized estimating equations approach makes no assumption about the form of potential correlation among responses, although it is designed to accommodate correlation regardless of its form.
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RESULTS |
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Mean cycle length did not vary significantly by BMI, smoking, or physical activity in all three models. Compared with women aged 34 years or younger, women aged 35 years or older had a significantly decreased (0.94 days, 95 percent confidence interval (CI): 1.83, 0.05) adjusted mean cycle length (model 1). Asian women had a significantly increased (1.65 days, 95 percent CI: 0.54, 2.76) adjusted mean cycle length compared with Caucasian women because of a significantly increased (2.05 days, 95 percent CI: 0.53, 3.56) follicular phase length for those less than age 35 years (model 1). Women who consumed one or more alcoholic drinks per week had a significantly decreased (1.26 days, 95 percent CI: 2.21, 0.31) adjusted mean cycle length because of a significantly decreased follicular phase length (1.73 days, 95 percent CI: 3.19, 0.28) for those aged 35 years or older (model 1). Mean cycle length was significantly inversely associated with the prior luteal phase length (0.18 days, 95 percent CI: 0.36, 0.00; model 3).
Mean follicular phase length did not differ significantly by BMI in all three models, and age modified the effects of other factors. Compared with women nonsmokers aged 35 years or older not passively exposed to cigarette smoke, current smokers in the same age group had a significantly decreased (2.17 days, 95 percent CI: 3.97, 0.37) mean follicular phase length (model 1). In contrast, follicular phase length was not significantly influenced by smoking for women less than age 35 years. For women aged less than 35 years, physical activity of 4 or more hours per week was associated with a significantly increased (2.26 days, 95 percent CI: 0.29, 4.23) adjusted mean follicular phase length (model 3), but no such association was observed for women aged 35 years or older. In addition, each 1-day increase in the length of the prior luteal phase was associated with a significant decreased adjusted mean follicular phase length in the subsequent menstrual cycles for both age groups (model 3).
Mean luteal phase length was significantly associated with length of the prior luteal phase. Each 1-day increase in the length of the prior luteal phase was associated with an increase of 0.18 days (95 percent CI: 0.10, 0.26) in the subsequent adjusted mean luteal phase length. Mean luteal phase length was not significantly associated with any other factors in all three models.
Dichotomous measures
The likelihood of a menstrual cycle shorter than 25 days, based on the marginal logistic regression model, was not appreciably associated with any of the risk factors examined (table 6). However, women aged 35 years or older were less likely than younger women to have long cycles (>35 days) (adjusted odds ratio (AOR) = 0.53, 95 percent CI: 0.28, 1.01). Compared with Caucasian women, Asian women were significantly more likely to have long cycles (AOR = 2.60, 95 percent CI: 1.20, 5.65) and follicular phases longer than 23 days (AOR = 2.09, 95 percent CI: 0.97, 4.49). Women who consumed one or more alcoholic drinks per week were significantly less likely to have long cycles (AOR = 0.38, 95 percent CI: 0.20, 0.69) or long follicular phases (AOR = 0.39, 95 percent CI: 0.21, 0.72). Physical activity of 4 hours or more per week compared with no activity was significantly negatively associated with short follicular phases (<13 days) (AOR = 0.36, 95 percent CI: 0.18, 0.74). Increasing physical activity was also moderately positively associated with long cycles and long follicular phases. Smoking status and BMI were not significantly associated with any of the dichotomous endpoints.
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DISCUSSION |
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Previous data on ethnic differences in menstrual function are somewhat limited. Other than genetic heterogeneity, such racial differences may also be due to diet and other cultural factors (30). However, the random intercept term in the linear mixed model in the present study might have accounted for these factors if we assume that they did not vary across menstrual cycles.
The present results indicated that current smoking was associated with a significant decrease in mean follicular phase length for women over the age of 35 years. An earlier study of largely Caucasian women (11) reported that smoking was associated with a decreased mean menstrual segment length and an increased risk of a short menstrual segment. This finding is consistent with an effect of smoking on the progression of follicular recruitment and maturation but not on ovulation or luteal function. This effect is likely one of accelerated follicular maturation, possibly due to increased pituitary drive, since rate of follicular development is a direct result of stimulation by follicle-stimulating hormone (FSH) (31). Increased FSH production could result from perturbation of inhibin production, increased metabolism of circulating estrogen, or increased hypothalamic drive through interference with the estrogen negative feedback (31, 32). Thus, we can speculate that exposure to cigarette smoke may affect FSH production by different mechanisms depending on the quality or quantity of the exposure. At high, direct exposures, FSH drive increases and truncates the follicular phase by stimulating ovarian follicles to develop faster (32, 33). The current study did not provide any direct information as to which mechanism was operating, however.
While largely speculative, the concept that cigarette smoke affects FSH is consistent with mechanisms for other related observations. For example, the apparent protective effect of smoking on breast and endometrial cancer (3, 5, 34, 35) could be a likely result of shorter periods of exposure to unopposed estrogens due to truncated follicular phases. In addition, the earlier age at menopause associated with smoking (36) would be consistent with short and more frequent ovulation and an earlier depletion of oocytes.
In the present study, physical activity of 4 or more hours per week was associated with an increased cycle length, which could be due to a dampening of FSH pulses during the luteal-follicular transition, leading to delayed maturation of the next cohort of follicles (26, 31). Increased cycle length is associated with delayed ovulation and increased follicular phase length, since luteal phases are self-limited to 14 days (33).
Cycle length has been negatively associated with age because of shortening of the follicular phase (2, 37), consistent with our results. Harlow et al. (7) demonstrated that overweight is associated with the probability of long cycles in college women, but we found no association of BMI with any outcomes in our older and more ethnically diverse population. In addition, just as we found, alcohol consumption has been reported to be associated with a reduction in long cycles in young women (38) and changes in hormone dynamics (39, 40).
We modeled the between-woman and within-woman variances, as well as the fixed effects for risk factors, simultaneously. For example, when the first-order autoregressive within-woman covariance structure was used, the between-woman standard deviation was 3.45 days and the within-woman standard deviation was 2.84 days for mean cycle length, based on 943 cycles. This finding confirms the expected greater variability in menstrual cycle length between women than within women. The autoregressive parameter for cycle length was 0.22 (p = 0.0006), suggesting that cycle length may vary between consecutive ovulatory menstrual cycles: a pattern of a longer cycle followed by a shorter cycle, and vice versa. The intrawoman correlation for cycle length was 0.51 between any adjacent pair of observations.
The target population in the present study was women who were experiencing menstrual cycles and were ovulating. Our results show that excluding women who contributed data on only one ovulatory cycle did not appreciably change the estimates. Bias was possible because we excluded these 29 women for having anovulatory cycles only; they might have had unobserved ovulatory cycles. However, this small subset of the study population was unlikely to bias the results greatly. There is no issue of imputation in our paper, because 1) excluding cycles for which data were incomplete was unlikely to cause bias if it did not result in excluding women, and 2) the missingness mechanism would have to be greatly simplified so that any imputation of menstrual cycles was doable. It is entirely possible that this mechanism depends on many factors, including the dropout mechanism and correlation pattern within women. Therefore, reanalysis with only simple imputation that does not take such factors into account may result in false security.
One of our strongest findings was that mean cycle (or fol-licular phase) length for ovulatory cycles was highly predicted by length of the prior luteal phase. This noteworthy finding needs to be considered in future studies evaluating menstrual cycle and hormonal patterns, since it suggests that cycle length and dynamics might well be correlated with prior luteal phase length.
In conclusion, we examined the association of demographic and lifestyle factors with menstrual cycle characteristics in a large sample of women aged 2044 years by using both continuous and categorical outcomes and multivariable, repeated-measures statistical techniques. We found our results to be both internally consistent and largely consistent with prior studies that did not include data based on daily urinary hormone metabolites to assess ovarian function. These results suggest that nonmodifiable host factors, such as ethnicity, and potentially modifiable risk factors, such as smoking, physical activity, and alcohol consumption, may affect menstrual cycle outcomes. Therefore, both genetic and environmental factors may influence these characteristics, which in turn are related to long-term disease risk.
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ACKNOWLEDGMENTS |
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The authors thank Dr. K. O. Waller for sharing her SAS code to assess ovulatory status and day of ovulation.
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NOTES |
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REFERENCES |
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