1 Epidemiology Branch, National Institute of Environmental Health Sciences, Durham, NC.
2 Westat, Inc., Research Triangle Park, NC.
![]() |
ABSTRACT |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
alcohol drinking; menarche; menopause; obesity; parity
Abbreviations: CI, confidence interval; FSH, follicle-stimulating hormone; NHANES, National Health and Nutrition Examination Survey; NHANES III, Third National Health and Nutrition Examination Survey; OR, odds ratio
![]() |
INTRODUCTION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Most studies that examine risk factors for menopause have used self-reported menstrual characteristics to define menopause. A few (1820
) have used the hormonal marker follicle-stimulating hormone (FSH). FSH concentrations may increase for up to several years before menstrual cycles cease (21
,22
).
We examined the associations between lifestyle, demographic, and reproductive factors (specifically, body mass index, exercise, alcohol use, ethnicity, education, age at menarche, pregnancy history, and oral contraceptive use) and menopause in women of late reproductive age from a representative sample of the United States population. The use of both menstrual and hormonal measures of menopause allows us to determine consistencies and inconsistencies between them.
![]() |
MATERIALS AND METHODS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
We selected NHANES III women aged 3549 (n = 2,205) for study. This age restriction was chosen because we were interested in factors that affected early menopause. The reasons for exclusion varied somewhat between the analysis of menstrual patterns status and FSH (table 1), but 227 women were excluded from both analyses because of specific medical indications or medication use (bilateral oophorectomy, current oral contraceptive use, and menopause related to chemotherapy or radiation) that precluded assessment of current menstrual periods and FSH levels. For the analyses of menstrual patterns, 274 women who had had surgical menopause (hysterectomy) or had an uncertain menopausal status were excluded. For the FSH analyses, women who had had a hysterectomy without bilateral oophorectomy were included, but 184 women who were currently pregnant or breastfeeding or who had missing hormone or oophorectomy data were excluded.
|
Use of hormone replacement therapy may present some difficulties in the classification of menopausal status based on either menstrual cycles or FSH because of withdrawal bleeding that can occur with progestins and because of a suppressive effect of estrogen on FSH. However, excluding users of hormone replacement therapy could introduce a selection bias into the study, and including hormone replacement therapy use in the logistic regression models could produce attenuated estimates due to overadjustment of an intermediary variable. We included 19 women who were currently using hormone replacement therapy in the analysis of menopause based on menstrual cycles. Twelve of the women were classified as postmenopausal (>12 months since the last menstrual period), and seven were classified as perimenopausal. We also examined the effect of hormone replacement therapy on FSH by using the NHANES III data. Among 129 women aged 3549 years with a bilateral oophorectomy, 85 percent of the women who were currently using hormone replacement therapy pills had an FSH that exceeded 20 IU/liter compared with 86 percent of nonusers. However, the prevalence of very high (>40 IU/liter) levels was somewhat lower among hormone replacement therapy users (52 percent compared with 67 percent for nonusers; odds ratio (OR) = 0.5, 95 percent confidence interval (CI): 0.2, 1.3, p = 0.13). A cutpoint of 20 IU/liter differentiated between 50 premenopausal and 49 oophorectomized women in a study by Nordin et al. (25). For these reasons, we chose to define elevated FSH as above 20 IU/liter, and we included 59 current users of hormone replacement therapy in the FSH analysis.
After all exclusions, there were 1,555 premenopausal women (1,520 who had had a period in the previous 2 months, 19 currently pregnant, and 16 currently breastfeeding), 66 perimenopausal women (212 months since the last menstrual period), and 75 postmenopausal women (>12 months since the last menstrual period), for a total of 1,696 women in the menstrual-cycle analysis. There were 1,721 women in the FSH analysis (1,481 with FSH 20 IU/liter and 240 with FSH >20 IU/liter).
The interview data collected in NHANES III included information on age, ethnicity, education, smoking history, menopausal status, use of birth control pills, use of hormone replacement therapy, number of pregnancies, and number of livebirths. Information on alcohol use was obtained from the food frequency questionnaire that included three questions about usual consumption over the previous month of beer and light beer, wine, wine coolers, sangria, champagne, and hard liquor. An additional question asked about frequency of binge drinking (nine or more drinks in a day) over the previous 12 months. Categorization of exercise was based on the summation of self-reported frequency of eight specific activities (running/jogging, bicycling, swimming, aerobics, other dancing, calisthenics, gardening, and lifting weights) during the previous month. Height and weight were measured as part of the physical examination and were used to calculate body mass index (kg/m2). We divided body mass index based on cutpoints defining obesity that were used in a previous analysis of National Health and Nutrition Examination Survey (NHANES) data (26).
FSH was measured in NHANES III by using an immunoradiometric assay (FSH MAIAclone, Serono Diagnostics, East Walpole, Massachusetts). The coefficient of variation was 8.9 percent for low controls and 3.1 percent for high controls (27).
Logistic regression, fitting generalized logits to model a three-level, nominal (nonordered) outcome of menopausal status based on menstrual cycles (premenopause, perimenopause, and postnatural menopause), was used for the analysis. Age (as a continuous variable), current smoking (yes/no), and unilateral oophorectomy were included in all models because these were predictors of menopausal status in other studies (6, 17
, 20
, 28
). Other factors (body mass index, exercise, alcohol use, ethnicity, education, age at menarche, parity, and oral contraceptive use) were added to the model one at a time. We then looked for evidence of confounding between these variables by examining the change in associations when combinations of variables that could be correlated (e.g., body mass index and exercise; education and ethnicity; and body mass index, ethnicity, and education) were added to the model. We stratified by smoking status to examine the possible interaction between smoking and body mass index. This interaction was reported in a previous study (7
). Results are presented in terms of odds ratios and 95 percent confidence intervals.
We used logistic regression to analyze the prevalence of elevated FSH (>20 IU/liter). In addition to age, smoking, and unilateral oophorectomy, we adjusted for hysterectomy in this model (20). The analytic strategy was similar to the process described for the analysis of menstrually based menopausal status.
We examined the effect of some of our inclusion and exclusion decisions by comparing results with subsets of the data. For example, we included 35 women who were currently pregnant or breastfeeding in the analysis of menopausal status based on menstrual cycles and classified them as premenopausal. There was little difference in the observed associations when we excluded these women. We also repeated the analyses, limiting both sets to the 1,487 women who had FSH and the menstrually defined measure of menopausal status, to determine whether any differences in the associations that we observed could be attributed to differences in the sample. Our results were similar when using this more limited sample. We present the results from the larger, more representative sample.
![]() |
RESULTS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
|
|
On the basis of menstrually defined menopause, African Americans had a somewhat increased likelihood of being postmenopausal and Hispanics were more likely to be perimenopausal, but there was little association with the hormonally defined menopause measure in either group. There was little evidence of an association between FSH or menstrually defined menopausal status and education level, number of livebirths, or oral contraceptive use. Additional adjustment for body mass index had little effect on the associations with ethnicity or education (data not shown). Younger age at menarche (10 years) was somewhat associated with postmenopausal status and with increased FSH levels, but the estimates were not statistically significant. No association was seen with later age at menarche.
![]() |
DISCUSSION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
The menstrual-based measures of menopause allowed us to identify perimenopause women as a separate group. However, it does not necessarily follow that factors that cause early menopause would also affect perimenopause. Perimenopause, defined by a time of irregular menstrual cycling before final amenorrhea, may or may not occur. It is possible for women to have regular cycles up to the time of their last period, thus skipping a menstrually defined perimenopausal interval. An exposure that extended the perimenopausal interval but did not result in earlier entry into menopause would be a risk factor for perimenopause but might actually reduce the likelihood of menopause. Therefore, we evaluated menstrually defined perimenopause and postmenopause separately rather than as a single ordinal outcome.
We observed an association between higher body mass index and reduced risk of being menopausal (OR = 0.6), as defined by a hormonal measure of elevated FSH. However, the associations with menopausal status based on menstrual cycle patterns were in the opposite direction (that is, odds ratios were greater than 1.0). Similar patterns with body mass index were seen in smokers and nonsmokers. Data from the Nurses' Health Study suggested that heavier women had a reduced risk of becoming menstrually postmenopausal during 2 years of follow-up, but this association was seen only in smokers (7).
Because obesity is associated with menstrual irregularity (29, 30
), obese premenopausal women may be misclassified as peri- or postmenopausal. Consistent with this interpretation, only 37 percent of very obese women (body mass index
30.0 kg/m2) who were classified as perimenopausal based on menstrual cycle patterns (212 months since the last menstrual period) had an FSH of more than 20 IU/liter compared with 63 percent of perimenopausal women with a lower body mass index (OR = 0.4, 95 percent: CI: 0.1, 1.0). Comparable differences were seen with higher FSH values: 27 percent of the very obese perimenopausal women had FSH values of 40 IU/liter or more compared with 53 percent of thinner perimenopausal women (OR = 0.3, 95 percent CI: 0.1, 0.9). Postmenopausal estrogen levels may be higher in obese compared with nonobese women, but still are well below premenopausal levels (31
). Whether such a small change in estrogen could account for the low FSH in obese women who reported peri- and postmenopausal status is doubtful.
There was an association between exercise frequency and reduced risk of both hormonal and menstrual menopause in the NHANES data. This association has not been reported previously. In our study, younger women (those aged 3539 years) were somewhat more likely to exercise three or more times per week than were those aged 4044 or 4549 years, so we repeated the exercise analyses, excluding women aged 3539. This exclusion did not appreciably change the associations we had observed, so we do not think our results are due to residual confounding by age. Because exercise is a modifiable behavior, it is important to examine the relation between it and menopause in additional studies, including prospective designs that would specifically address exercise habits antecedent to menopause.
Our analysis suggested an association between alcohol use and reduced risk of hormonally or menstrually defined menopause, but the relations were not statistically significant. Torgerson et al. (16) reported a reduced risk (OR = 0.5) of menopause with higher alcohol consumption in a population-based study of 2,073 women aged 4549 years. A mechanism for an alcohol effect has not been proposed.
NHANES included a large proportion of African Americans and Hispanics. Although there was some evidence of increased risk of being postmenopausal based on menstrual-cycle patterns in African Americans compared with Whites, this association was not statistically significant, and there were no clear associations in Hispanics. Some other studies have reported an earlier age at menopause in African Americans (11, 14
, 28
), but this difference was statistically significant in only one of the studies (11
). There was no association between education level and either measure of menopausal status in our analysis. This finding is similar to those of other studies that adjusted for smoking (8
11
, 13
, 14
).
There was some evidence of an association between early menarche (age <11 years) and earlier menopause in our study. Data from other studies on this question have been inconsistent (1315
, 17
). We did not observe any pattern with respect to menopausal risk and number of livebirths. Several studies have reported an association between higher parity and later age at menopause (13
17
) or perimenopause (32
), but no association was seen in the prospective study by Brambilla et al. (9
). None of these studies included analyses that adjusted for body mass index. Analyses of menopause in relation to oral contraceptive use (11
, 14
, 17
), like those in our analysis, do not provide evidence for an association.
There are limitations to our study. In analyzing FSH, we could not control for day of cycle, since information about the exact date of the last menstrual period was not collected. However, our exclusion of participants with a ratio of luteinizing hormone to FSH higher than 2.0 should reduce the likelihood of misinterpreting an ovulation-related increase in FSH. There is an arbitrary element to any categorization of a continuous variable, such as we used with the dichotomous outcome of FSH greater than 20 IU/liter. Cutpoints of 20, 25, 40, and 70 IU/liter have been used in clinical and epidemiologic studies of ovarian function and menopause (18, 33
36
). We chose 20 IU/liter because this cutpoint differentiated between 50 premenopausal and 49 oophorectomized women in a study by Nordin et al. (25
) and best allows for inclusion of women using hormone replacement therapy. These are cross-sectional data, so some associations could be due to behavioral changes that might occur with menopause. For example, if women stopped exercising at menopause, that could explain data showing that exercise is protective of menopause.
A strength of our study is that it is a population-based, relatively large sample of the United States population, with a substantial number of African Americans and Hispanics included in the sample. A standardized protocol was used for the interview. Collection of menstrual cycle and FSH data allows for analysis of both measures. These parallel analyses can be very informative, even (or especially) when results are "discrepant," such as are seen with the analyses of body mass index. Our analysis highlights potential problems with the classification of menopausal status based on menstrual cycle patterns in obese women or those with other characteristics or exposures that affect menstrual regularity.
Understanding of the extent to which ovarian senescence is influenced by weight, exercise, alcohol, and other behavioral or environmental factors can stimulate research into the mechanisms involved in follicular atresia as well as identify modifiable exposures that influence menopause.
![]() |
ACKNOWLEDGMENTS |
---|
Drs. Allen Wilcox and Amy Sayle reviewed an earlier draft of the manuscript.
![]() |
NOTES |
---|
![]() |
REFERENCES |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|