American Journal of Epidemiology Vol. 153, No. 12 : 1159-1165
Copyright © 2001 by The Johns Hopkins University School of Hygiene and Public Health
ORIGINAL CONTRIBUTIONS |
Measures of Menopausal Status in Relation to Demographic, Reproductive, and Behavioral Characteristics in a Population-based Study of Women Aged 35–49 Years
1 Epidemiology Branch, National Institute of Environmental Health Sciences, Durham, NC.
2 Westat, Inc., Research Triangle Park, NC.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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The purpose of this cross-sectional analysis of women aged 35–49 years from the Third National Health and Nutrition Examination Survey, conducted between 1988 and 1994, was to assess associations with menopausal status based either on menstrual cycle patterns or on elevated (>20 IU/liter) follicle-stimulating hormone. Menstrual cycle-based menopausal status was defined for women who had not had surgical menopause by months since the last period (<2, 2–12, and >12 months for pre-, peri-, and postmenopause, respectively). Logistic regression was adjusted for age, smoking, and unilateral oophorectomy. Higher body mass index (
30 kg/m2 compared with < 25.0 kg/m2) was associated with a lower likelihood of elevated follicle-stimulating hormone (odds ratio (OR)=0.6, 95% confidence interval (CI): 0.4, 0.9) but this association was not seen with the menstrual measure of menopause. Exercise (three or more times per week) was associated with a lower likelihood of being postmenopausal on the basis of menstrual (OR = 0.3, 95% CI: 0.2, 0.7) and hormonal (OR = 0.6, 95% CI: 0.4, 1.0) measures. Alcohol use also tended to be associated with postmenopausal status by either measure, but not significantly so. There was little evidence of associations with ethnicity, education, age at menarche, number of livebirths, and oral contraceptive use. Menstrual-based definitions of menopause can be misclassified for women with menstrual irregularity. This might explain why obese women were classified menstrually as menopausal while remaining hormonally premenopausal.
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
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Early natural menopause has been associated with an increased risk of all-cause (1
, 2) and cardiovascular (3) mortality. Age at natural menopause has also been proposed as a marker of ovarian toxicity for factors that may directly or indirectly damage the follicular pool (4). Approximately 5–10 percent of women in the United States experience natural menopause by age 45 years, and the median age at menopause is approximately 51 years (5). Numerous studies (6–13), including a 1990 meta-analysis (6), reported an earlier age at natural menopause among current smokers compared with women who had never smoked. An early age at menopause in women with lower levels of education has been observed in univariate analyses in many studies, but this association was not seen when adjustment was made for smoking and other factors (8–11, 13, 14). Increasing parity was associated with a later age at menopause in most studies that examined this factor (11, 13–17). Relatively few studies have examined other factors (e.g., alcohol use, age at menarche) in relation to age at natural menopause, and there are inconsistent results among the studies that are currently available.
Most studies that examine risk factors for menopause have used self-reported menstrual characteristics to define menopause. A few (18–20) 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.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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The Third National Health and Nutrition Examination Survey (NHANES III) was a national, cross-sectional, population-based study of 33,994 noninstitutionalized, civilian persons aged 2 months to 74 years conducted between 1988 and 1994 by the National Center for Health Statistics. NHANES III was designed to evaluate the nutritional and health statuses of the United States population and used a stratified, multistage sampling procedure to select the study sample. Data collection included a standardized in-person interview, blood sample, physical examination, and other clinical and laboratory procedures (23
). We selected NHANES III women aged 35–49 (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.
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At the time of the blood draw, women were asked, "About how long ago was your last period," with response categories: having it now, less than 2, 2–3, 4–6, 7–9, and 10–12 months ago. Women who had not had a period in the previous 12 months were asked about their age at their last period. FSH concentrations in postmenopausal women far exceed those found at any time during the premenopausal menstrual cycle, but perimenopausal FSH can be similar in concentration to increases seen at time of ovulation for premenopausal women. Since the blood samples were not timed to a specific day of the cycle, some samples were taken around the time of ovulation and could not be used to assess menopausal status reliably. The rise in luteinizing hormone is greater than that in FSH around the time of ovulation, with luteinizing hormone-to-FSH ratios of 1.5–2.0 or higher (24
). In contrast, FSH tends to rise before luteinizing hormone during the premenopausal years, resulting in a luteinizing hormone-to-FSH ratio of less than 1.0 (21). On the basis of this information, we excluded 65 women with a ratio of luteinizing hormone to FSH higher than 2.0 to reduce the potential influence of increases in FSH that occur around ovulation.
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 35–49 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 (2–12 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.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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FSH was strongly correlated with the menstrually defined measure of menopause (figure 1). The proportions with FSH of at least 20 IU/liter were 6, 50, and 85 percent for the premenopausal, perimenopausal, and postmenopausal groups, respectively.
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Higher body mass index was associated with lower FSH levels (adjusted OR = 0.6 for
30 kg/m2 compared with <25.0 kg/m2) (table 2). However, the direction of the associations with the measures based on menstrual cycle patterns were reversed. Higher body mass was associated with an increased risk of being peri- or postmenopausal, although the confidence intervals around these estimates were wide and included 1.0. Results were similar when stratified by smoking status, so there was no evidence of effect modification.
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Exercise was associated with a lower likelihood of having an elevated FSH level or of being postmenopausal as defined by menstrual cycle patterns (table 2). Similar, but somewhat weaker, associations were seen with alcohol use. Little association was seen with perimenopausal status with either exposure. Binge drinking (nine or more drinks per day at least once during the previous year) was reported by only 5 percent of the study participants. The patterns of association with binge drinking (OR for being postmenopausal = 0.4, 95 percent CI: 0.1, 1.9; OR for elevated FSH = 0.8, 95 percent CI: 0.3, 1.7) were similar to the associations with the alcohol measure based on drinks per week, but, given the low frequency of this exposure, these are imprecise estimates.
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.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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The purpose of this study was to identify factors that can influence the process of ovarian senescence, marked by missed menses and elevations in FSH. Ovarian senescence is caused by the final demise of the prenatally determined pool of follicles. Follicular atresia by apoptosis occurs throughout a woman's life, and variation in age of menopause is thought to reflect variation in rates of atresia. Little is known about the biologic factors controlling atresia.
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 (2–12 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 35–39 years) were somewhat more likely to exercise three or more times per week than were those aged 40–44 or 45–49 years, so we repeated the exercise analyses, excluding women aged 35–39. 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 45–49 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 (13–15, 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.
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ACKNOWLEDGMENTS |
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Supported by the Intramural Research Program of the National Institute of Environmental Health Sciences.
Drs. Allen Wilcox and Amy Sayle reviewed an earlier draft of the manuscript.
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NOTES |
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Reprint requests to Dr. Glinda S. Cooper, Epidemiology Branch A3-05, National Institute of Environmental Health Sciences, Durham, NC 27709 (e-mail: cooper1{at}niehs.nih.gov).
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REFERENCES |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
|---|
-
Snowdon 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/Free Full Text] - Cooper GS, Sandler DP. Age at natural menopause and mortality. Ann Epidemiol 1998;8:229–35.[Web of Science][Medline]
- 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.[Web of Science][Medline]
- Mattison DR. Clinical manifestations of ovarian toxicity. In: Dixon RL, ed. Reproductive toxicology. New York, NY: Raven Press, 1985:109–30.
- Cramer DW, Xu H. Predicting age at menopause. Maturitas 1996;23:319–26.[Web of Science][Medline]
- Midgette AS, Baron JA. Cigarette smoking and the risk of natural menopause. Epidemiology 1990;1:474–80.[Medline]
-
Willett W, Stampfer MJ, Bain C, et al. Cigarette smoking, relative weight, and menopause. Am J Epidemiol 1983;117:651–8.
[Abstract/Free Full Text] - McKinlay SM, Bifano NL, McKinlay JB. Smoking and age at menopause in women. Ann Intern Med 1985;103:350–6.
- Brambilla DJ, McKinlay SM. A prospective study of factors affecting age at menopause. J Clin Epidemiol 1989;42:1031–9.[Web of Science][Medline]
-
Luoto R, Kaprio J, Uutela A. Ages at natural menopause and sociodemographic status in Finland. Am J Epidemiol 1994;139:64–76.
[Abstract/Free Full Text] -
Bromberger JT, Mathews KA, Kuller LH, et al. Prospective study of the determinants of age at menopause. Am J Epidemiol 1997;145:124–33.
[Abstract/Free Full Text] - Cooper GS, Sandler DP, Bohlig M. Active and passive smoking and age at natural menopause. Epidemiology 1999;10:771–3.[Web of Science][Medline]
- Parazzini F, Negri E, La Vecchia C. Reproductive and general lifestyle determinants of age at menopause. Maturitas 1992;15:141–9.[Web of Science][Medline]
- Stanford JL, Hartge P, Brinton LA, et al. Factors influencing the age at natural menopause. J Chronic Dis 1987;40:995–1002.[Web of Science][Medline]
-
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/Free Full Text] - Torgerson DJ, Avenell A, Russell IT, et al. Factors associated with onset of menopause in women aged 45–49. Maturitas 1994;19:83–92.[Web of Science][Medline]
- Cramer DW, Xu H, Harlow B. Does "incessant" ovulation increase risk for early menopause? Am J Obstet Gynecol 1995;172:568–73.[Web of Science][Medline]
- Cramer DW, Barbieri RL, Xu H, et al. Determinants of basal follicle-stimulating hormone levels in premenopausal women. J Clin Endocrinol Metab 1994;79:1105–9.[Abstract]
- Cooper GS, Baird DD, Hulka BS, et al. Follicle-stimulating hormone concentrations in relation to active and passive smoking. Obstet Gynecol 1995;85:407–11.[Web of Science][Medline]
- Cooper GS, Thorp JM. FSH Levels in relation to hysterectomy and unilateral oophorectomy. Obstet Gynecol 1999;94:969–72.[Web of Science][Medline]
- Lenton EA, Sexton L, Lee S, et al. Progressive changes in LH and FSH and LH:FSH ratio in women throughout reproductive life. Maturitas 1988;10:35–43.[Web of Science][Medline]
- Metcalf MG, Donald RA, Livesey JH. Pituitary-ovarian function before, during and after the menopause: a longitudinal study. Clin Endocrinol (Oxf) 1982;17:489–94.[Medline]
- National Center for Health Statistics. 1995 Plan and operation of the Third National Health and Nutrition Examination Survey, 1988–1994. Hyattsville, MD: National Center for Health Statistics, 1994. (Vital and health statistics, series 1: programs and procedures, no. 32) (DHHS publication no. (PHS) 94–1308) (GPO no. 017–022–01260–0).
- Djahanbakhch O, McNeilly AS, Warner PM, et al. Changes in plasma levels of prolactin, in relation to those of FSH, oestradiol, androstenedione and progesterone around the preovulatory surge of LH in women. Clin Endocrinol 1984;20:463–72.[Medline]
- Nordin BEC, Crilly RG, Marshall D, et al. Oestrogens, the menopause and the adrenopause. J Endocrinol 1981;89:131–43.
- Flegal KM, Carroll MD, Kuczmarski RJ, et al. Overweight and obesity in the United states: prevalence and trends, 1960–1994. Int J Obes 1998;22:39–47.[Web of Science][Medline]
- Gunter EW, Lewis BG, Koncikowski SM. Laboratory procedures used for the Third National Health and Nutrition Examination Survey (NHANES III), 1988–1994. Atlanta, GA: Centers for Disease Control and Prevention, 1996.
- MacMahon B, Worcester J. Age at menopause—United States–1960–1962. Vital Health Stat 1 1966;11:1–20.
- Kato I, Toniolo P, Koenig KL, et al. Epidemiologic correlates with menstrual cycle length in middle aged women. Eur J Epidemiol 1999;15:809–14.[Web of Science][Medline]
- Cooper GS, Sandler DP, Whelan EA, et al. Association of physical and behavioral characteristics with menstrual cycle patterns in women age 29 to 31. Epidemiology 1996;7:624–8.[Web of Science][Medline]
-
Potischman N, Swanson CA, Siiteri P, et al. Reversal of relation between body mass and endogenous estrogen concentrations with menopausal status. J Natl Cancer Inst 1996;88:756–8.
[Free Full Text] -
Hardy R, Kuh D. Reproductive characteristics and the age at inception of the perimenopause in a British national cohort. Am J Epidemiol 1999;149:612–20.
[Abstract/Free Full Text] - Menon RK, Okonofua FE, Agnew JE, et al. Endocrine and metabolic effects of simple hysterectomy. Int J Gynaecol Obstet 1987;25:459–63.[Medline]
- Feeney DD, Moore DH, Look KY, et al. The fate of the ovaries after radical hysterectomy and ovarian transposition. Gynecol Oncol 1995;56:3–7.[Web of Science][Medline]
- Lindsay MK, Usher DJ. Unrecognized ovarian failure after hysterectomy. Br J Gen Pract 1992;42:529–30.
- Scott RT, Toner JP, Muasher SJ, et al. Follicle-stimulating hormone levels on cycle day 3 are predictive of in vitro fertilization outcome. Fertil Steril 1989;51:651–4.[Web of Science][Medline]
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