American Journal of Epidemiology
Copyright © 1999 by The Johns Hopkins University School of Hygiene and Public Hearth
All rights reserved
Vol. 149, No. 6
Printed In U.S.A.
Caffeine Consumption and Menstrual Function
Laura Fenster,1 Chris Quale,1 Kirsten Waller,1 Gayle C. Windham,1 Eric P. Elkin,1 Neal Benowitz,2 and Shanna
H. Swan1
The relation between caffeine intake and menstrual function was examined in 403 healthy premenopausal
women who belonged to Kaiser Permanente Medical Care Program in 1990-1991. A telephone interview
collected information about caffeinated beverage intake as well as other lifestyle, demographic, occupational,
and environmental factors. Subjects collected daily urine samples and completed a daily diary for an average of
five menstrual cycles. Metabolites of estrogen and progesterone were measured in the urine, each cycle was
characterized as anovulatory or ovulatory, and a probable day of ovulation was selected when appropriate.
Logistic regression and repeated measures analyses were performed on menstrual parameters. Women whose
caffeine consumption was heavy (>300 mg of caffeine per day) had less than a third of the risk for long menses
(£8 days) compared with women who did not consume caffeine (adjusted odds ratio = 0.30, 95% confidence
interval 0.14-0.66). Those whose caffeine consumption was heavy also had a doubled risk for short cycle length
(£24 days) (adjusted odds ratio = 2.00, 95% confidence interval 0.98-4.06); this association was also evident in
those whose caffeine consumption was heavy who did not smoke (adjusted odds ratio = 2.11, 95% confidence
interval 1.03-4.33). Caffeine intake was not strongly related to an increased risk for anovulation, short luteal
phase (£10 days), long follicular phase (£24 days), long cycle (£36 days), or measures of within-woman cycle
variability. Am J Epidemiol 1999; 149:550-7.
caffeine; estrone; follicular phase; luteal phase; menstrual cycle; menstruation disorders; ovulation; pregnanediol
Caffeine is one of the most commonly ingested,
pharmacologically active substances. It is present in coffee, tea, soda, cocoa, solid milk chocolate, and many
medications (1). Caffeine is rapidly absorbed from the
digestive tract and distributes throughout all tissues (2).
The mechanisms of action of caffeine include inhibition
of hydrolysis of cyclic 3',5'-adenosine monophosphate
and 3',5'-guanosine monophosphate (3) and antagonism
of adenosine (4), making it plausible that caffeine might
alter hormonal profiles and thereby affect menstrual
function. Menstrual function, in turn, may be related to
other health outcomes, such as fertility, osteoporosis, and
breast cancer (5).
The results of studies of coffee and caffeinated beverage consumption in relation to fecundity are inconsistent. Several studies in humans have reported an
association between caffeine intake and delayed time
to conception (6-12); in contrast, others have shown
either no association (13, 14) or a relation only at very
high levels of intake (15, 16). Examination of the relation between caffeine intake and menstrual function
may help to elucidate possible biologic mechanisms by
which caffeine might alter fecundability.
The current study describes the relation between
caffeine intake and menstrual function in the Women's
Reproductive Health Study, which was conducted by
the California Department of Health Services in collaboration with the Division of Research of the Kaiser
Permanente Medical Care Program of Northern
California. This study used urinary metabolites of sex
steroids to explore the associations between menstrual
function and several environmental and lifestyle exposures in healthy, premenopausal women.
MATERIALS AND METHODS
Received for publication January 28,1998, and accepted for publication July 9, 1998.
Abbreviations: PdG, pregnanediol-3-glucuronide; SE, standard
error.
1
Reproductive Epidemiology Section, Department of Hearth
Services, Emeryville, CA.
2
Division of Clinical Pharmacology and Experimental
Therapeutics, Department of Medicine, University of California, San
Francisco, CA.
Subject recruitment
The study population, materials, and methods have
been described previously in detail (17). Married
women aged 18—39 years who belonged to Kaiser
Permanente Medical Care Program and who lived in
550
Caffeine Consumption and Menstrual Function
zip code areas located near the study's field office
were screened by telephone for eligibility. Eligibility
criteria were primarily intended to identify women at
some risk of pregnancy because another goal of the
study was to examine fetal loss and time to conception.
Women were not eligible if they did not speak English;
had not had a menstrual period in 6 weeks; were currently pregnant; had been surgically sterilized; were
using oral contraceptives, intrauterine devices, or hormonal medications; had been having unprotected
intercourse for more than 3 months without becoming
pregnant; or were anticipating travel that would separate them from their husbands. Of 6,481 women
screened for eligibility, 1,092 were eligible for recruitment. Of these, 553 agreed to begin the study, 89
dropped out during urine collection, and 61 became
ineligible (e.g., due to beginning oral contraceptives or
separation from their husbands), leaving a total of 403
women who completed the study.
Interviewing
All eligible women were interviewed by telephone
on the following topics: demographics; reproductive
and medical history; lifestyle factors, such as consumption of alcohol, tobacco, and caffeinated beverages, life event stress, and exercise; and occupation,
industry, and job-related exposures. Subjects were
asked to quantify in cups or cans per day their usual
daily consumption of caffeinated coffee, tea, and soda.
Total caffeine consumption was estimated by summing
the usual total per day, assuming a caffeine content of
107 mg/cup of coffee, 34 mg/cup of tea, and 47
mg/can of soda (18). The occurrence of stressful life
events over the 3-month period preceding the interview was assessed using 11 life event items compiled
from two standardized questionnaires (19, 20).
Definition of menstrual endpoints
Each subject collected a sample of first morning
urine daily and recorded vaginal bleeding and other
information in a daily diary. Creatinine and metabolites
of the sex steroid hormones estrone (estrone conjugates) and progesterone (pregnanediol-3-glucuronide
(PdG)) were measured in urine by enzyme-linked
immunoassay (21) and were corrected for urine dilution using the creatinine level. Urinary measures of
estrone conjugates and PdG vary due to individual differences in metabolism, but are reliable enough to
enable assignment of the day of ovulation (22).
We determined whether cycles were ovulatory or
nonovulatory based on the rise in PdG levels (17). In
ovulatory cycles, we estimated the day of ovulation by
using a modification of an algorithm designed by
Am J Epidemiol
Vol. 149, No. 6, 1999
551
Baird et al (23); the modification is described in the
paper by Waller et al. (17). We discovered that the PdG
analyte occasionally precipitated prior to assay resulting in spurious cycle "abnormalities." Since costs prohibited duplicate analyses of all cycles, we reassayed
all nonovulatory cycles and all cycles with an "abnormal" day of ovulation. Abnormal patterns were confirmed in 243 (45.6 percent) of the 533 potentially
abnormal cycles that were reanalyzed. We used the
more "normal" assay for reassayed cycles with discordant results (17).
We examined outcomes at the woman level and the
cycle level. Menstrual cycle-level endpoints were
defined as follows: 1) length of menses = number of
consecutive days a woman reported as bleed days in
her diary; 2) cycle length = number of days from the
date of the start of the cycle to the day before the next
cycle start date; 3) follicular phase length = number
of days from the date of the start of the cycle up to
and including the day of ovulation; 4) luteal phase
length = difference between the cycle length and follicular phase length. We used the fifth and 95th percentiles of cycle length to define short and long
cycles. Similarly, we used the 95th percentile to
define long follicular phase length and long menses
and the fifth percentile to define short luteal phase
length. We restricted analysis of each outcome to
cycles with adequate data (17).
Woman-level endpoints included anovulation and
measures of within-woman cycle variability.
Anovulation was defined as 36 or more days without
ovulation (17). Anovulation was analyzed at the
woman level rather than at the cycle level because a
single anovulatory episode could contain several or no
nonovulatory "cycles" depending on whether or not
the woman had breakthrough bleeding. We calculated
within-woman ranges for length of menses, cycle
length, follicular phase length, and luteal phase length
by subtracting each woman's minimum value from her
maximum value for each of these variables; we
dichotomized these ranges at the 75th percentile to
define women with greater variability of these endpoints.
Statistical analyses
Cycle-level endpoints were analyzed using repeated
measures techniques to account for the correlation of
within-woman measurements (24). We also analyzed
cycle-level endpoints as binary variables (25) as follows: long menses length (>7 vs. <7 days); short cycle
length (<25 vs. 25-35 days); long cycle length (>35
vs. 25-35 days); long follicular phase length (>24 vs.
<24 days); and short luteal phase length (<10 vs. >10
days).
552
Fenster et al.
TABLE 1. Distribution of potential confounders among vromen by caffeine consumption, California Women's Reproductive
Health Study, 1990-1991
Dalty caffeine Intake (mg/day)*
Variable
No.t
(n=109)
Age (years)
<30
30-34
235
%t
No.
(27%)
(n=153)
34
47
28
Age (years)
Mean (SD)§
151-300
1-150
None
(3D
(43)
(26)
%
(37%)
61
61
31
(40)
(40)
(20)
%
(n-90)
(22%)
25
34
31
(28)
(38)
(34)
No.
(n = 5 1)
n
r
%
(13%)
valued
(35)
(33)
(31)
0.19
18
17
16
33 (4.0)
31 (4.3)
32(4.1)
>300
No.
0.04H
32 (3.9)
Education
No college
Some college
College graduate
19
37
53
(17)
(34)
(49)
36
63
54
(24)
(41)
(35)
19
31
40
(21)
(34)
(44)
16
20
15
(31)
(39)
(29)
0.17
Race
White
Asian
Other
77
22
10
(71)
(20)
(9)
104
19
30
(68)
(12)
(20)
66
9
15
(73)
(10)
(17)
39
3
9
(76)
(6)
(18)
0.06
Income/year (dollars)
<$35,000
>$35,000-$55,000
>$55,000-$75,000
>$75,000
16
50
27
15
(15)
(46)
(25)
(14)
27
56
39
27
(18)
(38)
(26)
(18)
12
30
26
21
(13)
(34)
(29)
(24)
5
21
12
9
(11)
(45)
(26)
(19)
0.64
Body mass index (kg/m2)
<19.1
19.1-27.3
>27.3
13
76
20
(12)
(70)
(18)
9
114
30
(6)
(75)
(20)
5
70
15
(6)
(78)
(17)
3
32
16
(6)
(63)
(31)
0.16
Gravidity
None
1-2
23
13
55
41
(12)
(50)
(38)
17
80
56
(11)
(52)
(37)
12
47
31
(13)
(52)
(34)
6
26
19
(12)
(51)
(37)
1.00
15
73
21
(14)
(67)
(19)
35
103
15
(23)
(67)
(10)
22
59
9
(24)
(66)
(10)
12
33
6
(24)
(65)
(12)
0.17
Parity
None
1-2
23
Table continues
We used logistic regression to model risk for the following woman-level endpoints: anovulation; large
range for menses length (>3 days); large range for
cycle length (>7 days); large range for follicular phase
length (>7 days); and large range for luteal phase
length >3 days). We restricted the analyses of withinwoman ranges to women with two or more cycles.
We analyzed caffeine consumption primarily as a
categorical variable (no caffeine and 1-150, 151-300,
and >300 mg/day). Covariates that were related to caffeine consumption in univariate analyses at p < 0.2
were included in initial models as potential confounders. We then used the change-in-estimate method
(26) to examine confounding. We retained all covariates that (upon removal from any model) altered the
effect estimates by more than 10 percent. The following covariates were used in all final models: age, education, race, body mass index, cigarettes smoked per
day, weekly alcohol consumption, and parity. When
modeling risk of anovulation, we controlled for smoking as a yes/no variable rather than by cigarettes per
day because of small numbers. We also ran models that
excluded anovulatory episodes so that we could ensure
that any effect seen with cycle length or variability was
not due to an increase in anovulation.
RESULTS
Table 1 compares the characteristics of women who
consumed different levels of caffeine. Compared with
Am J Epidemiol Vol. 149, No. 6, 1999
Caffeine Consumption and Menstrual Function
553
TABLE 1. Continued
Dally caffeine Intake (mg/day)
None
Variable
No.
(n=109)
History of spontaneous
abortion
None
1
22
Previous elective abortion
None
1
22
MET§ score/week
Mean (SD)
Smoking (cigarettes/day)
0
1-10
>10
1-150
%
(27%)
No.
(n = 90)
(22%)
84
17
8
(77)
(16)
(7)
117
27
9
(76)
(18)
(6)
68
19
3
(76)
(21)
(3)
87
17
5
(80)
(16)
(5)
97
36
20
(63)
(24)
(13)
59
17
14
(66)
(19)
(16)
15.4 (21.7)
19.7 (24.9)
19.3 ( 24.4)
No.
(n = 51)
%
n
H
(13%)
value
38
12
1
(75)
(24)
(2)
0.63
29
16
6
(57)
(31)
(12)
0.02
16.2 (21 •5)
0.44H
7
(93)
(3)
(4)
82
5
3
(91)
(6)
(3)
37
6
8
(73)
(12)
(16)
0.001
(44)
(54)
(2)
43
100
10
(28)
(65)
(7)
17
58
15
(19)
(64)
(17)
7
32
12
(14)
(63)
(24)
0.001
41
47
21
(38)
(43)
(19)
71
67
15
(46)
(44)
(10)
46
30
14
(51)
(33)
(16)
18
24
9
(35)
(47)
(18)
0.14
67
42
(61)
(39)
99
54
(65)
(35)
70
20
(78)
(22)
41
10
(80)
(20)
0.02
(99)
(1)
(0)
142
48
59
2
Life events
None
1-2
;>3
Employed
Yes
No
£4
>300
%
%
(37%)
108
1
0
Alcohol (drinks/week)
0
1-3
151-300
No.
(n> 153)
4
* Conversion to milligrams of caffeine based on 107 mg/cup of coffee, 34 mg/cup of tea, and 47 mg/can of soda (Bunker and McWilliams,
J Am Diet Assoc 1979;74:28-32).
t Numbers for some variables may not total due to missing values. Percentages for some variables may not total 100 because of rounding errors.
t p value is for chi-square test of independence unless noted otherwise.
§ SD, standard deviation; MET, metabolic equivalent.
H p value is for test of Ho: all caffeine categories have the same mean.
nonconsumers, women with heavy caffeine consumption were somewhat older; less likely to have gone to
college; less likely to be Asian; and more likely to have
a higher body mass index, to be nulliparous, to have
had an elective abortion, to have higher levels of alcohol and cigarette consumption, and to be employed.
The number of urine samples collected and the number
of cycles available for analyses did not differ appreciably by level of caffeine intake.
Women with heavy caffeine consumption were
somewhat less likely to have an anovulatory episode
(table 2), but the numbers were very small, and the
confidence interval included unity. Caffeine consumption was not appreciably associated with risk for short
luteal phase length, long follicular phase length, or
Am J Epidemiol Vol. 149, No. 6, 1999
long cycle length (table 3). However, those with heavy
caffeine consumption were twice as likely to experience short cycles compared with nonconsumers (table
3). This relation was also seen when cycle length was
examined as a continuous variable; heavy caffeine
intake was associated with a decrease in mean cycle
length of over one third of a day (adjusted coefficient =
-0.38 day, standard error (SE) = 0.44, p value = 0.39).
For those with heavy caffeine intake, the reduction in
cycle length was primarily related to a decrease in follicular phase length (adjusted coefficient = -0.44 day,
SE = 0.46, p value = 0.34). Caffeine consumption was
related in a dose-response manner to a decreased risk
for long menses (table 4). A strong dose-response relation was also demonstrated between caffeine con-
554
Fenster et al.
TABLE 2. Risk of anovulation* according to caffeine Intake,
California Women's Reproductive Health Study, 1990-1991
Caffeine
Intaket
(mg/day)
0
1-150
151-300
>300
No.
of
women
(n«365)
99
139
85
44
%of
women
wfthan
6.1
5.0
6.0
2.3
Adjusted
OR*
Referent
0.71
0.97
0.36
95%CI§
0.22-2.31
0.26-3.63
0.04-3.36
• Anovulation episode £36 days.
t Conversion to milligrams of caffeine based on 107 mg/cup of
coffee, 34 mg/cup of tea, and 47 mg/can of soda (Bunker and
McWilliams, J Am Diet Assoc 1979;74:28-32).
t Adjusted odds ratios (OR) were calculated using multiple logistic regression adjusting for the following variables: maternal age,
race, body mass index, smoking status, weekly alcohol consumption, and parity.
§ Cl, confidence interval.
TABLE 3. Risk for binary cycle endpolnts according to
caffeine Intake, California Women's Reproductive Hearth
Study, 1990-1991
Caffeine
Intake*
(mg/day)
No.
of
cycles
%of
cycles
with
end point
Adjusted
ORt
95% C l t
Short luteal phase length (£10 days) vs. not short (n = 1,436)
0
1-150
151-300
>300
403
541
317
175
6.5
4.8
5.4
5.1
Referent
0.54
0.70
0.42
0.25-1.18
0.29-1.71
0.12-1.46
Long follicular phase length (>24 days) vs. not long (n = 1,526)
0
1-150
151-300
>300
425
576
338
187
5.9
4.5
4.7
5.9
Referent
0.68
0.92
1.10
0.36-1.27
0.42-2.02
0.38-3.14
Short cycle (£24 days) vs. normal length (25-35 days) (h = 1,539)
0
1-150
151-300
>300
415
584
356
184
9.9
6.9
9.3
15.8
Referent
0.78
0.91
2.00
0.42-1.44
0.46-1.81
0.98-^.06
Long cycle (236 days) vs. normal length (25-35 days) (h = 1,493)
0
1-150
151-300
>300
408
576
341
168
8.3
5.6
5.3
7.7
Referent
0.69
0.84
1.24
0.37-1.29
0.39-1.85
0.46-3.39
• Conversion to milligrams of caffeine based on 107 mg/cup of
coffee, 34 mg/cup of tea, and 47 mg/can of soda (Bunker and
McWilliams, J Am Diet Assoc 1979;74:28-32).
t Adjusted odds ratios (OR) were calculated using generalized
estimating equation analyses adjusting for the following variables:
maternal age, race, body mass index, cigarettes smoked per day,
weekly alcohol consumption, and parity.
t Cl, confidence interval.
sumption and a decreased risk for long menses
(adjusted p value = 0.01). Heavy caffeine intake was
associated with a shortening of menses length of a lit-
tle less than half of a day (adjusted coefficient = -0.39
day, SE = 0.24, p value = 0.10). Caffeine intake was
not strongly associated with variability of the menstrual
endpoints as measured by within-woman range (table
5). There was a suggestion of an increased risk for
large luteal phase range related to increasing caffeine
consumption, but the confidence intervals were wide
and included unity (table 5).
To determine whether the associations found
between caffeine intake and menstrual function might
be due to possible residual confounding by smoking or
alcohol, we examined the relation between caffeine
intake and menstrual function in women who did not
report smoking and in those who did not report either
smoking or drinking alcohol. Restricting our analyses
to nonsmokers did not appreciably alter the findings.
For example, in nonsmokers, the adjusted odds ratio
for short cycle for heavy caffeine consumption was
2.11 (95 percent confidence interval 1.03-4.33) and
for long menses, it was 0.30 (95 percent confidence
interval 0.13-0.69). Results were similar for the subset
of women who neither smoked nor drank alcohol, but
were based on smaller numbers. In addition, removing
anovulatory episodes did not appreciably affect any of
our results.
DISCUSSION
We found that women who consume caffeine are
less likely to have long menses. This finding is biologically plausible because caffeine is a known vasoconstrictor (27). Constriction of uterine blood vessels
would be expected to reduce uterine blood flow, which
could reduce menstrual bleeding and shorten the duration of menses. Research in pregnant animals (28) and
humans (29) indicates that caffeine increases uterine
vascular resistance and reduces uterine blood flow.
Our data also indicate that those whose caffeine consumption is heavy are twice as Likely to have a short
menstrual cycle compared with nonconsumers. The
mechanism by which caffeine may alter the duration of
the menstrual cycle is not clear, but such an effect
could occur via the effect of caffeine on sex hormones
or the hormone receptors. Kitts (30) found evidence to
suggest that constituents of coffee are weakly estrogenic. Caffeine inhibits the action of adenosine, which
in laboratory studies affects luteinizing hormone and
follicle-stimulating hormone (31, 32), which could in
turn affect menstrual cycle length. Gilbert and Rice
(33) found depressed estrogen levels in female monkeys at a dose level of caffeine associated with miscarriages, stillbirths, and decreased maternal weight
gain. Associations between caffeine intake and estradiol and/or estrone levels have been found in three studies of humans (34-36) but not in two others (37, 38).
Am J Epidemiol
Vol. 149, No. 6, 1999
Caffeine Consumption and Menstrual Function
TABLE 4. Risk of long menses* according to caffeine Intake,
California Women's Reproductive Health Study, 1990-1991
No.
of
Caffeine
Intaket
(mg/day)
cycles
(n= 1,726)
0
1-150
151-300
>300
477
652
385
212
%of
cycles
wltha
long
menses
Adjusted
OR*
13.4
7.4
5.2
4.7
Referent
0.51
0.34
0.30
95%CI§
0.27-0.96
0.15-0.76
0.14-O.66
* 28 days.
t Conversion to milligrams of caffeine based on 107 mg/cup of
coffee, 34 mg/cup of tea, and 47 mg/can of soda (Bunker and
McWilliams, J Am Diet Assoc 1979;74:28-32).
$ Adjusted odds ratios (OR) were calculated using repeated
measures analyses adjusting for the following variables: maternal
age, race, body mass index, cigarettes smoked per day, weekly
alcohol consumption, and parity.
§ Cl, confidence interval.
TABLE 5. Risk for large range* of binary cycle endpolnts
according to caffeine Intake, California Women's Health
Study, 1990-1991
Caffeine
intaket
(mg/day)
No.
of
women
%of
women
with a
large range
Adjusted
OR*
95% Cl§
Range of luteal phase length >3 days (n == 330)
0
1-150
151-300
>300
91
123
75
41
14.8
24.3
24.4
30.6
Referent
1.63
1.76
1.97
0.75-3.55
0.74-4.18
0.71-5.49
Range of folllcular phase length >3 days (n = 350)
0
1-150
151-300
>300
98
131
77
44
20.4
16.0
19.5
18.2
Referent
0.71
1.13
0.97
0.35-1.45
0.51-2.50
0.36-2.62
Range of menses length >7 days (n = 366)
0
1-150
151-300
>300
98
141
83
44
14.3
13.5
8.4
18.2
Referent
1.00
0.55
1.44
0.46-2.19
0.20-1.50
0.51-4.06
Range of cycle length >7 days (n = 354)
0
1-150
151-300
>300
96
134
80
44
22.9
14.9
17.5
20.5
Referent
0.62
0.86
0.95
0.31-1.26
0.39-1.90
0.36-2.49
• Longest minus shortest.
t Conversion to milligrams of caffeine based on 107 mg/cup of
coffee, 34 mg/cup of tea, and 47 mg/can of soda (Bunker and
McWilliams, J Am Diet Assoc 1979;74:28-32).
t Adjusted odds ratios (OR) were calculated using repeated
measures analyses adjusting for the following variables: maternal
age, race, body mass index, cigarettes smoked per day, weekly
alcohol consumption, and parity.
§ Cl, confidence interval.
Short cycle length may reflect underlying endocrine
patterns and thereby have potential implications for
other health endpoints. One long-term prospective
Am J Epidemiol Vol. 149, No. 6, 1999
555
study of menstrual and reproductive health found that
women with short cycle lengths (<26 days) reached
menopause 1.4 years earlier than did women with normal length cycles and about 2.2 years earlier than did
women with long cycle lengths (39). An increased risk
for low bone density has been related to earlier age at
menopause (40). Women with shorter menstrual cycle
lengths may also be at increased risk for breast cancer
if, as has been hypothesized, risk increases with more
total lifetime ovulatory menstrual cycles (41-43).
However, most epidemiologic studies have not shown
a relation between caffeine intake and an increased
risk for breast cancer (44—46).
The potential contribution of caffeine to alterations
in menstrual function has scarcely been examined and
is deserving of further investigation. Cooper et al. (47)
did not observe any notable relation between caffeine
intake and cycle length, variability, and menses
length. For this study, menstrual data on 766 women
aged 29-31 years were collected prospectively; however, the mean age of the women at the time they were
asked to recall their caffeine intake was 73 years. We
prospectively collected all data and examined potential confounders, such as alcohol, cigarette consumption, and stress. We also obtained detailed menstrual
function data using daily urine metabolites of sex
steroid hormones, whereas Cooper et al. (47) assessed
menstrual function using questionnaire data, a method
that may be inaccurate (48).
Wilcox et al. (6) prospectively investigated the relation between caffeinated beverage consumption and
time to conception in 100 women who had been trying
to conceive for up to 3 months. Women who drank the
equivalent of about one cup of caffeinated coffee per
day were half as likely to become pregnant per cycle
as were women who drank less. In addition, the estimated relative risk of infertility among women who
drank one cup per day was almost five times that of
those who drank less. Wilcox et al. (6) attempted to
examine an effect of caffeine on ovulation to explain
the association they found between caffeine consumption and decreased fertility. They examined daily levels of the urinary metabolite of progesterone for 206
cycles in approximately 50 women, but found too
small a proportion of anovulatory cycles (4 percent) to
explain the caffeine-related decreased fecundability.
We also had limited power to investigate rare endpoints such as anovulation, but our findings were in
the direction of those reported by Wilcox et al.; that is,
there was no indication that caffeine intake was related to an increased risk for anovulation.
Potential exposure misclassification may have
affected our estimates of total caffeine intake. We estimated caffeine dose by interview, and there can be
556
Fenster et al.
substantial variation in caffeine content according to
differences in serving size, brand, ingredients, method
of beverage preparation, and brewing time (18, 49).
Such random imprecision would tend to bias the effect
estimate toward the null (50). While we did not collect
information on all foods or medications containing
caffeine, their relative contribution to total caffeine
intake has been shown to be minimal (49). Coffee, tea,
and soda are the main sources of caffeine for adults in
the United States (51).
In summary, our data revealed that caffeine consumption was related to a decreased risk for long
menses and that heavy caffeine consumption was associated with an increased risk for short cycle length.
These findings could have implications for women's
long-term health. Further studies should be performed
to confirm these findings and to examine subsequent
health outcomes in women.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
ACKNOWLEDGMENTS
This research was primarily supported by National
Institute of Child Health and Development grant HD29682.
Support was also received from National Institutes of Health
grant DA01696. The hormone laboratory work was partially supported by National Institutes of Health grant ESO6198.
The authors thank Dr. Bill Lasley for supervising the laboratory analyses and Ceciley Wilder for assistance in manuscript preparation.
21.
22.
23.
24.
REFERENCES
1. Dlugosz L, Bracken MB. Reproductive effects of caffeine: a
review and theoretical analysis. Epidemiol Rev 1992;
14:83-100.
2. Mirkin BL, Singh S. Placental transfer of pharmacologically
active molecules. In: Mirkin BL, ed Perinatal pharmacology
and therapeutics. New York, NY: Academic Press, Inc.,
1976:1-69.
3. Weathersbec PS, Lodge JR. Caffeine: its direct and indirect
influence on reproduction. J Reprod Med 1977; 19:55-63.
4. Rail TW. Drugs used in the treatment of asthma. The methylxanthines, cromolyn sodium, and other agents. In: Goodman
Gilman A, Rail TW, Nies AS, et al., eds. The pharmacological
basis of therapeutics. Eighth ed. New York, NY: Pergamon
Press, 1990:25.
5. Harlow SD, Ephross SA. Epidemiology of menstruation and
its relevance to women's health. Epidemiol Rev 1995;
17:265-86.
6. Wilcox A, Weinberg C, Baird D. Caffeinated beverages and
decreased fertility. Lancet 1988;2:1453-6.
7. Christianson RE, Oechsli FW, Van den Berg BJ. Caffeinated
beverages and decreased fertility. (Letter). Lancet 1989; 1:378.
8. Williams MA, Monson RR, Goldman MB, et al. Coffee and
delayed conception. (Letter). Lancet 1990; 1:1603.
9. Grodstein F, Goldman MB, Ryan L, et al. Relation of female
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
infertility to consumption of caffeinated beverages. Am J
Epidemiol 1993;137:1353-60.
Hatch EE, Bracken MB. Association of delayed conception
with caffeine consumption. Am J Epidemiol 1993;
138:1082-92.
Stanton CK, Gray RH. Effects of caffeine consumption on
delayed conception. Am J Epidemiol 1995; 142:1322-9.
Bolumar F, Olsen J, Rebagliato M, et al. Caffeine intake and
delayed conception: a European multicenter study on infertility and subfecundity. Am J Epidemiol 1997;145:324-34.
Alderette E, Eskenazi B, Sholtz R. Effect of cigarette smoking
and coffee drinking on time to conception. Epidemiology
1995;6:403-8.
Joesoef M, Beral V, Rolfs R, et al. Are caffeinated beverages
risk factors for delayed conception? Lancet 1990;335:136-7.
Olsen J. Cigarette smoking, tea and coffee drinking, and subfecundity. Am J Epidemiol 1991;133:734-9.
Florack EIM, Zielhuis GA, Rolland R. Cigarette smoking,
alcohol consumption, and caffeine intake and fecundability.
Prev Med 1994;23:175-80.
Waller K, Swan SH, Windham GC, et al. Use of urine biomarkers to evaluate menstrual function in healthy premenopausa] women. Am J Epidemiol 1998; 147:1071-80.
Bunker ML, Me Williams M. Caffeine content of common beverages. J Am Diet Assoc 1979;74:28-32.
Holmes TH, Rahe RH. The social readjustment rating scale. J
PsychosomRes 1967;11:213-18.
Dohrenwend BS, Krasnoff L, Askenasy AR, et al.
Exemplification of a method for scaling life events. J Health
SocBehavl978;19:205-29.
Munro CJ, Stabenfeldt GH, Cragun JR, et al. Relationship of
serum estradiol and progesterone concentrations to the excretion profiles of their major urinary metabolites as measured by
enzyme immunoassay and radioimmunoassay. Clin Chem
1991;37:838-44.
Collins WP, Branch CM, Collins PO. Ovulation prediction and
detection by the measurement of steroid glucuronides. In:
Cortes-Prieto J, Campos da Paz A, Neves-e-Castro M, eds.
Research on fertility and sterility. Baltimore, MD: University
Park Press, 1981:19-33.
Baird DD, Weinberg CR, Wilcox AJ, et al. Using the ratio of
urinary oestrogen and progesterone metabolites to estimate
day of ovulation. Stat Med 1991;10:255-66.
Laird NM, Ware JH. Random-effects models for longitudinal
data. Biometrics 1982;38:963-74.
Liang K, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika 1986;73:13-22.
Greenland S. Modeling and variable selection in epidemiologic analysis. Am J Public Health 1989;79:340-9.
Benowitz NL. Clinical pharmacology of caffeine. Annu Rev
Med 1990;41:277-88.
Wilson SJ, Ayromlooi J, Errick JK. Pharmacokinetic and
hemodynamic effects of caffeine in the pregnant sheep. Obstet
Gynecol 1983;61:486-92.
Miller RC, Watson WJ, Hackney AC, et al. Acute maternal and
fetal cardiovascular effects of caffeine ingestion. Am J
Perinatal 1994;ll:132-6.
Kitts D. Studies on the estrogenic activity of a coffee extract J
Toxicol Environ Health 1987;20:37-49.
Polan ML, DeCherney AH, Haseltine FP, et al. Adenosine
amplifies folhcle-stimulating hormone action in granulosa
cells and luteinizing hormone action in luteal cells of rat and
human ovaries. J Clin Endocrinol Metab 1983;56:288-94.
Picanco-Diniz DL, Lopez-Jimenez MA, Valenca MM, et al.
Effect of adenosine on gonadotropin and prolactin secretion by
hemipituitaries in vitro. Braz J Med Biol Res 1989;22:783-5.
Gilbert SG, Rice DC. Effects of chronic caffeine consumption
in pregnant monkeys (Macacafascicularis) on blood and urine
clinical chemistry parameters. Fundam Appl Toxicol 1991;
16:299-308.
London S, Willett W, Longscope C, et al. Alcohol and other
dietary factors in relation to serum hormone concentrations in
Am J Epidemiol Vol. 149, No. 6, 1999
Caffeine Consumption and Menstrual Function
women at climacteric. Am J Clin Nutr 1991;53:166-71.
35. Petridou E, Katsouyanni K, Spanos E, et al. Pregnancy estrogens in relation to coffee and alcohol intake. Ann Epidemiol
1992;2:241-7.
36. Ferrini RL, Barrett-Connor E. Caffeine intake and endogenous
sex steroid levels in postmenopausal women: The Rancho
Bernardo Study. Am J Epidemiol 1996; 144:642^1.
37. Cooper C, Atkinson EJ, Wanner HW, et al. Is caffeine consumption a risk factor for osteoporosis? J Bone Miner Res
1992;7:465-71.
38. Westhoff C, Gentile G, Lee J, et al. Predictors of ovarian
steroid secretion in reproductive-age women. Am J Epidemiol
1996;144:381-8.
39. Whelan EA, Sandier DP, McConnaughey DR, et al. Menstrual
and reproductive characteristics and age at natural menopause.
Am J Epidemiol 1990; 131:625-32.
40. Parazzini F, Bidoli E, Franceschi S, et al. Menopause, menstrual and reproductive history, and bone density in northern
Italy. J Epidemiol Commun Health 1996;50:519-23.
41. Henderson BE, Ross RK, Judd HL, et al. Do regular ovulatory
cycles increase breast cancer risk? Cancer 1985;56:1206-8.
42. Kelsey JL, Gammon MD, John EM. Reproductive factors and
breast cancer. Epidemiol Rev 1993;15:36-47.
43. Whelan EA, Sandier DP, Root JL, et al. Menstrual cycle patterns and risk of breast cancer. Am J Epidemiol
1994;140:1081-90.
44. IARC Working Group on the Evaluation of Carcinogenic
Am J Epidemiol
Vol. 149, No. 6, 1999
45.
46.
47.
48.
49.
50.
51.
557
Risks to Humans. Coffee tea, matd, methylxanthines and
methylglyoxal. Series title: IARC monographs on the evaluation of carcinogenic risk to humans v. 51. Distributed for the
International Agency for Research on Cancer by the
Secretariat of the World Health Organization, Geneva
Switzerland, 1991.
Smith SJ, Deacon JM, Chilvers CE. Alcohol, smoking, passive
smoking and caffeine in relation to breast cancer risk in young
women. UK National Case-Control Study Group. Br J Cancer
1994;70:112-19.
McLaughlin CC, Mahoney MC, Nasca PC, et al. Breast cancer
and methylxanthine consumption. Cancer Causes Control
1992;3:175-8.
Cooper GS, Sandier DP, Whelan EA, et al. Association of
physical and behavioral characteristics with menstrual cycle
patterns in women age 29-31 years. Epidemiology
1996;7:624-8.
Kesner JS, Wright DM, Schrader SM, et al. Methods of monitoring menstrual function in field studies: efficacy of methods.
Reprod Toxicol 1992;6:385-400.
Schreiber GB, Maffeo CE, Robins M, et al. Measurement of
coffee and caffeine intake: implications for epidemiologic
research. Prev Med 1988;17:28O-94.
Rothman KJ. Modem epidemiology. Boston, MA: Little
Brown & Co., Inc., 1986.
Graham DM. Caffeine: its identity, dietary sources, intake and
biological effects. Nutr Rev 1978;36:99-102.