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Human Reproduction, Vol.30, No.7 pp. 1714 –1723, 2015
Advanced Access publication on May 6, 2015 doi:10.1093/humrep/dev099
ORIGINAL ARTICLE Reproductive epidemiology
Effects of over-the-counter analgesic use
on reproductive hormones and ovulation
in healthy, premenopausal women
R.A. Matyas 1, S.L. Mumford 1, K.C. Schliep 1, K.A. Ahrens 1, L.A. Sjaarda 1,
N.J. Perkins 1, A.C. Filiberto 1, D. Mattison 2, S.M. Zarek 1,3,
J. Wactawski-Wende 4, and E.F. Schisterman 1,*
1
Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human
Development (NICHD), NIH, Bethesda, MD, USA 2Risk Sciences International and University of Ottawa, Ottawa, ON, Canada 3Program in
Reproductive and Adult Endocrinology, NICHD, NIH, Bethesda, MD, USA 4Department of Epidemiology and Environmental Health, University
at Buffalo, The State University of New York, Buffalo, NY, USA
*Correspondence address. Epidemiology Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development,
6100 Executive Blvd, 7B03, Rockville, MD 20852, USA. Tel: +1-301-435-6893; Fax: +1-301-402-2084; E-mail: [email protected]
Submitted on November 4, 2014; resubmitted on March 14, 2015; accepted on March 25, 2015
study question: Does use of commonly used over-the-counter (OTC) pain medication affect reproductive hormones and ovulatory
function in premenopausal women?
summary answer: Few associations were found between analgesic medication use and reproductive hormones, but use during the
follicular phase was associated with decreased odds of sporadic anovulation after adjusting for potential confounders.
what is known already: Analgesic medications are the most commonly used OTC drugs among women, but their potential effects
on reproductive function are unclear.
study design, size, duration: The BioCycle Study was a prospective, observational cohort study (2005–2007) which followed
259 women for one (n ¼ 9) or two (n ¼ 250) menstrual cycles.
participants, setting, methods: Two hundred and fifty-nine healthy, premenopausal women not using hormonal contraception
and living in western New York state. Study visits took place at the University at Buffalo.
main results and the role of chance: During study participation, 68% (n ¼ 175) of women indicated OTC analgesic use.
Among users, 45% used ibuprofen, 33% acetaminophen, 10% aspirin and 10% naproxen. Analgesic use during the follicular phase was associated
with decreased odds of sporadic anovulation after adjusting for age, race, body mass index, perceived stress level and alcohol consumption
(OR 0.36 [0.17, 0.75]). Results remained unchanged after controlling for potential confounding by indication by adjusting for ‘healthy’ cycle
indicators such as amount of blood loss and menstrual pain during the preceding menstruation. Moreover, luteal progesterone was higher
(% difference ¼ 14.0, 21.6 –32.1, P ¼ 0.08 adjusted) in cycles with follicular phase analgesic use, but no associations were observed with estradiol, LH or FSH.
limitations, reasons for caution: Self-report daily diaries are not validated measures of medication usage, which could lead to
some classification error of medication use. We were also limited in our evaluation of aspirin and naproxen which were used by few women.
wider implications of the findings: The observed associations between follicular phase analgesic use and higher progesterone
and a lower probability of sporadic anovulation indicate that OTC pain medication use is likely not harmful to reproduction function, and certain
medications possibly improve ovulatory function.
study funding/competing interests: This work was supported by the Intramural Research Program of the Eunice Kennedy
Shriver National Institute of Child Health and Human Development, National Institutes of Health (contract # HHSN275200403394C).
The authors have no conflicts of interest to disclose.
Key words: over-the-counter drugs / analgesics / ovulation / reproductive hormones
Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology 2015. This work is written by (a) US Government employee(s) and
is in the public domain in the US.
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Analgesic use, reproductive hormones and ovulation
Introduction
Human reproduction is an inefficient, complex process that requires a
series of intricately timed events to achieve pregnancy. Ovulation and
implantation are critical events that are affected by endogenous and
exogenous factors and, for those trying to conceive, maximizing the likelihood of both events can lead to greater reproductive success. It has
been hypothesized that aspirin use could improve fertility outcomes,
possibly due to increased ovarian and/or endometrial vascular perfusion
(Schisterman et al., 2014). Yet, other medications used for pain relief (i.e.
analgesics), have been reported to inhibit ovulatory function in human
and animal studies (Priddy et al., 1990; Miyazaki et al., 1991; Kranzfelder
et al., 1992; Athanasiou et al., 1996).
Analgesics are among the most commonly used medications and
proper use is considered safe and effective (Hancock et al., 1992;
Eggen, 1993; Furu et al., 1997; Kaufman et al., 2002). According to a
2006 US survey of ambulatory adults, OTC analgesics are the most frequently used individual products among U.S. Food and Drug
Administration-regulated medications, with 17 –23% of the population
using such medications in a given week (Kaufman et al., 2002). Furthermore, women reported greater OTC analgesic use than men
(Kaufman et al., 2002; Koushede et al., 2011) but analgesic use across
the menstrual cycle and the effects of OTC analgesics on hormones
and ovulatory function in premenopausal women have not been investigated. Given that OTC analgesics are readily available, commonly used to
treat dysmenorrhea, and little is understood about their potential effects
on reproductive function, we sought to investigate both the acute and
chronic effects of daily-measured OTC analgesic use on reproductive
hormones and ovulatory function in healthy, eumenorrheic, premenopausal women with carefully timed, repeated measures of blood
hormone concentrations across the menstrual cycle.
Materials and Methods
Study population
The BioCycle Study (2005– 2007) included 259 healthy, regularly menstruating women in Western New York State, 18 – 44 years of age. In this prospective cohort study, women participated for one (n ¼ 9 women) or two
(n ¼ 250) menstrual cycles. Exclusion criteria included current use of oral
contraceptives, vitamin/mineral supplements, or certain prescription medications; pregnancy, breastfeeding, or trying to conceive in the preceding
6 months; self-reported body mass index (BMI) of ,18 or .35 kg/m2 at
screening; laparoscopy confirmed endometriosis; and diagnosis of chronic
conditions, including menstrual or ovulatory disorders. The University at
Buffalo Health Sciences Institutional Review Board (IRB) approved the
study and served as the IRB designated by the National Institutes of Health
under a reliance agreement. All participants provided written informed
consent. Further details of the study design and inclusion and exclusion criteria were previously described (Wactawski-Wende et al., 2009).
Participants provided fasting blood samples up to eight times per cycle during
the following menstrual cycle phases: menses; mid and late follicular phase; luteinizing hormone (LH) surge; expected ovulation; and the early, mid, and late
luteal phase. Visits were timed using fertility monitors (Clearblue Easy Fertility
MonitorTM ; Inverness Medical, Waltham, Massachusetts) (Howards et al.,
2009). Nearly all participants (94%) completed ≥7 clinic visits (including
blood collection) per cycle, and 100% completed ≥5 clinic visits per cycle.
Biospecimen protocols were designed to minimize variability (WactawskiWende et al., 2009). All samples were processed and frozen at 2808C within
90 min of phlebotomy, and analytes were measured in participant-specific
batches within a single run. Estradiol, LH, follicle-stimulating hormone
(FSH) and progesterone were measured in fasting serum samples using solidphase competitive chemiluminescent enzymatic immunoassays (DPC Immulite 2000 analyzer, Siemens Medical Solutions Diagnostics, Deerfield, IL,
USA) at the Kaleida Health Center for Laboratory Medicine (Buffalo, NY,
USA). The inter-assay coefficients of variation for these tests reported by
the laboratory were ≤10% for estradiol, ≤5% for LH and FSH, ≤14% for
progesterone.
Sporadic anovulatory cycles were defined as cycles with peak serum progesterone concentrations ≤5 ng/ml and no observed serum LH peak during
the mid or late luteal phase visit to ensure that progesterone was assessed
during the luteal phase (Lynch et al., 2014).
Medication use assessment
Participants recorded daily medication intake (type [e.g. name of medication], dose, frequency per day). Eighty-nine percent of the participants completed .75% of their daily diaries. Each medication reported in the daily diary
was grouped into one of six major categories based on indication: pain,
allergy, cold and cough, gastrointestinal, antibiotics, musculoskeletal and
central nervous system. Each medication was subsequently identified via
the Micromedex online database or manufacturer’s website and further categorized by its primary active ingredient (e.g. acetaminophen, ibuprofen,
aspirin, etc.) and dose. Combination medications were assigned to a category
based on the principal ingredient in the product. This analysis focuses on the
active ingredients in the category of pain (i.e. analgesic) medications. Total
dosage of medication each day was determined for each woman. If the
total doses of medications reported were greater than the maximum recommended daily dose as defined by the label, this was defined as an overdose.
Covariate assessment
At study enrollment, age, race, smoking, reproductive history and perceived
stress were obtained by using questionnaires (Wactawski-Wende et al.,
2009). Perceived stress level was also determined at baseline using the
Cohen Perceived Stress Scale (PSS) and was subsequently categorized as:
low stress (,14), moderate stress (14– 27) and high stress (28 –42)
(Cohen et al., 1983). Participants also prospectively recorded daily stress
levels (not stressful [1], a little stressful [2], very stressful [3]) over the
course of the two menstrual cycles. Alcohol intake was also recorded daily
and was averaged over the period of observation as follows: low (0– 0.5
drinks/day), moderate (0.5– 1 drinks/day) or high (≥ 1 drinks/ day). Information about menstrual blood loss was assessed using a detailed menstrual
flow questionnaire (Wyatt et al., 2001) where participants characterized
their blood loss as low, medium or heavy (Dasharathy et al., 2012). Previous
week menstrual pain was assessed at each study visit using a questionnaire
that captured the severity (none, mild, moderate and severe) of the following
symptoms: swelling of hands or feet, breast tenderness or fullness, lower abdominal cramping, generalized aches and pains, lower backache, and headache. Each cycle was then assigned a menstrual pain score, calculated by
taking the average of pain scores from assessments during menses and midfollicular phase.
Statistical analysis
Descriptive statistics for continuous and categorical covariates were compared between users and non-users of any OTC analgesic, and for each of
the four most commonly reported analgesic active ingredients: acetaminophen, aspirin, ibuprofen, and naproxen. Chi-square or Fisher exact tests
were used to evaluate significance for categorical variables and Student’s
t-test for continuous variables.
Timing of medication use during the menstrual cycle was determined using
a standardized 28-day cycle to evaluate in what phase the majority of
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Matyas et al.
analgesic use occurred. Days were aligned in relation to the day of ovulation,
which was estimated based on dates and levels of LH peak from the fertility
monitor compared with the observed LH maximum value in serum and the
first day of progesterone rise (Mumford et al., 2011). If the cycle was classified
as anovulatory, cycle day 14 was assigned as the estimated day of expected
ovulation for comparison purposes. Average frequency of analgesic use
was calculated and compared between early cycle (standardized Day 1
[start of menses] to standardized Day 5 [average bleeding length]), which
included menses), mid cycle (standardized Day 6 to standardized Day 13
[day of LH surge]) and late cycle (standardized Day 14 [estimated day of ovulation] to standardized Day 28 [day prior to start of next menses]) using linear
mixed models to account for repeated cycles within women. Pair-wise
comparisons were made between early, mid and late cycle phases using
the Tukey method to account for multiple comparisons.
All hormones, as well as blood pressure, pulse, cycle length, menses length
and blood loss, were log-transformed for normality. Linear mixed models
were used to estimate the associations between follicular phase (start of
menses to estimated day of ovulation) analgesic use (yes/no) and estradiol
and FSH concentrations across the menstrual cycle, as well as mid-cycle
LH, and luteal progesterone concentrations, corresponding to the cycle
phases with the most hormonal variability. We also assessed the effects
of time-varying analgesic use on reproductive hormone concentrations
(estradiol and FSH across the cycle, mid-cycle LH, and luteal progesterone)
by averaging reported daily analgesic use for the 5 days before each clinic visit.
Table I Participant characteristics according to ever versus never using over-the-counter analgesic medicine during the
study period.
Total cohort
User
Non-user
P-valuea
.............................................................................................................................................................................................
Number of women (%)
259
179 (69)
80 (31)
–
27.3 + 8.2
27.8 + 8.2
26.1 + 8.1
0.12
Mean + SD
Age, years
2
BMI, kg/m
24.1 + 3.9
24.4 + 3.7
23.4 + 4.1
0.06
Age at menarche, years
12.5 + 1.2
12.4 + 1.2
12.6 + 1.4
0.25
98.8 + 1.1
99.2 + 1.1
98.1 + 1.1
0.37
Baseline blood pressure (mmHg)b
Systolic
Diastolic
60.7 + 1.1
61.0 + 1.1
59.9 + 1.2
0.34
Pulse (bpm)
69.2 + 1.1
69.1 + 1.1
69.5 + 1.1
0.74
n (%):
Race
0.02
White
154 (59)
117 (76)
37 (24)
Black
51 (20)
30 (59)
21 (41)
Other
54 (21)
32 (59)
22 (41)
Education
0.94
Post-secondary
226 (87)
156 (69)
70 (31)
≤High school
33 (13)
23 (70)
10 (30)
Nonsmoker
249 (96)
173 (69)
76 (31)
Current smoker
10 (4)
6 (60)
4 (40)
Low
25 (10)
16 (64)
9 (36)
Moderate
92 (36)
66 (72)
26 (28)
High
142 (55)
97 (68)
45 (32)
Low
191 (74)
122 (64)
69 (36)
Moderate
34 (13)
27 (79)
7 (20.6)
High
34 (13)
30 (88)
4 (12)
Low
85 (33)
54 (64)
31 (36)
Moderate
82 (32)
66 (80)
16 (20)
High
92 (36)
59 (64)
33 (36)
Smoking
0.50
Physical activity
0.72
Alcohol consumptionc
0.007
Perceived stressd
a
0.03
P-value from Chi-Square or Fisher’s test for categorical variables and Student’s t-test for continuous variables comparing user to non-user group.
Geometric means + SD.
c
Alcohol levels: low (≤0.5 drinks/day), moderate (0.5 –1 drinks/day), high (≥1 drinks/day) based on average over entire study period.
d
Daily perceived stress scale (PSS) tertiles (low, moderate and high stress) based on average PSS over entire study period.
b
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Analgesic use, reproductive hormones and ovulation
Time-varying estimates were calculated using weighted models to control
for factors such as reproductive hormones which could be both causes
and consequences of short-term medication use (Robins et al., 2000). We
constructed inverse probability weights using concurrent estradiol, LH,
FSH, progesterone, blood loss and menstrual pain variables. Generalized
linear models were used to estimate the effect of follicular phase analgesic
use on the odds of sporadic anovulation while accounting for multiple
cycles per woman.
Models were adjusted for potential confounders including age (continuous), race (white/black/other), BMI (continuous), smoking status
(nonsmoker or current smoker), perceived stress (low/medium/high)
and alcohol intake (low/moderate/high) (note that in the time-varying
models perceived stress and alcohol intake were handled as time-varying
confounders). Additional models were adjusted for ‘healthy cycle indicators’: blood loss during menstruation and menstrual pain score. These
potential confounders were considered indicators of both analgesic medication use and ovulation. We hypothesized that greater blood loss during
menstruation may reflect greater prior-cycle endometrial growth and
therefore increase the likelihood of ovulation (Dasharathy et al., 2012).
Similarly, cycles with more menstrual pain, a potential indicator for
analgesic use, may also be associated with ovulatory status (Dawood,
1985). All analyses were carried out using SAS version 9.3 (SAS Institute,
Inc., Cary, NC, USA).
Results
Characteristics of participants using
medication
Participants were on average relatively young (mean age, 27.3 years), of
healthy weight (mean BMI, 24.1), educated (87% had post-secondary
education), and primarily non-smokers (96%) (Table I). Compared
with the non-users, women who had taken any OTC analgesic during
the study period were older (P ¼ 0.04), and were more commonly
white (P ¼ 0.02), with reported high category of alcohol consumption
(P , 0.01) and moderate stress level (P ¼ 0.03, Table I).
(38.9%) of the women in the study with use of multiple medications.
Women reporting use of multiple analgesic active ingredients contributed to each active ingredient category.
Analgesic use and the menstrual cycle
Over-the-counter analgesics were taken primarily during menstruation,
with a steep decline after menstruation (Fig. 1). Analgesic users had significantly greater blood loss during menstruation (P ¼ 0.025), higher
average luteal progesterone (P ¼ 0.017) and heavier menstrual flow
(P ¼ 0.0048) (Table II) than non-users.
Anovulation in analgesic users
Of the 509 cycles studied, 42 (8.3%) were anovulatory and 31 women
contributed to these 42 cycles. Based on unadjusted results, OTC
analgesic users had a lower percentage of anovulatory cycles compared
with non-users (4 versus 14%, P ¼ 0.009). Follicular phase OTC analgesic was associated with a decreased odds of an anovulatory cycle
(OR [95% CI] ¼ 0.32 [0.16, 0.65]). This relationship remained after
adjusting for age, race, BMI, smoking, perceived stress, and alcohol
intake (OR ¼ 0.36 [0.17, 0.75]), and upon adjusting for potential
confounding-by-indication variables (blood loss amount and menstrual
pain score) (OR ¼ 0.33 [0.14, 0.76]; Table III).
Hormones in analgesic users
OTC analgesic use during the follicular phase of the menstrual
cycle was associated with higher luteal progesterone concentrations
(% difference ¼ 14.0 [21.6, 32.1]; P ¼ 0.08 adjusted), but analgesic
use was not associated with differences in estradiol, LH or FSH concentrations (Table IV). Use of naproxen during the follicular phase was associated with lower periovulatory LH whereas no other medication type
was associated with LH. Interestingly, when naproxen users were
excluded from the any analgesic use analysis as a sensitivity analysis, we
observed a significant increase in periovulatory LH concentrations.
When analgesic use was modeled as a time-varying variable to assess
short-term effects of recent medication use, we observed lower
Active ingredient use and duration
More than half (68%) of the study participants reported any OTC
analgesic during the course of the study. The most commonly used
OTC analgesic active ingredients were ibuprofen (45% of women, 513
person-days reported), acetaminophen (33% of women, 227 persondays reported), naproxen (10% of women, 83 person-days reported)
and aspirin (10% of women, 71 person-days reported). Of those who
reported consuming these active ingredients, the median number of
days (intraquartile range) consumed was 3 (1, 6) days for ibuprofen, 2
(1, 3) days for acetaminophen, 2 (1, 5) days for naproxen and 3 (1, 4)
days for aspirin. The maximum recommended daily dose, as defined
by the label, of each OTC analgesic evaluated in this study was
1200 mg for ibuprofen, 4000 mg of acetaminophen, 660 mg of naproxen
and 4000 mg of aspirin. Consumption over the maximum recommended
daily dose of an active ingredient was reported on 65 days for ibuprofen
(including four occurrences over the prescription dose level of 3200 mg/
day), 15 days for acetaminophen and 12 days for naproxen. There were
no occurrences of consuming over the recommended daily dose
in aspirin in the 71 days it was consumed. A total of 54 women, during
57 cycles, reported taking more than one analgesic. The most common
combination was acetaminophen and ibuprofen, which accounted for 21
Figure 1 Over-the-counter analgesic medication use across the
menstrual cycle, standardized to a 28-day cycle centered around ovulation (Day 14).
1718
Table II Menstrual cycle characteristics and average hormone concentrations according to analgesic medication use overall and by active ingredient.
Total
cohort
Any analgesic
Aspirin
Naproxen
Acetaminophen
Ibuprofen
...................................... .................................... ...................................... .................................... ......................................
User
Non-user
P
User
Non-user
P
User
Non-user
P
User
Non-user
P
User
Non-user
P
..........................................................................................................................................................................................................................................................
Number of cycles (%)
509
285 (56)
224 (44)
–
39 (7.7)
470 (92.3)
–
34 (6.7)
475 (93.3)
28.5 + 4.0 28.8 + 4.1
–
131 (25.7) 378 (74.3)
–
177 (34.8) 332 (65.2)
–
0.93
28.6 + 3.1 28.9 + 4.4
0.92
28.8 + 3.5 28.9 + 4.4
0.78
0.1
7.1 + 2.2
0.68
7.13 + 2.3 6.9 + 2.2
0.35
Mean + SD
Cycle length, days
28.8 + 4.1 28.7 + 3.5 29.0 + 4.8
0.67
29.1 + 2.7 28.8 + 4.2
0.59
Menses length, days*
7.0 + 2.2
0.058
7.9 + 2.4
0.012 7.6 + 2.2
Blood loss, ml*
46.0 + 2.7 50.5 + 2.7 40.5 + 2.6
0.025
53.7 + 2.4 45.4 + 2.7
0.21
61.3 + 1.4 45.0 + 2.7
0.076
48.6 + 2.5 45.1 + 2.7
0.77
48.5 + 2.9 44.7 + 2.5
0.081
Estradiol, pg/ml
83.0 + 1.5 83.9 + 1.4 82.0 + 1.5
0.91
92.6 + 1.4 82.3 + 1.5
0.32
76.8 + 1.4 83.5 + 1.5
0.15
89.2 + 1.4 81.0 + 1.5
0.27
82.0 + 1.4 83.6 + 1.5
0.61
Luteal progesterone, ng/ml
3.5 + 2.3
3.9 + 2.0
3.1 + 2.6
0.017
3.8 + 1.7
3.5 + 2.4
0.89
3.6 + 2.4
3.5 + 2.3
0.73
4.4 + 1.9
3.3 + 2.4
0.014 3.8 + 2.0
3.4 + 2.5
0.18
FSH, mIU/ml
5.4 + 1.4
5.4 + 1.4
5.3 + 1.4
0.79
5.1 + 1.4
5.4 + 1.4
0.91
5.3 + 1.5
5.4 + 1.4
0.26
5.2 + 1.4
5.4 + 1.4
0.32
5.6 + 1.4
5.3 + 1.4
0.46
LH, ng/ml
6.3 + 1.5
6.2 + 1.5
6.3 + 1.4
0.36
6.6 + 1.4
6.2 + 1.5
0.62
5.1 + 1.5
6.4 + 1.4
0.0082 6.1 + 1.4
6.3 + 1.5
0.28
6.5 + 1.4
6.2 + 1.5
0.52
Menstrual pain score
9.7 + 2.8
10.1 + 2.9 9.1 + 2.6
0.001
10.6 + 2.4 9.6 + 2.8
Light
154 (33)
76 (28)
78 (40)
0.0048 10 (28)
144 (33)
6 (18.2)
148 (34.1)
Medium
159 (34)
94 (35)
65 (33)
11 (31)
148 (34)
14 (42.4)
145 (33.4)
Heavy
154 (33)
101 (37)
53 (27)
15 (42)
139 (32)
13 (39.4)
141 (39.4)
42 (8)
11 (4)
31 (14)
2 (5.9)
40 (8.4)
7.2 + 2.2
6.8 + 2.3
6.9 + 2.2
7.0 + 2.2
7.0 + 2.3
0.071 10.1 + 3.0 9.6 + 2.8
0.87
10.2 + 2.9 9.5 + 2.8
0.085 10.1 + 2.8 9.5 + 2.8
0.038
0.29
0.03
37 (30.1)
117 (34.0)
0.97
50 (29.6)
104 (34.9)
0.0099
45 (36.6)
114 (33.1)
55 (32.5)
104 (34.9)
41 (33.3)
113 (32.9)
64 (37.9)
90 (30.2)
4 (3.1)
38 (10.1)
n (%):
Menstrual flow
Anovulation
Yes
0.0009 1 (3)
41 (9)
0.13
0.52
0.013 7 (4.0)
35 (10.5)
0.065
Blood loss (low, medium, heavy) was estimated from detailed participant questionnaire.
Previous week menstrual pain was assessed at each study visit (none, mild, moderate and severe) of the following symptoms: swelling of hands or feet, breast tenderness or fullness, lower abdominal cramping, generalized aches and pains, lower
backache, and headache. Menstrual pain score was calculated as the average of pain scores from Day 2 and Day 7 clinic visits for each cycle.
FSH, follicle stimulating hormone; LH, luteinizing hormone.
*Missing values (N ): menses length (7), blood loss (7).
Matyas et al.
1719
0.03
0.32 (0.11, 0.92)
0.53 (0.19, 1.49)
0.56
1.50 (0.38, 5.85)
0.68
0.70 (0.13, 3.76)
0.01
0.33 (0.14, 0.76)
C
Model A: Unadjusted.
Model B: Adjusted for age (continuous), BMI (continuous), race (categorical: white, black, other), stress (baseline Cohen Perceived Stress Scores categorized as low (,14), moderate (14 –27) or high (28 –42) stress levels), and alcohol (categorical:
low (0 – 0.5 drinks/day), moderate (0.5 –1 drinks/day) or high (.1 drinks/day)).
Model C: Adjusted for Model B variables and blood loss during preceding menstruation and menstrual pain (both continuous variables).
0.07
0.43 (0.18, 1.06)
0.23
0.06
0.43 (0.18, 1.04)
0.08
0.06
0.41 (0.16, 1.05)
0.42 (0.16. 1.10)
0.90
0.68
0.79 (0.25, 2.45)
1.10 (0.27, 4.47)
0.46
0.16
0.37 (0.09, 1.49)
0.52 (0.09, 3.00)
0.006
0.36 (0.17, 0.75)
B
0.002
0.32 (0.16, 0.65)
A
P-value
Ibuprofen
P-value
Acetaminophen
P-value
Naproxen
P-value
Aspirin
P-value
Any analgesic
Model
..........................................................................................................................................................................................................................................................
Table III Association of analgesic medication use during the follicular phase of the menstrual cycle and sporadic anovulation. Data are odds ratios (95% confidence
intervals).
Analgesic use, reproductive hormones and ovulation
estradiol (% difference ¼ 215.5 [220.7, 29.9]; P ¼ , 0.001) and
higher FSH (% difference ¼ 10.0 [4.8, 15.5]; P ¼ 0.001) among
women who used analgesics during the 5 days prior to the blood draw
compared with women who did not use analgesics during this time
period (Table V). Mid-cycle LH and luteal progesterone were not significantly associated with time-varying analgesic use. Results were similar
after adjustment for concurrent reproductive hormones using marginal
structural models.
Discussion
Over-the-counter analgesic use was highest during menses and was associated with reduced odds of having an anovulatory menstrual cycle
among healthy women with regular cycles and not on oral contraceptives. The women who took analgesics during the follicular phase of
their menstrual cycle also had significantly higher luteal progesterone
than those who did not, but LH, FSH and estradiol levels were not significantly different between users and non-users. However, we did observe
that analgesics taken during the 5 days prior to blood sample collection
were associated with lower estradiol and higher FSH concentrations.
These findings support that OTC analgesic use at the reported doses
and frequency of use does not adversely affect reproductive function in
normally cycling women who are not using oral contraception, and
that analgesic use may be associated with ovulatory function. Moreover,
a protective effect of aspirin or other OTC analgesics on ovulation lends
support to previous studies reporting improved fertility outcomes associated with aspirin use (Empson et al., 2002; Farquharson et al., 2002;
Schisterman et al., 2014).
Our findings are not inconsistent with the limited existing literature
regarding OTC analgesic use and ovulation and reproductive hormones.
Though animal studies consistently have shown that non-steroidal antiinflammatory drugs (NSAIDs) are associated with inhibition of ovulation,
studies among women are less clear (Gaytan et al., 2006). In a randomized crossover trial of ibuprofen use (800 mg, three times per day for
10 days beginning during the follicular phase), no associations with reproductive hormones were observed, though significant delays in the timing
of ovulation were found, supporting the theory that ovulation may occur
through inflammatory-related processes that lead to the targeted
rupture of a follicle and that anti-inflammatory agents can interfere
with that process (Uhler et al., 2001). Though this previous study benefited by assessing ovulation by ultrasound, the study utilized high doses of
ibuprofen for 10 consecutive days among a small group of women (n ¼
12), which are important distinctions from the present observational
study. Similarly, a retrospective study of over 1800 natural IVF cycles
reported short-term low-dose NSAID use diminished the rate of unwanted premature ovulations (Kawachiya et al., 2012). It is also important to note that the present study reveals the vast majority of analgesic
use in healthy young women occurs during the early follicular phase, highlighting the relevance of studying the effects of this common timing of exposure. Inconsistencies in the literature regarding the effects of NSAID
use on ovulatory function may relate to the timing of exposure, particularly when comparing observational and intervention studies, or due to
differences in the populations being studied.
With regard to hormone levels, previous work indicates that circulating levels of hormones (e.g. progesterone, LH, estradiol, FSH) are unchanged in the presence of analgesic use (Bauer et al., 2013) even
though ovulation was delayed (Uhler et al., 2001). However, findings in
1720
Table IV Follicular phase analgesic medication use and percent difference of reproductive hormone concentrations.
Percent difference (95% confidence interval)
..........................................................................................................................................................................................................................................................
Active ingredient
Model
E2 (pg/ml)
P-value
FSH (mIU/ml)
P-value
Mid-cycle LH (ng/ml)
P-value
Luteal progesterone
(ng/ml)
P-value
..........................................................................................................................................................................................................................................................
21.8 (28.5, 5.5)
22.7 (29.6, 4.7)
22.7 (29.9, 5.1)
Any analgesic
A
B
C
1.9 (24.7, 8.8)
2.0 (24.7, 9.2)
2.8 (24.2, 10.4)
0.59
0.56
0.44
1.6 (23.8. 7.2)
0.6 (24.7, 6.2)
0.8 (24.8, 6.7)
0.57
0.83
0.78
Aspirin
A
B
C
10.9 (22.8, 26.5)
12.3 (21.6, 28.2)
14.8 (20.10, 31.9)
0.12
0.09
0.05
23.3 (213.2, 7.8)
26.5 (215.8, 4.0)
27.0 (216.8, 4.0)
0.55
0.22
0.20
8.5 (25.8. 25.0)
5.6 (28.4, 21.9)
5.0 (29.7, 22.1)
Naproxen
A
B
C
212.2 (223.8, 1.1)
215.8 (226.9, 23.2)
215.8 (227.0, 22.8)
0.07
0.02
0.02
21.1 (212.0, 11.1)
23.0 (213.3, 8.5)
25.1 (215.5, 6.5)
0.85
0.22
0.37
224.7 (235.3, 212.5)
224.0 (234.6, 211.6)
225.5 (236.2, 213.1)
Acetaminophen
A
B
C
6.3 (22.1, 15.3)
6.8 (21.5, 15.8)
6.4 (22.2, 15.8)
0.15
0.11
0.15
22.6 (29.0, 4.1)
22.7 (8.8, 3.8)
23.1 (29.4, 3.7)
0.43
0.40
0.37
24.3 (212.3, 4.5)
26.4 (212.2, 2.2)
26.9 (215.0, 2.0)
Ibuprofen
A
B
C
20.2 (27.1, 7.2)
1.5 (25.7, 9.3)
2.2 (25.3, 10.3)
0.95
0.69
0.57
0.05
0.15
0.10
5.3 (22.5, 13.8)
6.2 (21.9, 14.9)
6.7 (21.8, 15.8)
6.1 (0.0001, 12.5)
4.4 (21.5, 10.7)
5.3 (20.9, 12.0)
0.62
0.47
0.48
0.26
0.45
0.52
16.4 (1.8, 33.1)
16.2 (1.13, 33.3)
14.0 (21.6, 32.1)
7.0 (217.3, 40.8)
4.9 (220.1, 37.6)
0.9 (224.2, 34.2)
0.03
0.03
0.08
0.58
0.73
0.95
218.1 (238.7, 9.5)
218.5 (239.2, 9.4)
220.0 (240.9, 8.2)
0.18
0.17
0.15
0.33
0.14
0.12
16.8 (20.9, 37.7)
16.4 (21.5, 37.4)
13.9 (24.3, 35.5)
0.06
0.07
0.14
0.18
0.14
0.12
11.2 (24.1, 28.9)
10.6 (25.2, 29.0)
8.5 (27.6, 27.4)
0.16
0.20
0.32
0.0002
0.0004
0.0002
Model A: Unadjusted.
Model B: Adjusted for age (continuous), BMI (continuous), race (categorical: white, black, other), stress (baseline Cohen Perceived Stress Scores categorized as low (,14), moderate (14 –27) or high (28 –42) stress levels), and alcohol (categorical:
low (0 – 0.5 drinks/day), moderate (0.5 –1 drinks/day) or high (.1 drinks/day)).
Model C: Adjusted for Model B variables and Healthy Cycle Indicators (blood loss during preceding menstruation and menstrual pain (both continuous variables)).
E2, estradiol; LH, luteinizing hormone; FSH, follicle-stimulating hormone; BMI, body mass index.
Matyas et al.
Percent difference (95% confidence interval)
..........................................................................................................................................................................................................................................................
Active ingredient
Model
E2 (pg/ml)
P-value
FSH (mIU/ml)
P-value
Mid-cycle LH
(ng/ml)
P-value
Luteal progesterone
(ng/ml)
P-value
..........................................................................................................................................................................................................................................................
215.3 (220.5, 29.7)
215.0 (220.3, 29.4)
215.5 (220.7, 29.9)
215.3 (221.0, 29.2)
,0.0001
,0.0001
,0.0001
,0.0001
Any analgesic
A
B
C
D
Aspirin
A
B
C
D*
–
Naproxen
A
B
C
D
217.9 (231.6, 21.4)
218.4 (223.0, 22.1)
218.8 (232.3, 22.6)
228.2 (240.2, 213.8)
Acetaminophen
A
B
C
D
25.8 (214.7, 4.0)
24.5 (213.6, 5.4)
25.1 (214.1, 4.8)
23.4 (212.7, 6.9)
0.24
0.36
0.30
0.50
Ibuprofen
A
B
C
D
216.7 (223.2, 29.6)
216.5 (223.1, 29.3)
216.7 (223.3, 29.6)
217.1 (224.5, 29.0)
,0.0001
,0.0001
,0.0001
,0.0001
8.4 (29.7, 30.0)
9.1 (29.1, 30.9)
8.0 (210.0, 29.6)
0.39
0.35
0.41
–
0.03
0.03
0.02
0.0004
,0.0001
,0.0001
0.0001
0.0007
217 (213.2, 11.5)
23.8 (215.5, 9.6)
23.7 (215.5, 9.7)
21.4 (214.6, 14.0)
0.79
0.56
0.57
0.85
28.9 (224.1, 9.3)
211.9 (226.7, 5.9)
213.5 (228.0, 4.0)
214.0 (229.2, 4.5)
0.32
0.18
0.12
0.13
0.96
0.74
0.75
24.6 (232.3, 34.4)
24.8 (232.5, 34.4)
23.6 (231.8, 36.2)
–
0.79
0.78
0.83
–
26.8 (242.0, 49.8)
211.5 (244.7, 41.6)
214.3 (246.4, 36.9)
–
0.77
0.61
0.52
–
25.5 (9.1, 44.3)
24.0 (8.0, 42.2)
23.5 (7.6, 41.8)
14.6 (28.1, 42.9)
0.001
0.002
0.003
0.23
238.3 (256.8, 211.9)
239.2 (257.4, 213.2)
239.8 (257.9, 214.0)
244.6 (266.4, 28.5)
0.008
0.006
0.005
0.02
244.5 (268.6, 22.8)
240.5 (265.9, 3.6)
241.6 (266.5, 1.7)
240.8 (262.4, 26.3)
0.04
0.07
0.06
0.03
4.7 (22.9, 12.9)
2.9 (24.5, 10.9)
2.8 (24.6, 10.8)
1.8 (27.1, 11.5)
0.23
0.46
0.47
0.71
5.2 (213.1, 27.5)
3.5 (214.7, 25.5)
3.8 (214.5, 26.1)
2.8 (214.0, 24.3)
0.60
0.73
0.70
0.78
15.4 (211.7, 50.8)
15.4 (211.6, 50.7)
13.4 (213.1, 48.0)
19.8 (25.1, 51.3)
0.29
0.29
0.35
0.13
0.23
0.32
0.32
0.10
26.9 (226.1, 17.2)
212.7 (230.7, 10.1)
214.0 (231.8, 8.4)
216.0 (231.6, 3.2)
11.7 (6.4, 17.3)
10.1 (4.9, 15.6)
10.0 (4.8, 15.5)
10.3 (4.2, 16.7)
0.3 (212.7, 15.3)
22.3 (214.8, 12.1)
22.2 (214.8, 12.2)
–
13.7 (6.9, 21.0)
11.7 (5.0, 18.8)
11.5 (4.8, 18.6)
14.7 (7.7, 22.1)
–
,0.0001
0.0005
0.0006
,0.0001
10.2 (26.1, 29.4)
8.9 (27.8, 28.6)
8.8 (27.8, 28.5)
15.1 (22.7, 36.0)
Analgesic use, reproductive hormones and ovulation
Table V Time-varying analysis of analgesic medication use in the preceding 5 days (yes/no) and associated percent difference in reproductive hormone
concentrations.
0.54
0.25
0.20
0.096
Model A: Unadjusted.
Model B: Adjusted for age (continuous), BMI (continuous), race (categorical: white, black, other), stress (average of stress levels recorded in the daily diary in the preceding 5 days: not stressful [1], a little stressful [2], very stressful [3]), and alcohol
(average number of drinks per day recorded in the daily diary in the preceding 5 days).
Model C: Adjusted for Model B variables and blood loss during preceding menstruation and menstrual pain (both continuous variables).
Model D: Adjusted for Model C variables and concurrent reproductive hormones using marginal structural models.
E2, estradiol; LH, luteinizing hormone; FSH, follicle-stimulating hormone; BMI, body mass index.
*Unable to estimate due to low numbers of aspirin users and unstable weight models.
1721
1722
other studies regarding estrogen levels are conflicting, reporting both
higher and lower levels of estrogen with OTC analgesic use (Cramer
et al., 1998; Gates et al., 2010).
Though the specific mechanisms by which OTC analgesics may affect
ovulation are incompletely understood, it is plausible that increased
blood flow via increased ovarian and/or endometrial vascular perfusion,
associated with aspirin treatment may play an important role in improving fertility outcomes (Rubinstein et al., 1999). Indeed, studies have
demonstrated interdependence of blood flow to reproductive organs
and reproductive hormones which may have subsequent effects on ovulation (Battaglia et al., 1990), and aspirin treatment has been shown to
increase uterine and ovarian blood flow velocity (Rubinstein et al.,
1999). Aspirin and other analgesics are known to inhibit two forms of
the cyclooxygenase (Cox) enzyme, Cox-1 and Cox-2, which can lead
to increased blood flow. Specifically, these enzymes catalyze the conversion of arachidonic acid to eicosanoids, causing vasodilation and also
decreased platelet aggregation. At low doses (typically 70–150 mg),
aspirin effectively inhibits platelet production of Thromboxane A2
(TXA2) with little effect on endothelial Prostacyclin (PGI2), resulting in
a net increase in the PGI2:TXA2 ratio to decrease thrombosis and increase blood flow (FitzGerald et al., 1983; Patrono et al., 2001; Vane
and Botting, 2003). Such mechanisms may have contributed to the protective effect of analgesic use on ovulation observed here. Indeed, any
effects of analgesic use on ovulatory function are unlikely to be directly
due to altered gonadotrophin or steroid hormone concentrations, as
we observed only short-term associations between recent OTC analgesic use and lower estradiol, and higher FSH concentrations, but no sustained impact on hormonal patterns. Further studies are needed to
determine whether analgesics, and which analgesic types in particular,
may be working through hormonal or inflammatory pathways to influence ovulatory function.
Our results of improved ovulatory function among women reporting
follicular phase OTC analgesic use were robust to adjustment by various
potential confounding factors. As previously stated, several characteristics (age, BMI, race, stress and alcohol) were considered potential confounders because of their known or possible associations with
ovulation, reproductive hormones, and analgesic use. Where appropriate, we took the time-varying nature of stress and alcohol consumption
into account in our adjusted models. Additionally, we attempted to
address potential confounding by indication. Specifically, it is possible
that women who have a more painful menses are also more likely to regularly ovulate and use OTC analgesics. To attempt to address this
concern, measures of blood loss and menstrual pain were included as
cycle-specific markers of a robust/painful menses, a proxy for this
healthy cycle characteristic which may be associated with both subsequent analgesic use and subsequent ovulation. Interestingly, the
primary findings of a protective effect on ovulation and differences in hormones remained, even after adjusting for these potential confounders.
While we were able to account for these healthy cycle indicators, we
cannot rule out the possibility of residual confounding.
Strengths of this study include the comprehensive observation and
monitoring of a large number of women through two menstrual cycles.
No previous study has assessed daily analgesic use and multiple longitudinal measures of reproductive hormone levels and ovulatory status
throughout more than one menstrual cycle. Clinic visits timed with fertility monitors provided significant improvement in individualized monitoring of cycles. By assessing multiple participant characteristics in a
Matyas et al.
standardized manner, the ability to adjust for confounding in the study
was increased. Self-report daily diaries are not validated measures of
medication usage, which could lead to some classification error of medication use status and level of active ingredient consumption. Most of the
analgesic use in the BioCycle Study occurred in the early follicular phase,
thereby limiting our ability to assess whether the effects of analgesic use
varied depending on its timing throughout the cycle (e.g. effects of periovulatory or chronic use). We were also limited in our evaluation of
aspirin and naproxen, which were used by few women. Though there
were limited reports of naproxen use, our results suggest that there
may be differences in the hormonal effects of naproxen, and when we
excluded naproxen users from the analysis of any analgesic use we
observed a significant increase in periovulatory LH concentrations.
Though the mechanisms of action are very similar between naproxen
and ibuprofen (both are non-selective COX inhibitors), naproxen has
a much longer duration than ibuprofen (around 12 h compared with
4–6 h) which may partially explain some of these differences.
Overall, we observed that OTC analgesic use in healthy, premenopausal women is frequent and varies over the menstrual cycle with increased
use during menses. OTC analgesic use was significantly associated with
higher luteal levels of progesterone overall, and use of OTC analgesics
in the preceding 5 days was associated with reduced estradiol, and
increased FSH compared with women who did not use analgesics
during this time period. After adjusting for confounders, we also
observed a protective effect on ovulation with OTC analgesic use.
Overall, these findings indicate that typical use of analgesic medication
in normally cycling, premenopausal women is likely not harmful to reproduction function, and use of certain medications may even improve ovulatory function. However, specific mechanisms of these potentially
beneficial effects require further study.
Authors’ roles
R.A.M. analyzed data and wrote the manuscript; S.L.M. participated in
data analysis and interpretation, and helped draft and critically review
the manuscript; K.C.S. participated in data analysis and interpretation
and helped draft and critically review the manuscript; K.A.A. participated
in data analysis and interpretation and helped draft and critically review
the manuscript; L.A.S. participated in data analysis and interpretation
and helped draft and critically review the manuscript; N.J.P. participated
in data analysis and interpretation and critically reviewed the manuscript;
A.C.F. participated in data analysis and interpretation and critically
reviewed the manuscript; D.M. participated in interpretation of the
data and critically reviewed the manuscript; S.M.Z. participated in interpretation of the data and critically reviewed the manuscript; J.W.-W.
aided in design of study, directed site data collection, and critically
reviewed manuscript; E.F.S. designed study, directed data collection,
analysis and interpretation, and critically reviewed manuscript.
Funding
This work was supported by the Intramural Research Program of
the Eunice Kennedy Shriver National Institute of Child Health and
Human Development, National Institutes of Health (contract #
HHSN275200403394C).
Analgesic use, reproductive hormones and ovulation
Conflict of interest
None declared.
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