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C-reactive protein across the menstrual cycle.

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AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 136:138–146 (2008)
C-Reactive Protein Across the Menstrual Cycle
Katherine Wander,1* Eleanor Brindle,2 and Kathleen A. O’Connor1,2
1
2
Department of Anthropology, University of Washington, Seattle, WA 98195
Center for Studies in Demography and Ecology, University of Washington, Seattle, WA 98195
KEY WORDS
inflammation; estrogen; progesterone; biomarker
ABSTRACT
C-reactive protein (CRP) is a widely
used, sensitive biomarker of inflammation. Studies conducted among users of exogenous hormones suggest
that estrogen increases CRP, whereas progesterone
decreases CRP. Examinations of CRP in normally cycling women suggest the opposite: CRP is negatively
associated with endogenous estrogen and positively
associated with endogenous progesterone. This work
evaluates the association between menstrual cyclerelated hormone changes and events (menstruation and
ovulation) and CRP. Eight female subjects gave urine
and blood samples from twelve days across the menstrual cycle, for a total of eleven cycles. Blood samples
were assayed for CRP; urine samples for b-follicle stimulating hormone (bFSH), pregnanediol 3-glucuronide
(PDG), and estrone glucuronide (E1G). Ovulation day
was estimated using hormone levels. Presence or absence of menses was reported by subjects. Analyses
were conducted with random-effects linear regression.
All cycles were ovulatory; day of ovulation was identified for nine cycles. A ten-fold increase in progesterone
was associated with a 23% increase in CRP (P 5 0.01),
a ten-fold increase in estrogen was associated with a
29% decrease in CRP (P 5 0.05), and menses was associated with a 17% increase in CRP (P 5 0.18); no association between ovulation or FSH and CRP was found.
Hormone changes across the menstrual cycle should be
controlled for in future studies of inflammation in
reproductive-age women. Am J Phys Anthropol 136:
138–146, 2008. V 2008 Wiley-Liss, Inc.
C-reactive protein (CRP) and other indicators of inflammation are widely used markers of health because they
are both indicative of current infectious disease status
and predictive of many chronic diseases. Inflammation is
associated with a variety of chronic degenerative diseases, notably cardiovascular disease (CVD) (Danesh
et al., 2000; Pearson et al., 2003) and adult-onset diabetes (Pradhan et al., 2001). Evidence is strong that the
relationship between inflammation and chronic disease
is not secondary to some other causal relationship, but
that inflammation directly contributes to the damage responsible for these diseases (Ross, 1999; Libby et al.,
2002; Dandona et al., 2004); however, the possibility
remains that elevations in CRP result from, rather than
contribute to, chronic disease (see, for example, Woodward
et al., 2003).
C-reactive protein is part of the acute phase immune
response. Upon injury or infection, CRP levels can
increase up to 10,000-fold (Pepys and Hirschfeld, 2003).
This increase is rapid, but not immediate: circulating
concentrations reach 5 mg/L by about 6 h and peak by
about 48 h; the half-life of CRP in the blood is around
19 h (Vigushin et al., 1993). Because production of CRP
responds predictably to insult, quickly returns to normal following insult, and clearance from the blood happens at a constant rate, CRP levels provide a reliable
index of ongoing inflammation (Vigushin et al., 1993;
Pepys and Hirschfeld, 2003). Dramatic elevations in
CRP concentration indicate ongoing infection; absent
infection, CRP reflects the degree of background inflammation characteristic of an individual (Pepys and
Hirschfeld, 2003).
The utility of CRP for anthropological, epidemiological,
and nutritional research is quite broad. Using CRP, myriad studies have demonstrated a relationship between
inflammation and degenerative disease in Western popu-
lations (i.e., Danesh et al., 2000; Pradhan et al., 2001).
This relationship has recently been investigated among a
traditionally nonagricultural population as well: among
the Yakut, Sorensen et al. (2006) demonstrate low CRP
values (relative to Western populations), despite the
presence of substantial CVD mortality, suggesting that
subsistence activities may modify the relationship between inflammation and CVD.
Beyond investigations of degenerative disease, CRP is
used as a marker of acute infection. Identifying acute
infection is crucial to accurate characterization of health
and nutritional status (i.e., Beall et al., 2002; Shell-Duncan and McDade, 2004), as the acute phase response
substantially alters expression of many biomarkers. CRP
and other acute phase proteins have also been used to
assess environmental and infectious disease stresses
among non-Western children (Panter-Brick et al., 2001;
McDade et al., 2005). Here, we explore the effect of the
menstrual cycle on CRP, to inform future use of this biomarker and to shed light on interactions between human
reproduction and immune function.
C 2008
V
WILEY-LISS, INC.
C
Grant sponsors: National Science Foundation Graduate Research
Fellowship, National Institute of Child Health and Human Development; Grant number: R24D042828.
*Correspondence to: Katherine Wander, Department of Anthropology, Box 353100, University of Washington, Seattle, Washington
98195, USA. E-mail: kwander@u.washington.edu
Received 16 July 2007; accepted 29 November 2007
DOI 10.1002/ajpa.20785
Published online 6 February 2008 in Wiley InterScience
(www.interscience.wiley.com).
CRP ACROSS THE MENSTRUAL CYCLE
CORRELATES OF C-REACTIVE PROTEIN
Within individuals, CRP measures are moderately stable over time: when classified by CRP (i.e., the American
Heart Association’s suggestions for classifying CVD risk,
Pearson et al., 2003), about two thirds fall into the same
category when reclassified across the course of 1 year
(see Macy et al., 1997; Kluft and de Maat, 2001; Ockene
et al., 2001). In addition to injury and infection, strenuous exercise elevates individual CRP (Brull et al., 2003);
moderate alcohol consumption (Sierksma et al., 2002)
and weight loss (Tchernof et al., 2002) decrease CRP.
Statin therapy (Albert et al., 2001) and possibly aspirin
(Ikonomidis et al., 1999) reduce CRP; few other pharmaceuticals are known to alter CRP levels. There is no systematic diurnal (Meier-Ewert et al., 2001) or seasonal
(Fröhlich et al., 2002) variation in CRP.
Among individuals not experiencing infection, CRP
concentrations vary consistently with many attributes.
In adults, CRP is positively associated with age (Chenillot et al., 2000; Ford et al., 2003) and negatively associated with birth weight (Sattar et al., 2004). Adult
females have higher CRP than males (Wener et al.,
2000; Ford et al., 2003). Smokers have higher CRP than
nonsmokers (Chenillot et al., 2000). CRP is negatively
associated with measures of physical fitness (LaMonte
et al., 2002), and positively associated with BMI and adiposity (Visser et al., 1999; LaMonte et al., 2002; Tchernof
et al., 2002). Some component of variation in circulating
levels of CRP is heritable: estimates of heritability
between 30% (Austin et al., 2004) and 50% (MacGregor
et al., 2004) have been offered.
Oral contraceptive (OC) use (Dreon et al., 2002; Kluft
et al., 2002) and orally administered hormone replacement therapy (HRT) (Walsh et al., 2000; Kluft et al.,
2002) consistently elevate CRP, whether the preparation
includes progesterone and estrogen or estrogen alone.
Estrogen administration is also associated with increases
in CRP among men being treated for prostate cancer
(Kovacs et al., 2005). Such studies repeatedly demonstrate that exogenous estrogen can increase CRP. However, dose and delivery method appear to mediate exogenous estrogen’s effect on CRP: Prestwood et al. (2004)
demonstrate significant increases in CRP among women
taking a higher estrogen HRT preparation and significant decreases among women taking a lower estrogen
preparation; transdermally administered HRT has no demonstrable effect on CRP (Decensi et al., 2002; Ropponen et al., 2005).
Comparisons of different HRT preparations suggest
that exogenous progesterone decreases CRP (Cushman
et al., 1999; Skouby et al., 2002; but see Ridker et al.,
1999). A pro-inflammatory effect of exogenous progesterone administration has been reported in men (Zitzmann
et al., 2005). The use of progesterones of different types
complicates these studies, as progestational agents differ
in receptor affinity and glucocorticoid effects; the ‘‘pure
progestational’’ Nomegestrol acetate (which interacts
only with progesterone receptors and has no glucocorticoid effects) was found to be associated with decreases in
CRP (Gol et al., 2006).
The results of the few existing studies examining the
effects of endogenous hormones on CRP appear to be
inconsistent with the above trends. Jilma et al. (1997)
examined measures from 18 women taken in the follicular phase, at mid-cycle, and in the luteal phase of one
menstrual cycle, and found a significant (univariate)
139
association between individuals’ relative increase in progesterone and CRP from the follicular phase to mid-cycle
(r 5 0.60, P 5 0.01), as well as from the follicular phase
to the luteal phase (r 5 0.71, P 5 0.001). Blum et al.
(2005) examined 15 measures from 15 women across one
menstrual cycle. In random effects maximum likelihood
regression, increasing estrogen was associated with
decreasing CRP (b 5 20.23, P < 0.001; univariate) such
that a ten-fold increase in estrogen was associated with
a 41% decrease in CRP. The effect of progesterone on
CRP in this study was not significant (P 5 0.15).
Although subjects were sampled daily around ovulation,
a potential effect of ovulation per se on CRP concentration was not discussed. In this study, CRP concentrations were highest during menstruation; independent
effects of estrogen and menses were not tested.
Thus, the effects of endogenous and exogenous hormones on CRP appear to differ: oral exogenous estrogen
at most doses is associated with elevations in CRP
(Walsh et al., 2000; Dreon et al., 2002; Kluft et al., 2002;
Kovacs et al., 2005), endogenous estrogen seems to be
associated with decreases in CRP (Blum et al., 2005); exogenous progesterone may be associated with decreases
in CRP (Cushman et al., 1999; Skouby et al., 2002; Gol
et al., 2006), endogenous progesterone seems to be associated with increases in CRP (Jilma et al., 1997).
Neither study examining the effects of endogenous
hormones reports any evaluation of confounding between
the effects of estrogen and progesterone, despite the fact
that changes in these hormones regularly co-occur across
the menstrual cycle. Further, these studies do not evaluate potential independent effects of menses or ovulation,
despite good reason to expect these events to substantially affect inflammation. Ovulation and menstruation
have both been described as ‘‘inflammatory’’ events
(Espey, 1980; Kelly, 1994). Inflammatory mediators and
leukocytes play important roles in folliculogenesis and
follicular rupture (Bukulmez and Arici, 2000). Similarly,
prostaglandins and leukocytes play a direct role in
accomplishing menstruation (Critchley et al., 2001). As
such, an effect of these menstrual cycle events on circulating concentrations of CRP bears exploration. Further,
because elevations in follicle stimulating hormone (FSH)
are typically associated with both menstruation (Miro
and Aspinall, 2005) and ovulation (Schwartz, 1974), it is
possible that changes in this hormone may explain any
association between those cycle events and CRP.
Thus, this pilot study was undertaken to verify the
unexpected effects of endogenous reproductive steroids
on CRP, to evaluate potential effects of menses, ovulation, and FSH on CRP, and to evaluate potential confounding and interaction in these relationships.
METHODS
Participants and specimen collection
Participants were eight non-obese normally cycling
females between 21 and 47 years of age without any
reported history of diabetes or cardiovascular disease;
none used exogenous hormones, statins, or more than
occasional anti-inflammatory medication. Paired urine
and capillary dried blood spot (DBS) collections were
made from three volunteers for two months (24 collections per subject), and from five volunteers for one
month (12 collections per subject). Collections were made
every Monday, Wednesday, and Friday during the collecAmerican Journal of Physical Anthropology
140
K. WANDER ET AL.
tion period. Procedures were approved by the Institutional Review Board of the University of Washington.
Although venous blood is more commonly used in
examinations of CRP as a biomarker, DBS were used in
this study for two reasons: this collection method is minimally invasive and was believed to be more conducive to
collection of serial samples; further, DBS are more likely
to be used in examinations of CRP in populations of interest to anthropologists (McDade et al., 2004). Blood
spots were collected by finger stick with a sterile lancet.
Spots of blood were allowed to fall freely from the subject’s finger onto filter paper (Schleicher and Schuell
#903 Specimen Collection Paper). Filter papers were
allowed to dry at room temperature in open air for one
to three days, and were then frozen with desiccant until
assay.
Within 1 h of DBS collection, subjects made urine
collections; urine was collected and refrigerated in prelabeled plastic vials. Most subjects made urine collections in the laboratory; other subjects collected and refrigerated urine samples in their homes for later transport
to the laboratory. In the laboratory, urine-specific gravity
measures were taken and a 600 lL aliquot was made
and refrigerated until assay; the remaining sample was
frozen. All initial assays were completed within two
weeks of the completion of subjects’ sample collection
periods; later reanalysis of some samples was conducted
using stored sample.
To address the possibility that DBS finger stick collection procedures were invasive enough to evoke some
measure of inflammatory response, subjects’ start days
were assigned irrespective of cycle day, so that no collection day systematically co-occurred with any cycle day.
Further, collections were not made over the weekend. If
collection procedures induced any increase in CRP, the
one-day interval preceding Wednesday and Friday collections would allow for more limited clearance than the
two-day interval preceding Monday collections, resulting
in regularly lower CRP values on Mondays. Thus, a test
for systematically lower CRP on Mondays could be
employed to evaluate a potential effect of finger-stick
procedures on CRP.
Assays
Dried blood spots were assayed for CRP with a highsensitivity sandwich enzyme immunoassay using polyclonal antibodies, described in detail elsewhere (McDade
et al., 2004). Microtiter plates were coated with antiCRP antibody (DAKO, cat. no. A0073, recently discontinued). Calibrators (Fitzgerald Industries International,
Inc., cat. no. 30-AC10; combined with washed erythrocytes and preserved on filter paper), in house controls,
and specimens were eluted in buffer from filter paper.
Horseradish peroxidase-labeled detection antibody
(DAKO, cat. no. P0227, recently discontinued) and substrate solution were used to develop a color signal proportional to CRP concentration. DBS measures of CRP
using this assay correlate highly with serum measures
of CRP (r 5 0.96 for 84 paired DBS and serum specimens; McDade et al., 2004). For the eight plates run for
this project, the inter-assay coefficient of variation (CV)
for in-house controls was 2.9% at high (3.0 mg/L) and
15.8% at low (0.11 mg/L) concentration.
Urine samples were assayed for estrone glucuronide
(E1G) and pregnanediol 3-glucuronide (PDG), the major
urinary metabolites of estradiol (E2) and progesterone
American Journal of Physical Anthropology
(P4), respectively. Competitive enzyme immunoassays
(EIAs) used to quantify urinary levels of E1G and PDG
are described in detail elsewhere (O’Connor et al., 2003,
2004). Plates were coated monoclonal capture antibodies (Quidel Corporation, clone 330 for PDG; Weizmann
Institute, clone 3F11 for E1G). Urine specimens, calibrators (Sigma, cat. no. P3635 for PDG; Sigma, cat. no.
E1752 for E1G), in-house controls, and PDG or E1G
conjugated to horseradish peroxidase were added to the
wells. Substrate was added to develop a color signal
inversely proportional to steroid conjugate concentration. Urine samples were assayed for FSH using a sandwich immunoenzymometric assay specific to total (free
and intact) bFSH (Brindle et al., 2006). Microtiter
plates were coated with a monoclonal capture antibody
specific to bFSH (Scantibodies Laboratory, Inc., clone
FS2.4A10.G10). Urine samples, in-house controls, and
calibrators (bFSH, A. F. Parlow, AFP2911A, NIDDK
NHPP) were added to the wells, followed by polyclonal
anti-bFSH detection antibody (A. F. Parlow, NIDDKanti-hBetaFSH-1, NHPP). Addition of horseradish peroxidase-labeled antibody and substrate solution yielded
a color signal proportional to FSH concentration. Urinary PDG, E1G, and bFSH measured using these
assays parallel serum progesterone, estradiol, and
intact FSH, respectively, across the menstrual cycle:
Pearson correlations between paired urine and serum
specimens from 30 averaged cycles (n 5 34 cycle days)
were 0.98 for PDG-progesterone (O’Connor et al., 2003),
0.94 for E1G-estradiol (O’Connor et al., 2004), and 0.86
for bFSH-serum intact FSH (Brindle et al., 2006). For
quality control, assay runs were repeated if the estimated value of both high and low controls fell outside
the controls’ established 95% confidence interval; control CI’s are calculated from 47 and 48 plates for E1G,
38 and 39 plates for PDG, and 3,352 plates for FSH.
All specimens, calibrators and controls were run in
duplicate; concentrations were estimated from optical
density using a four-parameter logistic model in BIOLINX 2.0 Software (Dynatech Laboratories, Chantilly,
VA). Urinary hormone values were adjusted for variation
in hydration status by specific gravity (Miller et al.,
2004); E1G results were statistically corrected for slight
assay nonparallelism (O’Connor et al., 2004).
Identification of ovulation
Cycles were identified as ovulatory or anovulatory
and day of ovulation was identified using methods
developed for intermittent sampling (O’Connor et al.,
2006), which rely on changes in urinary measures of steroid hormones. A cycle was identified as ovulatory if it
evinced a relative rise in progesterone (as reflected by
the running average PDG for each collection) that was
sustained for at least two consecutive collections. Ovulation day was identified as the second day of a five-day
dramatic decline in the ratio of estrogen to progesterone. When adapted for intermittent sampling, this
method (modified Baird) identified ovulation day to
within two days of the true ovulation day for 93% of
cycles. When a noncollection day was identified as the
day of ovulation, the following collection was defined as
ovulation day for these analyses. Sampling in this study
provided slightly fewer samples than that used to develop these methods, so the decision rule was relaxed
regarding the change in the ratio of steroid hormones
necessary to identify ovulation. The protocol calls for a
141
CRP ACROSS THE MENSTRUAL CYCLE
Fig. 1. Variation in hormones and CRP across the menstrual cycle: The nine cycles for which ovulation day could be estimated
were centered on the estimated ovulation collection, and the mean value was calculated for the ovulation collection, the preceding
six collections, and the five following collections. Between six and nine values are included in the mean for each collection. Cycles
were not centered on menstruation; however, menses was reported by most participants at the sixth or fifth collection preceding
estimated ovulation.
decrease of 60% across a five-day window. Since cycles
clearly identified as ovulatory did not consistently demonstrate this magnitude decrease, but did demonstrate
a 50% decrease, a 50% decrease was accepted in identifying ovulation day in these analyses.
Statistical analysis
Data were analyzed using StataSE 9.0 (Statacorp, College Station, TX). Statistical significance was defined as
P 0.05; suggestive results were defined as 0.05 < P 0.20; confounding was defined as a change >10% in the
coefficient of the predictor of interest with the inclusion
of a potential confounder. Relationships between CRP
and other variables were analyzed using random-effects
linear regression, wherein a random intercept term was
estimated for each participant to account for differences
among individuals.
To assess the effect of infection-related changes in
CRP, the magnitude of which could obscure meaningful
changes in background CRP, and which could introduce
potential confounding, analyses were carried out both
with and without data points for which CRP was elevated. Choice of such cut-off varies depending on the
fluid analyzed and the type and severity of infection
identified. Here, we employ the cut-off CRP > 5 mg/L
(238 mmol/L).
Because previous work (Prestwood et al., 2004) suggests opposite effects of low and high dose estrogen on
CRP, we chose to test the hypothesis that the effects of
estrogen on CRP may change at high concentrations. To
do this, analyses were carried out both with and without
points above the 90th percentile E1G.
RESULTS
Data from eleven menstrual cycles of eight female subjects were used to assess menstrual cycle-related
changes in CRP. Values for E1G and PDG were available
for a total of 126 data points; values for FSH were available for 124 data points. The presence or absence of
menses was reported by participants for 126 data points;
menses was reported as present for 37 data points. All
cycles were ovulatory; day of ovulation was identifiable
for nine cycles. Variation in hormones and CRP across
these cycles (centered on ovulation) is presented in Figure 1. CRP, PDG, E1G, and FSH showed skewed distributions and were therefore transformed using a natural
logarithm; within individuals, transformed CRP values
appear normally distributed. No subjects reported using
more than occasional anti-inflammatory medication. The
majority of anti-inflammatory medications reported were
ibuprofen and acetaminophen. On one occasion, one subject reported using aspirin; the associated collection was
dropped from all analyses.
Collection procedures did not artificially elevate CRP:
Monday collections (including all collections preceded by
two or more noncollection days) were tested for systematically lower CRP concentrations within individuals; no
effect was found (b 5 0.085, P 5 0.26).
Confounding was apparent between all predictors of
interest for which there was a suggestive univariate
effect (Table 1); only one model was ultimately used for
American Journal of Physical Anthropology
142
K. WANDER ET AL.
TABLE 1. Evaluation of confounding
Univariate associations with C-reactive protein, ln(CRP)
[Coefficient (P-value)]
Progesterone,
ln(PDG)
0.030 (0.33)
Estrogen,
ln(E1G)
20.093 (0.16)
Menses
0.179 (0.10)
Adjusted for potential confounders (bivariate)
ln(PDG)
ln(E1G)
Menses
ln(FSH)
Ovulation
–
0.088 (0.01)
0.052 (0.07)
0.027 (0.40)
0.032 (0.29)
20.191 (0.01)
–
20.045 (0.49)
20.096 (0.15)
20.080 (0.24)
0.214 (0.06)
0.154 (0.18)
–
0.169 (0.14)
0.169 (0.13)
Results of random-effects linear regression show substantial
confounding between these three predictors of interest. CRP, Creactive protein; PDG, pregnanediol 3 glucuronide, reflects serum progesterone levels; E1G, estrone glucuronide, reflects serum estradiol levels. The top row lists the coefficients and P-values for univariate associations between a variable and CRP, the
lower rows show how those coefficients and P-values change
with the inclusion of each potential confounder (listed to the
left). Thus, for example, the coefficient for estrogen increases in
magnitude, from 20.093 to 20.191, and significance, from P 5
0.16 to P 5 0.01, with the inclusion of progesterone in the
model.
all predictors (Table 2). All possible two-way interaction
terms from this model were tested individually. None
were found to be significant; however, the interaction
term coefficients for ln(PDG)*ln(E1G) (P 5 0.06) and
ln(E1G)*menses (P 5 0.18) were suggestive. Thus,
future studies with larger sample sizes should assess
interaction between the effects of progesterone and
estrogen and the effects of estrogen and menses on CRP.
According to the model presented in Table 2, controlling for E1G and menses, a ten-fold increase (typical for
steroids across the cycles we observed) in PDG is associated with a 23% increase in CRP (P 5 0.01). Controlling
for PDG and menses, a ten-fold increase in E1G is associated with a 29% decrease in CRP (P 5 0.05). Controlling for these hormones, a suggestive increase of 17% in
CRP (P 5 0.18) is associated with the presence of menses. R-squared values for this model are low, indicating
that a small proportion of the variation among normally
cycling women in CRP is explained by cycle-related variables; however, a significant proportion of the variation
in CRP within a normally cycling woman is explained by
these variables.
The effects of steroid hormones on CRP were assessed
within each cycle phase through two separate regression
models; menses was not included in either of these models (Table 3). Within the follicular phase, the effect of
estrogen is of greater magnitude and significance than
across the entire cycle, despite a reduction in sample
size, while the effect of progesterone is not apparent.
Within the luteal phase, the effect of progesterone is
apparent and significant, despite the reduced sample
size; the effect of estrogen is of lesser magnitude and significance than observed in either the follicular phase or
the entire cycle.
No association between FSH and CRP, nor any confounding between FSH and other predictors, was found.
Thus, FSH was disregarded in further analyses. The
expected increase in FSH with menses (b 5 0.311, P 5
0.01) and ovulation (b 5 0.679, P 5 0.00) was evident.
No association between ovulation and CRP (b 5
20.055, P 5 0.60 when included in the model in Table 2),
nor any confounding between ovulation and the effect
American Journal of Physical Anthropology
of steroids on CRP, was found. Thus, ovulation was disregarded in further analyses.
Four CRP values exceeded 5 mg/L (238 mmol/L); their
exclusion from the final regression model does not substantially alter the magnitude of any coefficients; the
coefficient for E1G, however, loses significance without
these data points (Table 4).
When the highest E1G values are excluded from the
analysis, the negative relationship between E1G and
CRP becomes stronger, despite the reduction in sample
size. The results of the regression model excluding points
with E1G above the 90th percentile [E1G 101,675.0
pg/mL (47.63 mmol/L), 13 values] are shown in Table 5.
Compared with the results in Table 2, coefficients for
PDG and menses decrease in magnitude and significance, concomitant with a reduction in sample size; the
E1G coefficient increases in magnitude and significance,
such that a ten-fold increase in E1G is associated with a
43% decrease in CRP (P 5 0.02).
DISCUSSION
Within individual women, CRP concentrations are
clearly associated with endogenous reproductive hormones: increases in estrogen are associated with
decreases in CRP (P 5 0.05), while increases in progesterone are associated with increases in CRP (P 5 0.01).
Independent of hormone changes, the presence of menses is associated with nonsignificant increases in CRP (P
5 0.18). Substantial confounding is apparent between
these effects; this may explain the lower magnitude of
the effects reported here, compared to previous studies
(Jilma et al., 1997; Blum et al., 2005). The negative association between estrogen and CRP is stronger in the follicular phase, while the positive association between progesterone and CRP is limited to the luteal phase.
Estrogen exhibits opposing effects on a number of
physiological variables at low and high levels (Calabrese,
2001). Evidence suggests that this pattern holds for
inflammation as well: increases in exogenous estrogen
are negatively associated in vivo with circulating CRP
(Prestwood et al., 2004) and in vitro with whole blood
interleukin (IL)-1b and IL-6 production (Rogers and
Eastell, 2001) at lower estrogen doses, and positively
associated at higher estrogen doses. When we excluded
the highest 10% of estrogen values (those that might be
proinflammatory), the negative effect of estrogen was
strengthened. This supports the hypothesis that estrogen
has opposite effects on inflammation at high and low
levels.
Our finding of a negative relationship between CRP
and endogenous estrogen at most levels is consistent
with the results reported by Blum et al. (2005); these
results are in contrast to the findings of studies focusing
on exogenous estrogen (Dreon et al., 2002; Kluft et al.,
2002). The opposite effects of exogenous and endogenous
estrogen may result in part from changes in the effect of
estrogen at high and low doses (described above). They
may also be the result of first-pass effects of oral estrogen on hepatic CRP production (Decensi et al., 2002).
Our results indicate that increases in estrogen at levels found across most of the menstrual cycle are associated with decreases in CRP; and that changes in the
relationship between estrogen and inflammation occur
across normal physiological levels of estrogen. An antiinflammatory effect of increasing estrogen concentrations
at most physiological levels is consistent with estrogen’s
143
CRP ACROSS THE MENSTRUAL CYCLE
TABLE 2. Relationship between CRP and menstrual cycle variables
Outcome variable: ln(CRP)
Predictor variable
Progesterone, ln(PDG)
Estrogen, ln(E1G)
Menses
Coefficient
0.090
20.148
0.156
P-value
0.01
0.05
0.18
95% CI
0.023, 0.157
20.297, 0.000
20.074, 0.387
Interpretation
100.090 5 1.23
1020.148 5 0.711
e0.156 5 1.17
R2 within 5 0.0754, R2 between 5 0.0005, R2 overall 5 0.0053.
TABLE 3. Relationship between CRP and steroid hormones by
cycle phase
Outcome variable: ln(CRP)
Predictor variable
Follicular phase
Progesterone, ln(PDG)
Estrogen, ln(E1G)
Luteal phase
Progesterone, ln(PDG)
Estrogen, ln(E1G)
Coefficient
P-value
Interpretation
0.021
20.285
0.84
0.01
100.021 5 1.05
1020.285 5 0.519
0.05
0.32
5 1.30
10
1020.106 5 0.783
0.114
20.106
Outcome variable: ln(CRP)
Coefficient
0.081
20.107
0.182
Outcome variable: ln(CRP)
Predictor variable
Progesterone, ln(PDG)
Estrogen, ln(E1G)
Menses
Coefficient
0.088
20.242
0.099
P-value
0.04
0.02
0.44
Interpretation
100.088 5 1.22
1020.242 5 0.573
e0.099 5 1.10
R2 within 5 0.0793, R2 between 5 0.0095, R2 overall 5 0.0154.
0.114
TABLE 4. Effect of excluding high CRP values
Predictor variable
Progesterone, ln(PDG)
Estrogen, ln(E1G)
Menses
TABLE 5. Effect of excluding high estrogen values
P-value
Interpretation
0.01
100.081 5 1.21
0.12
1020.107 5 0.782
0.11
e0.182 5 1.20
R2 within 5 0.0867, R2 between 5 0.0368, R2 overall 5 0.0001.
effect on inflammation in many tissue types: estrogen
has a consistently negative effect on inflammatory cell
migration and inflammatory marker production in
diverse nonreproductive, nonimmune tissues (Girasole
et al., 1992; Ashcroft et al., 1999; Miyamoto et al., 1999;
Vegeto et al., 2001). It is possible that increases in
inflammation with the highest levels of estrogen indicate
that estrogen plays a role in accomplishing increases in
inflammation observed across pregnancy (Watts et al.,
1991; Sacks et al., 2004).
Our finding of a positive effect of progesterone on CRP
is consistent with the relationship reported by Jilma
et al. (1997). It is in contrast to the negative effect suggested by studies of exogenous hormone administration;
however, these studies are themselves quite inconsistent
(for discussions, see Ropponen et al., 2005; Gol et al.,
2006). Our finding is difficult to reconcile with many
observations of progesterone’s effects on the immune system: while progesterone seems to promote the chemotactic activity of neutrophils and increase production of
some inflammatory mediators (IL-6 and leukemia inhibiting factor, LIF) by monocytes (Bouman et al., 2005), an
overall anti-inflammatory effect is generally attributed
to progesterone, which includes decreasing NK cell activity and macrophage tumor necrosis factor (TNF) a and
nitric oxide synthase (NOS) production, as well as inhibiting T-cell development in the thymus and T-cell activity
(for recent review, see Roberts et al., 2001). Our results
suggest that increases in progesterone typical of normally cycling women have an overall inflammatory
effect. Future work should focus on reconciling the
observed effects of progesterone on inflammatory cells
with its overall effect on systemic inflammation.
We report that menstruation is associated with a suggestive increase in CRP; this is consistent with Blum
et al. (2005) observation of highest CRP concentrations
during menstruation. Because of its small sample size,
our analysis lacks the power to reject alternative hypotheses at the 0.05 a-level for the observed magnitude of
the effect of menstruation; thus we interpret this result
as a suggestive pro-inflammatory effect of menstruation.
This uncontrolled effect is clear in our data (see Fig. 1
and Table 1), and the suggestive effect persists after controlling for the confounding effects of hormones. Studies
with larger samples should anticipate a proinflammatory
effect of menses, consistent with the innate immune system’s role in accomplishing menstruation (Critchley
et al., 2001).
Despite the involvement of inflammatory cells and
mediators in follicular rupture, ovulation does not
appear to affect systemic inflammation. The possibility
exists that our methods of identifying ovulation, sampling frequency, or power were inadequate to detect such
an effect, but the small magnitude and high P-value of
the coefficient that results from ovulation’s inclusion in
our model suggest that this is unlikely to be the case.
CONCLUSIONS AND IMPLICATIONS
Changes in estrogen and progesterone concentration
are significantly and independently associated with
changes in CRP concentration, suggesting that progesterone increases CRP, estrogen decreases CRP, and menstruation may increase CRP. Overall, these changes are
consistent with observed interactions between the reproductive and immune systems. The patterns we observed
are not consistent with reported effects of exogenous hormones on CRP; our results agree with others examining
endogenous reproductive steroids and CRP (Jilma et al.,
1997; Blum et al., 2005), and our analysis expands on
them, providing a single model of the independent associations between endogenous hormones and menstrual
cycle events and CRP.
C-reactive protein’s utility to human biology research
is only beginning to be realized. In addition to nutritional anthropology and infectious disease research,
investigations of human senescence and life history evolution will benefit from using CRP to explore variation
in background inflammation, its determinants, its effect
on life expectancy, and its contribution to post-reproductive mortality. As investigations of inflammation across
American Journal of Physical Anthropology
144
K. WANDER ET AL.
the lifecourse are undertaken, it will be important to
evaluate and control for potential confounding by reproductive variables. We suggest that studies using CRP as
a biomarker among reproductive-age women account for
cycle-related hormonal changes and menstruation in
sampling and analysis; further, when study samples
include individuals who may be undergoing any transition in their reproductive life course (adolescents, mothers returning to menses postpartum, or perimenopausal
women), differences in hormone levels or frequency of
menses may confound estimated effects of other variables on CRP.
For example, Mexican-American children included in
NHANES III have higher CRP than white children (controlling for age and BMI), especially among girls (3–17
years; Ford, 2003). This result may in part be attributable to earlier menarche among Mexican–American girls
(Wu et al., 2002), which would result in the inclusion of
more menses-associated collections for Mexican–American, compared to white, girls, elevating the estimated
mean CRP among Mexican–American girls. Although it
is unlikely that earlier menarche could entirely account
for the observed pattern, we nonetheless suggest controlling for the presence of menses in future examinations of
CRP among this age group.
Similarly, variation in CRP across adult populations
may be attributable in part to variation in steroid hormone levels. Forouhi et al. (2001) report significantly
higher CRP among South Asian women living in London, compared to white women of the same BMI category. Significant differences between these populations
in both progesterone and estrogen levels (O’Connor
et al., 2003), which persist beyond migration (Núñez-de
la Mora et al., 2007), may contribute to these differences
in CRP. Thus, we suggest controlling for steroid hormones levels in future comparisons of CRP and inflammation across populations.
ACKNOWLEDGMENTS
The authors thank Jennifer Aranda, Masako Fujita,
Steven Goodreau, Anita Rocha, Bettina Shell-Duncan,
Jane Shofer, and anonymous reviewers for their help in
the preparation of this manuscript.
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