Adiposity and the Development of Premenstrual Syndrome
Elizabeth R. Bertone-Johnson, Sc.D.,1Susan E. Hankinson, Sc.D.,2,3Walter C. Willett, M.D., Dr.P.H.,2,3,4
Susan R. Johnson, M.D.,5and JoAnn E. Manson, M.D., Dr.P.H.2,3,6
Background: Moderate to severe premenstrual syndrome (PMS) affects 8%–20% of premenopausal women and
causes substantial levels of impairment, but few modifiable risk factors for PMS have been identified. Adiposity
may impact risk through the complex interaction of hormonal and neurochemical factors, but it is not known if
adiposity increases a woman’s risk of developing PMS. We have addressed these issues in a prospective study
nested within the Nurses’ Health Study 2.
Methods: Participants were a subset of women aged 27–44 and free from PMS at baseline, including 1057 women
who developed PMS over 10 years of follow-up and 1968 controls. Body mass index (BMI), weight change and
weight cycling were assessed biennially via questionnaire.
Results: We observed a strong linear relationship between BMI at baseline and risk of incident PMS, with each
1kg=m2increase in BMI associated with a significant 3% increase in PMS risk (95% confidence interval [CI] 1.01-
1.05). After adjustment for age, smoking, physical activity, and other factors, women with BMI ?27.5kg=m2at
baseline had significantly higher risks of PMS than women with BMI <20kg=m2(ptrend¼0.003). A large weight
change between age 18 and the year 1991 was significantly associated with PMS risk, whereas weight cycling
during this period was not. BMI was positively associated with specific symptoms, including swelling of
extremities, backache, and abdominal cramping (all p<0.001).
Conclusions: Our findings suggest that maintaining a healthy body mass may be important for preventing the
development of PMS. Additional studies are needed to assess whether losing weight would benefit overweight
and obese women who currently experience PMS.
is characterized by physical, emotional, behavioral, and cog-
nitive symptoms occurring in the luteal phase of the men-
strual cycle that cause substantial levels of impairment and
interfere with interpersonal relationships and life activities.1,4
Because the efficacy of common pharmaceutical treatments
remains relatively low (i.e., <60%),5identifying ways to pre-
vent the initial development of PMS is important. However,
few population-based studies have identified modifiable
factors that may be etiologically related to PMS.
Adiposity may plausibly be related to PMS through a va-
riety of hormonal, neural, and behavioral mechanism, and
oderate to severe premenstrual syndrome (PMS)
affects 8%–20% of reproductive-aged women.1–3PMS
several studies have found women with PMS or menstrual
symptoms more likely to be overweight and obese than wo-
men without PMS.3,6–10To our knowledge, however, this re-
lationship has not been assessed in prospective studies, and it
is not known if adiposity contributes to the initial develop-
ment of PMS. Furthermore, it is unclear whether a large
weight gain within a short period of time or the repeated
gaining and losing of weight, termed ‘‘weight cycling,’’ is
associated with PMS risk independent of overall level of
We assessed how adiposity, fat distribution, and weight
change were associated with the development of PMS in a
prospective study nested within the Nurses’ Health Study 2
(NHS2). In addition, we examined whether adiposity may
affect the development of specific menstrual symptoms.
1Department of Public Health, University of Massachusetts, Amherst, Massachusetts.
2Channing Laboratory, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts.
3Departments of Epidemiology and4Nutrition, Harvard School of Public Health, Boston, Massachusetts.
5Department of Obstetrics and Gynecology, University of Iowa, Iowa City, lowa.
6Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts.
JOURNAL OF WOMEN’S HEALTH
Volume 19, Number 11, 2010
ª Mary Ann Liebert, Inc.
Materials and Methods
The NHS2 is a prospective epidemiological study of 116,678
registered nurses aged 25–42 from 11U.S. states who re-
sponded to a mailed questionnaire in 1989. Participants pro-
vided information on their medical history and health-related
behaviors, such as smoking and oral contraceptive (OC) use.
Participants have completed questionnaires every 2 years to
update information on health factors and identify new disease
diagnoses. The response rate for each questionnaire cycle has
been ?89%. The study protocol was approved by the Institu-
tional Review Board at Brigham and Women’s Hospital.
The NHS2 Premenstrual Syndrome Substudy
Our procedure for identifying PMS cases and controls has
been described previously.11,12Briefly, in 1989, participants
were asked if they had ever received a physician diagnosis of
PMS. On subsequent questionnaires in 1993, 1995, 1997, and
2001, participants were asked if they had received a new di-
agnosis of PMS during the previous 2–4-year period and to
indicate the timing of the diagnosis.
In January 2002, we conducted a substudy among NHS2
participants to identify PMS cases and controls. First, we
identified all cohort members who had not reported a diag-
nosis by 1991 and who, therefore, could possibly report a new
diagnosis of PMS during our follow-up period (i.e., 1991–
2001). To make sure that cases and controls provided infor-
mation about eligibility, adiposity, and other factors during
similar time periods, we assigned each woman a reference
year. For women who reported a new diagnosis of PMS
during the follow-up period, their reference year was equal
to their year of diagnosis. Because control women did not
develop PMS and thus did not have a year of diagnosis, we
assigned each control a randomly chosen reference year
between 1993 and 2001.
We used each woman’s reference year to determine her el-
igibility for the substudy, to evaluate menstrual symptom ex-
perience, and to assess aspects of adiposity. To reduce the
attributed to causes other than PMS, we excluded women who
ular menstrual cycles, infertility, hysterectomy, or menopause
before their reference year. From among all remaining eligible
women, we selected 6000 to participate in the PMS substudy,
including 3430 women who reported a new diagnosis of PMS
between 1993 and 2001 and 2570 who did not report PMS
during this period. For case selection, we gave preference to
women with the most recent reference years; noncases were
then frequency-matched to cases by reference year.
We mailed all 6000 participants a two-page questionnaire
based on the Calendar of Premenstrual Experiences designed
by Mortola et al.13Women were asked to report whether in
the specific 2-year period before their reference year, they had
experienced any of 26 different symptoms ‘‘most months of
the year for at least several days each month before [their]
menstrual period begins.’’ We also asked about the age when
symptoms first occurred, the timing of symptom onset and
cessation during an average menstrual cycle, symptom se-
verity, and the interference of symptoms with life activities
and interpersonal relationships. Completed questionnaires
were received from 2966 (86.5%) women self-reporting and
2504 (97.4%) women not reporting PMS.
We used information provided on the supplemental ques-
tionnaire to identify from among those self-reporting PMS the
women who met our case definition, based on criteria estab-
lished by Mortola et al.13We defined cases as women who
reported a new diagnosis of PMS during the follow-up period
(1991–2001) and who also reported (1) the occurrence of at
least one physical and one affective menstrual symptom, (2)
overall menstrual symptom severity classified as moderate or
severe or effect of symptoms on life activities and social re-
lationships classified as moderate or severe, (3) symptoms
beginning within 14 days of the onset of menses, (4) symp-
toms ending within 4 days after the onset of menses, and (5)
(35.6%) of the 2966 women self-reporting PMS met these crite-
ria and were included as validated PMS cases in our analysis.
We then identified a comparison group from among par-
ticipants whoboth didnotreportadiagnosisofPMS beforeor
during the follow-up period (through 2001) and experienced
either no menstrual symptoms or only mild symptoms that
had no substantial effect on life activities and relationships.
A total of 1968 of the 2504 noncases (78.6%) met these criteria
and were included in subsequent analyses as validated con-
trols. Women who did not meet criteria for either cases or
controls were excluded from analysis.
The validity of our approach to identifying PMS cases and
controls was assessed previously.14Briefly, participants in-
cluded 135 members of the NHS2 PMS substudy who first
reported PMS by questionnaire in 2001 and 371 who never
reported PMS (1989–2001). We found that the menstrual
symptom occurrence, timing, and severity in women meeting
criteria based on those established by Mortola et al.13as as-
sessed by our retrospective questionnaire were essentially
identical to those in women who also reported clinician-
supervised prospective symptom charting as part of their
diagnosis. Validated cases experienced more affective and
physical symptoms than unvalidated cases, and the symp-
toms were of greater overall severity and personal impact.
Assessment of adiposity, fat distribution,
physical activity, and other factors
Current weight (lbs) was self-reported by participants on
each biennial questionnaire. Weight at age 18 and height
(inches) was measured in 1989. We calculated body mass in-
dex (BMI) as weight (kg)=height (m2). In 1993, we asked each
participant to record the circumference of her waist at the
navel and her hips at the widest part (including buttocks) to
the nearest ¼ inch with a tape measure. We divided waist
circumference by hip circumference to calculate waist=hip
ratio. We assessed the effect of weight change on risk of PMS
at various time periods. For example, we subtracted weight at
age 18 from weight at the start of follow-up (1991). We then
calculated weight change in the 2-year and 4-year periods
before the reference year.
In 1993, we asked women to report how many times they
had intentionally lost 5–9, 10–19, 20–49, and ?50lbs between
the ages of 18 and 30 and in the previous 4 years, independent
of illness and pregnancy. We defined mild to severe weight
cycling as losing ?10 pounds ?3 times between the specified
time periods, as described by Field et al.14
We collected information on other factors potentially as-
1956 BERTONE-JOHNSON ET AL.
in order to control for confounding. Information on age,
number of pregnancies lasting longer than 6 months, age at
first birth, tubal ligation, and OC use was updated biennially.
Age at menarche and menstrual cycle characteristics were
assessed in 1989. Total energy and macronutrient and mi-
cronutrient intake were measured by food frequency ques-
tionnaires (FFQ) in 1991, 1995, and 1999; nutrients were
adjusted for total energy intake by the residual method.15Our
menstrual symptom questionnaire asked about diagnoses of
depression, antidepressant use, and the timing of each.
Childhood and adolescent trauma related to punitive par-
enting was assessed by supplemental questionnaire and used
to create a childhood trauma score.16
Participation in physical activity was measured on ques-
tionnaires in 1991 and 1997. Women were asked how much
time they spent each week participating in specific recrea-
tional activities, including walking or hiking outdoors, jog-
ging, running, bicycling, calisthenics=aerobics, racket sports,
lap swimming, and other aerobic activities, such as lawn-
mowing. We used this information to calculate metabolic
equivalent task (MET)-hours per week.17These questions
have been validated for use in this population and are de-
scribed in detail elsewhere.18
All statistical analyses were conducted with SAS (SAS In-
stitute, Inc., Cary, NC). We compared age-standardized base-
line characteristics of PMS cases and controls with generalized
linear models (PROC GLM) adjusting for age. We used odds
ratios (OR) toestimate the relativerisk(RR) ofPMS for women
across categories of adiposity and fat distribution and calcu-
lated 95% confidence intervals (CI). In multivariable analyses,
we included factors in logistic regression models that were
confounders of the adiposity-PMS relationship, as well as fac-
tors associated with adiposity or PMS or both in previous
studies. These included age, diagnosis year, pack-years of
cigarette smoking, number of full-term pregnancies, tubal
ligation, duration of OC use, antidepressant use, history of
B6(see footnote to Table 2 for variable categories). All analyses
were also adjusted for MET-hours of physical activity, al-
though activity level did not vary significantly between cases
and controls. Several additional variables were not included in
the final analysis because they were unrelated to the develop-
ment of PMS or BMI, including age at first birth, physical
activity, BMI at age 18, and dietary intake of magnesium,
manganese, potassium, vitamin E, linolenic acid, total carot-
enoids, and caffeine. The Mantel-extension test for trend was
used to evaluate linear trend across categories by modeling
the median value of each category as a continuous variable in
the multivariable regression models. Analyses of waist cir-
cumference, waist=hip ratio, and weight cycling were limited
to cases and controls with reference years after 1993, the year
when these exposure variables were assessed. In addition, our
analysis of BMI at age 18 excludes women with BMI <17
kg=m2to minimize the likelihood of including women with
Finally, we assessed the relationship between BMI in 1991
For each symptom, we assessed whether BMI was associ-
ated with symptom risk, comparing cases reporting the
symptom with controls not reporting the symptom; cases
not experiencing a specific symptom and controls reporting
the symptom were excluded from that analysis. Because of
the large number of comparisons in this analysis, we con-
sidered p values <0.01 instead of p<0.05 to be statistically
Baseline characteristics of cases and controls are presented
in Table 1.Cases were significantly younger than controls and
were more likely to be smokers and ever users of OC. Cases
Table 1. Age-Standardized Characteristics of Premenstrual Syndrome Cases and Controls
at Baseline, Nurses’ Health Study 2 PMS Substudy, 1991–2001
PMS cases (n¼1057)
Age at menarche
Number of full-term pregnancies
Total calorie intake (kcal=day)
Vitamin D intake (IU=day)b
Vitamin B6intake (mg=day)b
Alcohol intake (g=day)
Pack-years of cigarette smoking
MET-hours=week of physical activity
Ever use of oral contraceptive
Current oral contraceptive use
Previous use of antidepressant medications
History of significant childhood trauma
aAll characteristics except age standardized to the age distribution of cases and controls in 1991. Standard deviation (SD) presented for age
instead of standard error (SE).
bEnergy-adjusted using the residual method.15
ADIPOSITY AND PREMENSTRUAL SYNDROME 1957
MET-hours=week of physical activity and total calorie intake.
We observed a strong linear relationship between BMI at
baseline and risk of incident PMS (ptrend¼0.003) (Table 2).
Risk of PMS was significantly higher in women with BMI
?27.5 compared with women with BMI <20.0kg=m2. For
example, the RR in women with BMI ?35.0kg=m2was 1.66
(95% CI 1.06-2.59). In analyses of continuous BMI level, each
in PMS risk (RR 1.03, 95% CI 1.01-1.05). Results evaluating
BMI measured 2 years before reference year were nearly
with risk of PMS during the follow-up period.
Waist circumference was not linearly associated with risk
of incident PMS (ptrend¼0.15) (Table 2). After additional ad-
justment for BMI evaluated as a continuous variable, women
with waist circumference of ?36 inches in 1993 had an RR of
waist circumference of <28 inches. We also did not observe a
relationship between waist=hip ratio and risk of PMS; after
additional adjustment for BMI, the RR comparing the highest
vs. lowest quintile of waist=hip ratio was 0.85 (95% CI 0.53-
Compared with women who maintained a stable weight
(?20kg) had a significant 77% higher risk of developing
PMS during the follow-up period (95% CI 1.26-2.49) (Table 3).
Additional adjustment for BMI at reference year attenuated
risk, but results remained significant (RR 1.59, 95% CI 1.04-
reference year was consistently associated with risk.
Women who met criteria for weight cycling between ages
18 and 30 had a 36% higher risk (95% CI 1.07-1.71) com-
pared with women not reporting weight cycling; results were
Table 2. Age and Multivariable Relative Risks of Premenstrual Syndrome by Different Measures
of Adiposity, Nurses’ Health Study 2 PMS Substudy, 1991–2001
Aspect of adiposity Casesa
RR (95% CI)
Body mass index in 1991 (kg=m2)
Body mass index at age 18 (kg=m2)
Waist circumference in 1993 (inches)d
Waist=hip ratio in 1993d
aCase and controls numbers may not sum to 1057 cases and 1968 controls because of missing data.
bMultivariable relative risks (RR) are adjusted for the following factors assessed at reference year: age (<30, 30–34, 35–39, ?40 years),
diagnosis year (1993, 1994–1995, 1996–1997, 1998–1999, 2000–2001), parity (0, 1–2, 3–4 or ?5 pregnancies lasting ?6 months), oral
contraceptive use and duration (never, 1–23, 24–71, 72–119, ?120 months), pack-years of cigarette smoking (6 categories), history of tubal
ligation (no, yes), antidepressant use (never, ever), history of childhood trauma (four categories), physical activity (<3, 3–<9, 9–<18, 18–<27,
27–<42, ?42 METs=week), and dietary intake of vitamin B6and vitamin D (each in quintiles).
cExcludes women with BMI <17kg=m2at age 18.
dAnalysis limited to cases and controls who provided information on waist and hip circumference, and those with reference years after
CI, confidence interval; RR, relative risk.
1958BERTONE-JOHNSON ET AL.
attenuated after further adjustment for BMI and weight
change between ages 18 and 30 (RR 1.22, 95% CI 0.94-1.57).
Results for weight cycling between 1989 and 1993 were sim-
ilar, with cyclers having a 42% higher risk of PMS than non-
cyclers. After further adjustment for BMI and weight change
between 1989 and 1993, the RR for weight cycling was 1.24
(95% CI 0.84-1.83).
BMI evaluated continuously was significantly and posi-
tively associated with risk of a variety of physical symptoms
(Table 4). BMI was most strongly associated with swelling of
the extremities, with each 1kg=m2increase in BMI associated
with a significant 11% increase in risk. In addition, each
1kg=m2increase was associated with significant 5%–6%
higher risks of backache, abdominal cramping, diarrhea or
constipation, and food cravings. Risk of several emotional
symptoms was also positively associated with BMI; each
1kg=m2increase was associated with significant 3%–4%
higher risks of crying easily, mood swings, and irritability.
In our population of older premenopausal women, we
observed a strong positive relationship between BMI and the
significantly higher risks of developing PMS over 10 years of
follow-up compared with lean women. BMI was also posi-
tively associated with risk of specific physical and emotional
symptoms, including swelling of the extremities, backache,
abdominal cramping, diarrhea=constipation, mood swings,
and food cravings.
A limited number of previous studies have evaluated the
relationship between adiposity and premenstrual symptoms
and PMS, and to our knowledge, none of these studies have
been prospective.3,6–10Masho et al.7found PMS prevalence to
be 2.8-fold higher in obese women than underweight women
(POR¼2.8, 95%CI 1.1-2.7) in a population-based study. In the
Study of Women’s Health Across the Nation (SWAN),8the
prevalence of food cravings and bloating was significantly
higher in overweight and obese women than in normal
weight women and lower in underweight women. However,
BMI was unrelated to other symptom groups, including
anxiety and mood changes, cramps and back pain, breast
pain, and headaches.
PMS is likely caused by a complex interaction of hormonal
and neurochemical factors,5and adiposity may increase risk
through several mechanisms. Some studies have reported
inverse associations between BMI and follicular phase estra-
diol levels in older premenopausal women,19–21although
others have not observed a relationship with follicular or lu-
teal phase levels.22–24In a recent study in our cohort, adult
Table 3. Age and Multivariable Relative Risks of Premenstual Syndrome by Weight Change
and Weight Cycling, Nurses’ Health Study 2 PMS Substudy, 1991–2001
Weight change parameterCasesControls Age-adjusted RR
adjusted RR (95% CI)
Weight change from age 18 to 1991
Loss of ?5lbs
Loss or gain of <5lbs
Gain of 5–14lbs
Gain of 15–24lbs
Gain of 25–44lbs
Gain of ?45lbs
Weight change in 4 years before reference year
Loss of ?5lbs
Loss or gain of <5lbs
Gain of 5–14lbs
Gain of 15–24lbs
Gain of ?25lbs
Weight change in 2 years before reference year
Loss of ?5lbs
Loss or gain of <5lbs
Gain of 5–14lbs
Gain of 15–24lbs
Gain of ?25lbs
Weight cycling between ages 18 and 30b,c
Weight cycling between 1989 and 1993b
aMultivariable relative risks (RR) are adjusted for the following factors assessed at reference year: age, diagnosis year, parity, oral
contraceptive use and duration, pack-years of cigarette smoking, history of tubal ligation, antidepressant use, history of childhood trauma,
physical activity, and dietary intake of vitamin B6and vitamin D. See footnotebto Table 2 for variable definitions. Numbers of cases and
controls may not sum to totals because of missing data.
bWeight cycling was defined as having lost and regained ?10lbs at least three times during stated time period.14Analysis limited to cases
and controls with reference years after 1993.
cAnalysis also limited to cases and controls age ?30 by 1993.
ADIPOSITY AND PREMENSTRUAL SYNDROME1959
BMI was inversely associated with follicular and luteal phase
total estradiol and luteal phase progesterone but unrelated to
free estradiol levels21; compared with women with BMI <20,
women with BMI ?30kg=m2had 39% lower follicular estra-
diol, 20% lower luteal estradiol, and 20% lower progesterone
levels. Cyclic estrogen and progesterone fluctuations clearly
contribute to the onset of PMS, as treatments suppressing
ovulation are effective at preventing PMS symptoms.5
Whereas some evidence suggests that early luteal phase
progesterone and perhaps estradiol levels may be higher in
women with PMS compared with controls, women with PMS
may also be more sensitive to cyclic hormone fluctuations,
leading to more severe symptom experience.
Alternatively, obesity may alter neurotransmitter function
through its effect on estrogen and progesterone. In some
studies, PMS and premenstrual dysphoric disorder (PMDD)
cases have demonstrated abnormalities of the serotonin,
gamma-aminobutyric acid (GABA), and other systems com-
pared with symptom-free controls.5Estrogen enhances sero-
tonin action by increasing synthesis, transport, reuptake and
receptor expression, and postsynaptic responsiveness. Thus, it
may lead to impaired serotonin function and contribute to the
occurrence of PMS. This hypothesis is supported by clinical
studies finding selective serotonin reuptake inhibitors (SSRls)
to be effective at treating premenstrual mood symptoms, food
cravings, appetite changes, and abdominal bloating.25In our
study, BMI was positively associated with many of these
symptoms. Furthermore, the major progesterone metabolite,
allopregnanolone, binds to GABA-A receptors and increases
receptor sensitivity.5Epperson et al.26found follicular phase
cortical GABA levels to be significantly lower in PMDD cases
than in controls and to change in the opposite direction during
the luteal phase. Thus, lower progesterone levels associated
with obesity may impair GABA function and further contrib-
ute to the development of mood symptoms in PMS.
Increased adiposity may contribute to water-retention
symptoms in PMS though dysregulation of the renin-angio-
and fluid retention.27Estrogen stimulates the RAAS and in-
creases fluid retention, whereas late luteal phase progesterone
appears to counteract these effects.5,28Finally, adiposity may
be related to PMS by affecting vitamin D status. Obese indi-
viduals are at greater risk for vitamin D deficiency, as the main
circulating vitamin D metabolite, 25-hydroxyvitamin D, is se-
regulation.29A previous study in our population found dietary
intake of vitamin D to be inversely related to PMS incidence,
although we were unable to assess the contribution of endog-
enously produced vitamin D.11Additional studies evaluating
the potential interplay of adiposity, sex steroid hormones, and
neurotransmitters in PMS development are needed.
Our study has several limitations. As our participants were
aged 27–44 at baseline, we were not able to prospectively
Table 4. Risk of Specific Menstrual Symptoms for Each 1kg=m2Increase in Body Mass Index at Baseline,
Nurses’ Health Study 2 PMS Substudy, 1991–2001
Multivariable RR (95% CI)c
Swelling of extremities
Tendency to cry easily
Desire for aloneness
aNumber of PMS cases reporting specific symptom.
bNumber of controls not reporting specific symptom.
cBeta coefficients are adjusted for the following factors assessed at reference year: age, diagnosis year, parity, oral contraceptive use and
duration, pack-years of cigarette smoking, history of tubal ligation, antidepressant use, history of childhood trauma, physical activity, and
dietary intake of vitamin B6and vitamin D. See footnotebto Table 2 for variable definitions.
1960 BERTONE-JOHNSON ET AL.
evaluate the effect of adiposity and PMS at younger ages. Our
results suggested that BMI at age 18 was not associated with
PMS developing later in life (i.e., after age 27). Although ad-
PMS, it is also possible that women who were overweight or
obese at age 18 developed PMS during adolescence or young
adulthood. These women would have been ineligible for our
study, as we excluded women who had already been diag-
nosed with PMS by the beginning of follow-up in 1991. Pro-
spective studies of adolescent and young adult women are
needed to further assess this relationship.
adiposity (e.g., waist circumference, waist=hip ratio) or
weight cycling to be independently related to the develop-
ment of PMS. These aspects of adiposity may indeed be un-
related to PMS, but we were only able to assess them in a
subset of our study population; thus, our power for these
analyses was relatively low. In addition, because we assessed
only the presence of specific menstrual symptoms and not the
severity of each, we could not identify which symptoms were
most problematic to participants. Future large studies asses-
sing multiple aspects of adiposity and severity of specific
menstrual symptoms will be important to improving under-
standing of PMS etiology. Finally, as our study popula-
tion was predominantly white, our findings may not be
generalizable to women of other racial and ethnic groups,
although it is unlikely that the physiological relationship
between adiposity and PMS differs substantially between
Strengths of our study include our prospective assessments
of adiposity and aspects of weight change during the follow-
up period. In addition, we collected information on a wide
variety of additional factors potentially related to PMS oc-
currence and adiposity, including physical activity, smoking,
and diet, and have taken these into consideration in our
analysis. Finally, we used established criteria to define PMS
cases13and controls in order to identify women at the two
extreme ends of the spectrum of menstrual symptom experi-
ence, which limited the likelihood of misclassification and
maximized our ability to identify risk factors for moderate to
Our findings suggest that maintaining a healthy body
weight may be important for preventing the development of
PMS. Additional studies are needed to assess whether losing
weight would benefit overweight and obese women who
currently experience PMS and to further evaluate potential
underlying mechanisms of these relationships.
Consumer Healthcare; a cy pres distribution, Rexall=
Cellasene Settlement Litigation; and Public Health Services
grant CA50385 from the National Cancer Institute, National
Institutes of Health, Department of Health and Human
No competing financial interests exist.
1. Johnson SR. The epidemiology and social impact of pre-
menstrual symptoms. Clin Obstet Gynecol 1987;30:367–376.
2. Sternfeld B, Swindle R, Chawla A, et al. Severity of pre-
menstrual symptoms in a health maintenance organization
population. Obstet Gynecol 2002;99:1014–1024.
3. Deuster PA, Adera T, South-Paul J. Biological, social and
behavioral factors associated with premenstrual syndrome.
Arch Fam Med 1999;8:122–128.
4. Mortola JF. Issues in the diagnosis and research of pre-
menstrual syndrome. Clin Obstet Gynecol 1992;35:587–598.
5. Halbreich U. The etiology, biology, and evolving pathology
of premenstrual syndromes. Psychoneuroendocrinology
2003;28 (Suppl 3):55–99.
6. Strine TW, Chapman DP, Ahluwalia IB. Menstrual-related
problems and psychological distress among women in the
United States. J Womens Health 2005;14:316–323.
7. Masho S, Adera T, South-Paul J. Obesity as a risk factor for
premenstrual syndrome. J Psychsom Obstet Gynecol 2005;
8. Gold EB, Bair Y, Block G, et al. Diet and lifestyle factors
associated with premenstrual symptoms in a racially diverse
community sample: Study of Women’s Health Across the
Nation (SWAN). J Womens Health 2007;16:641–656.
9. Hourani LL, Yuan H, Bray RM. Psychosocial and lifestyle
correlates of premenstrual symptoms among military wo-
men. J Womens Health 2004;13:812–821.
10. Adewuya AO, Loto OM, Adewumi TA. Pattern and correlates
of premenstrual symptomatology amongst Nigerian Uni-
versity students. J PsychosomObstet Gynecol2009;30:127–132.
11. Bertone-Johnson ER, Hankinson SE, Bendich A, Johnson SR,
Willett WC, Manson JE. Calcium and vitamin D intake and
risk of incident premenstrual syndrome. Arch Intern Med
12. Bertone-Johnson ER, Hankinson SE, Johnson SR, Manson JE.
A simple method for assessing premenstrual syndrome in
large prospective studies. J Reprod Med 2007;52:779–786.
13. Mortola JF, Girton L, Beck L, Yen SS. Diagnosis of pre-
menstrual syndrome by a simple, prospective, and reliable
instrument: The Calendar of Premenstrual Experiences.
Obstet Gynecol 1990;76:302–327.
14. Field AE, Manson JE, Taylor CB, Willett WC, Colditz GA.
Association of weight change, weight control practices, and
weight cycling among women in the Nurses’ Health Study
II. Int J Obesity Rel Metab Disord 2004;28:1134–1142.
15. Willett WC. Nutritional epidemiology, 2nd ed. New York:
Oxford University Press, 1998.
16. Jun HJ, Rich-Edwards JW, Boynton-Jarrett R, Wright RJ.
Intimate partner violence and cigarette smoking: Association
between smoking risk and psychological abuse with and
without co-occurrence of physical and sexual abuse. Am J
Public Health 2008;98:527–535.
17. Ainsworth BE, Haskell WL, Leon AS, et al. Compendium of
physical activities: Classification of energy costs of human
physical activities. Med Sci Sports Exerc 1993;25:71–80.
18. Wolf AM, Hunter DJ, Colditz GA, et al. Reproducibility and
validity of a self-administered physical activity question-
naire. Int J Epidemiol 1994;23:991–999.
19. Potischman N, Swanson DA, Siiteri P, Hoover RN. Reversal
of relation between body mass and endogenous estrogen
concentrations with menopausal status. J Natl Cancer Inst
20. Randolf JF Jr, Sowers M, Gold EB, et al. Reproductive hor-
mones in the early menopausal transition: Relationship to
ADIPOSITY AND PREMENSTRUAL SYNDROME1961
ethnicity, body size and menopausal status. J Clin En- Download full-text
docrinol Metab 2003;88:1516–1522.
21. Tworoger SS, Eliassen AH, Missmer SA, et al. Birthweight
and body size throughout life in relation to sex hormones
and prolactin concerntrations in premenopausal women.
Cancer Epidemiol Biomarkers Prev 2006;15:2494–2501.
22. Dorgan JF, Reichman ME, Judd TT, et al. The relation of
body size to plasma levels of estrogens and androgens in
premenopausal women (Maryland, United States). Cancer
Causes Control 1995;6:3–8.
23. Nagata C, Kaneda N, Kabuto M, Shimizu H. Factors asso-
ciated with serum levels of estradiol and sex hormone-
binding globulin among premenopausal Japanese women.
Environ Health Perspect 1997;105:994–997.
24. Tufano A, Marzo P, Enrini R, Morricone L, Caviezel F,
Ambrosi B. Anthropometric, hormonal and biochemical
differences in lean and obese women before and after
menopause. J Endocrinol Invest 2004;27:648–653.
25. Halbreich U, O’Brien S, Eriksson E, Ba ¨ckstro ¨m T, Yonkers
KA, Freeman EW. Are there differential symptom profiles
that improve in response to different pharmacological
treatments of premenstrual syndrome=premenstrual dys-
phoric disorder? CNS Drugs 2006;20:523–547.
26. Epperson CN, Haga K, Mason GF, et al. Corticol gamma-
aminobutyric acid levels across the menstrual cycle in
healthy women and those with premenstrual dysphoric
disorder: A proton magnetic resonance spectroscopy study.
Arch Gen Psychiatry 2002;59:851–858.
27. Rahmouni K, Correiz MLG, Haynes WG, Mark AL. Obesity-
associated hypertension. New insights into mechanisms.
28. Olson BR, Forman MR, Lanza E, et al. Relation between
sodium balance and menstrual cycle symptoms in normal
women. Ann Intern Med 1996;125:564–567.
29. Lee JH, O’Keefe JH, Bell D, Hensrud DD, Holick MF. Vita-
min D deficiency: An important, common and easily treat-
able cardiovascular risk factor? J Am Coll Cardiol 2008;52:
Address correspondence to:
Elizabeth R. Bertone-Johnson, Sc.D.
Arnold House, University of Massachusetts
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