Running head: LEAN BODY MASS IN MENOPAUSE

Association of Lean Body Mass to Menopausal Symptoms
in the Study of Women’s Health Across the Nation

A DISSERTATION
Submitted to the Faculty of the School of Graduate Studies and Research of California
University of Pennsylvania in partial fulfillment of the requirements for the degree of Doctor of
Health Science (DHSc) in Health Sciences and Exercise Leadership

By
Rosanne Woods

Research Adviser, Dr. Rebecca Hess
California, Pennsylvania
2018
CALIFORNIA UNIVERSITY of PENNSYLVANIA
CALIFORNIA, PA

LEAN BODY MASS IN MENOPAUSE

DISSERTATION APPROVAL

Health Science and Exercise Leadership

We hereby approve the Dissertation of

Rosanne Woods
Candidate for the degree of Doctor of Health Sciences (DHSc)

LEAN BODY MASS IN MENOPAUSE

ACKNOWLEDGEMENTS

This has been an amazing process from which I have grown as a student of academics, a
researcher and as a person. For that I would like to thank my research advisor, Dr. Rebecca
Hess, for her direction, support, encouragement and expertise in preparing my dissertation.
Additionally, I would like to thank my research committee members, Dr. Marc Federico and Dr.
Carol Biddington for your guidance and support on this journey and Dr. Melissa Sovak for her
expertise and guidance on the statistical analysis. I would also like thank Dr. Jeffery Hatton for
having my back and going to bat for me. Without the support I received from all of you, I would
not have accomplished my dream.
And lastly, I am so very grateful to my family. To my husband, who continues to be my
biggest fan and my greatest source of strength, your unconditional support and unwavering
confidence in me motivates me every day. To my babies, Alee, Jake, Joe, Ben and Sam, who
didn’t see much of their Momma some days, thank you for never complaining and for allowing
me the time to get my work done. And to Brodie & Leah who helped both financially, and with
love, support and encouragement, I thank you. This would not have been possible without the
grace and love of all of you. I am truly blessed and appreciate and love every one of you more
than you know.

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TABLE OF CONTENTS
Page
List of Figures ...................................................................................................................... i

List of Tables ...................................................................................................................... ii

Abstract .............................................................................................................................. iii

Introduction ..........................................................................................................................1

Methods................................................................................................................................6
Research Design.......................................................................................................6
Participants ...............................................................................................................7
Instruments ...............................................................................................................8
Procedures ..............................................................................................................12
Data Analysis .........................................................................................................14

Results ................................................................................................................................16
SWAN Study Data .................................................................................................16
Study Sample Characteristics ................................................................................17
Hypotheses Testing ................................................................................................21
Predicted Probabilities ...........................................................................................23

Discussion ..........................................................................................................................24

Conclusion .........................................................................................................................30

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Future Directions for Research ..........................................................................................31

References ..........................................................................................................................32

Appendix A – Review of the Literature .............................................................................45

Appendix B – Problem Statement......................................................................................88

Appendix C – Additional Methodology
Appendix C1 – Informed Consent .........................................................................91
Appendix C2 – IRB Review Request Approval ..................................................104
Appendix C3 – Certificates of IRB and SAS Training ........................................106

References ........................................................................................................................109

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LIST OF FIGURES
Page

Figure 1

Timeline of SWAN participant visits ........................................................14

Figure 2

Association of body composition variables at baseline grouped by VMS with
symptoms = 1 represents ‘any’ and symptoms = 0, ‘none’ .......................19

Figure 3

Correlation of Mean SMI to Mean FMI over time ....................................20

Figure 4

Mean SMI, FMI and FFMI at each visit grouped by symptoms ...............21

Figure 5

Predicted probability of symptoms occurring at given levels of SMI (adjusted for
FMI) in women with no VMS at baseline .................................................23

Figure 6

Menopausal status of study cohort by visit ................................................24

Figure 7

Characteristics of remaining cohort ...........................................................25

Figure 8

Symptoms and deleterious effects of menopause ......................................49

Figure 9

Relationship between MetS components and cardiometabolic complications
....................................................................................................................54

Figure10

Menopause-related changes in muscle mass and its impact on functional status
....................................................................................................................57

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LIST OF TABLES
Page

Table 1

Baseline characteristics by symptom status .....................................................18

Table 2

Association of VMS to LBM ...........................................................................22

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Abstract
Vasomotor symptoms (VMS) are experienced by most women as they transition through
menopause, but their etiology is incompletely understood as is their relationship to body
composition. While the association of VMS to body fat, body weight, and body mass index has
been broadly researched, little is known about the role of lean body mass. The purpose of this
research was to examine the association of lean body mass (LBM) to the development of VMS
as women transition through menopause. Data from 2,533 participants in the longitudinal Study
of Women’s Health Across the Nation (SWAN) who provided bioelectrical impedance (BIA)
measurements over five visits, was accessed and examined. Women who reported no VMS at
baseline were modelled for concurrent association of skeletal muscle mass and fat free mass with
VMS, and additionally for percent change since prior visit and percent change since baseline.
Adjusted models included covariates of fat mass, age, race/ethnicity, education, and menopausal
status. VMS with concurrent LBM was significant in unadjusted (p <.0001) and adjusted models
(p = .036). Percent change since prior visit and since baseline models were significant (adjusted
p = .003, p <.001; unadjusted p = .009, p = .001) and overall mean association remained
significant (p = .007, p = .023). Associations at each visit were not significant in logistic
regression. Predicted probabilities of VMS showed a negative correlation to lean body mass for
all visits and overall regression analysis. These results suggest that maintaining higher levels of
LBM as women progress through menopause may protect against the development of VMS.
Keywords: Lean body mass – vasomotor symptoms – hot flashes – night sweats skeletal muscle mass – fat free mass

LEAN BODY MASS IN MENOPAUSE

Association of Lean Body Mass to Menopausal Symptoms
in the Study of Women’s Health Across the Nation

Menopause is a significant event in many women’s lives as it marks the end of the
natural reproductive life. For most women, menopause will occur between the ages of 40
and 58 years with the average being 51 years (Zapantis & Santoro, 2003). The Stages of
Reproductive Aging Workshop +10 (Harlow et al., 2012) classifies the transition through
menopause into five stages. Early menopause (variable) and late menopausal (1-3 years)
both precede the final menstrual period (FMP). The FMP marks the start of the
postmenopausal stages, early postmenopause (1-2 years), postmenopause (3-6 years), and
late postmenopause (remaining lifespan). The menopause transition is often
accompanied by a variety of bothersome symptoms (vasomotor, psychological,
psychosocial, urogenital) that are uniquely experienced and impact quality of life for
menopausal women. About 80-96% of women experience mild to severe physical or
physiological menopause-related complaints as they approach menopause due to
declining estrogen levels (Moilanen et al., 2012) which may include hot flashes and night
sweats, depression, irritability, sleep disorders, increased abdominal fat mass, increased
prevalence of metabolic syndrome, and increased risk of cardiovascular disease
(Stefanska et al., 2015). Hot flashes and night sweats are the most common symptoms of
menopause and are collectively referred to as vasomotor symptoms (VMS) due to the
dilation of blood vessels during an episode. They are reported as feelings of intense
warmth along with sweating, flushing (in the face, neck and chest), and chills (Gallichio
et al., 2014). It is reported that 60-80% of women will experience VMS at some point
during the menopausal transition (Thurston & Joffe, 2011).

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Recent research has been focused on examining the correlation of obesity, BMI,
and adiposity to the presence and burden of VMS (Da Fonseca et al., 2013; Gallichio et
al., 2014; Gjelsvik et al., 2011; Herber-Gast, et al., 2013; Kazlauskaite et al., 2015;
Thurston, Chang, Mancuso, & Matthews, 2013). It was originally hypothesized that
body fat protected against VMS because of the aromatization of androgens to estrogens
in fat tissue (Kershaw & Flier, 2004), but early studies showed an association of higher
BMI and body fat with greater VMS reporting (Freeman et al., 2001; Thurston et al.,
2009; Thurston et al., 2008a). Further research has shown that higher adiposity is a risk
factor for VMS in early postmenopause but is a protective factor in late menopause
(Abdulnour et al, 2012; Thurston et al., 2013; Thurston et al, 2008b). Thurston et al.
(2009) examined the relationship between body fat change to VMS over time in 1,659
women aged 47-59 and found that body fat gains were associated with greater hot flush
reporting during the menopausal transition. To date, several risk factors for VMS have
been identified including race/ethnicity, obesity, smoking, and diet (Thurston & Joffe,
2011), however, no studies have been conducted on the association of lean body mass
(LBM) to VMS or other menopausal symptoms.
As they transition through menopause, women will simultaneously experience a
decrease in basal metabolic rate and a loss of lean muscle tissue which increases the risk
of weight gain and obesity (Lovejoy, 2009). Loss of muscle mass and function normally
associated with aging is referred to as sarcopenia. In women, sarcopenia develops and is
highly prevalent during menopause, and is primarily due to an imbalance between muscle
protein synthesis and breakdown, contributed to by an increase in oxidative stress, proinflammation markers, and hormonal changes (Messier et al., 2011). Oxidative stress is

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caused by the imbalance between the production of reactive oxygen species (free
radicals) and antioxidant defenses resulting in tissue damage and its increase during
menopause is attributed to aging (Mittal & Kant, 2009). Sarcopenic obesity, which refers
to elevated body fat mass combined with reduced muscle mass, has also been proposed as
characterizing age-related changes in body composition (Roubenoff, 2004). Evidence
indicates that muscle strength and quality (ratio of muscle strength to mass) may be
negatively associated with the severity of menopausal symptoms due to declining levels
of sex hormones and the resulting increase in oxidative stress (Lee & Lee, 2013). The
authors also suggested that there is a vicious cycle of increased oxidative stress during
menopause that may induce the loss of muscle strength, and in turn, the loss of muscle
strength increases oxidative stress (Lee & Lee, 2013). Postmenopausal women have been
shown to have significantly higher oxidative stress blood marker levels and lower
antioxidant capacity relative to premenopausal women (Signorelli et al., 2006) which is
thought to be correlated to their higher body weight (Mittal & Kant, 2009).
Body weight gain at midlife is frequently reported by women; studies have shown
that an increase in subcutaneous adipose tissue is related to age whereas the increase in
visceral and total body fat is related to the abrupt decline in estrogen (Abdulnour et al.,
2012; Lovejoy et al., 2008). This accumulation of abdominal fat in postmenopausal
women appears to be a critical factor in the development of insulin resistance, which in
turn is a major risk factor for type 2 diabetes (Lobo et al., 2014). In their longitudinal
study, Kabat et al. (2014), found that BMI, waist circumference, and the waist
circumference to height ratio robustly predicted change in cardiometabolic risk in postmenopausal women with the strongest associations seen among increased triglycerides,

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glucose, and reduced high-density lipoprotein (HDL) cholesterol. For postmenopausal
women, chronic systemic inflammation, oxidative stress, abdominal visceral adipose
tissue, dyslipidemia, sarcopenia, and a sedentary lifestyle are all risk factors for metabolic
syndrome (Mendoza et al., 2016). A review by Maltais et al. (2009) found that the
menopausal transition is associated with a decline in estrogen, growth hormone, IGF-1,
and DHEA, a decrease in muscle protein synthesis, and an increase in catabolic factors
such as the pro-inflammatory cytokines, and tumor necrosis factor alpha (TNF-α) or
interleukine 6 (IL-6). The authors concluded that low physical activity, inadequate
protein intake, and elevated oxidative stress are the greatest contributors of sarcopenia in
postmenopausal women (Maltais et al., 2009).
A recent study by Takamura et al. (2017) found that weight-adjusted LBM and
skeletal muscle area were protective against weight-associated insulin resistance and
metabolic abnormalities. As skeletal muscle also has estrogen receptors and estradiol
plays a role in the glucose metabolism of skeletal muscles (Lemoine et al., 2003), the
decrease in estrogen production in menopausal women further impacts glucose
metabolism. Thus, women with lower muscle mass and fewer estrogen receptors are at
greater risk for metabolic complications (Ou et al., 2017). Additionally, Abildgaard et al.
(2013) found that decreased LBM is the most important contributor to changes in
metabolism for postmenopausal women as it correlates to low whole-body fat oxidation
and energy expenditure which in turn are associated with high visceral fat mass and low
insulin resistance.
Maintaining adequate levels of muscle mass as women transition into menopause
may play a role in minimizing the risks of sarcopenic obesity and protect against the

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development of deleterious metabolic conditions commonly associated with menopause.
Exercise has also been considered as a potential mitigating factor for the presence and
severity of menopausal symptoms, but the research is limited with focus on low- to
moderate-intensity cardiovascular exercise (Bailey et al., 2016; Daley et al., 2014; Luoto
et al., 2012; Sternfeld et al., 2104; Zhang et al., 2014) where the evidence has shown
mixed results. Research regarding resistance training is very limited (Alvarez &
Campillo, 2013; Boganha et al., 2012; Conceição et al., 2013; Correa et al., 2015; Leite et
al., 2010), but has shown a positive correlation with some metabolic markers and quality
of life. However, little is known regarding the role of LBM and its influence on
menopausal symptoms throughout the transition period.
The purpose of this research was to examine the association between relative
levels of lean body mass and the development of VMS as women transition through
menopause using the public data set from the Study of Women’s Health Across the
Nation (SWAN) (Sutton-Tyrell et al., 2018a-f). As the transition into menopause is
uniquely stressful and perceived symptoms can be impacted by many factors, the role of
hip/waist circumference, self-reported quality of life, and physical activity levels were
also considered. The main hypothesis tested was that lower LBM is associated with an
increased prevalence of reported VMS of hot flashes and night sweats.

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Methods

This section will describe the design and methods used for the current research
study and provide a brief review of the methods used for the Study of Women’s Health
Across the Nation (SWAN). The data collected from the SWAN study, forming part of a
free public use data set, was accessed for statistical analysis and interpretation in the
current study.

Research Design
This study conducted a secondary analysis of existing data utilizing the public
dataset from the SWAN study participants to identify, analyze, and describe factors
contributing to the development of VMS over time. LBM (relative to height) was
compared over the span of the study as the participants moved through menopause to the
development/presence of VMS. Statistical comparisons of variables, including body
weight, waist circumference, bodyfat percent, and LBM was made between those who
developed VMS and those who did not. The SWAN study was a U.S. seven-site,
longitudinal cohort study of five racial/ethnic groups of women aged 42 to 52 years at
enrollment in 1995 to 1997 who were pre- or early perimenopausal. Data was still being
collected at the 15th visit in 2016. This analysis was limited to data collected at five of
these sites (Pittsburgh, PA, Boston, MA, Detroit, MI, Los Angeles, CA, and Oakland,
CA) which collected additional data from dual-energy x-ray absorptiometry (DXA) scans
for bone mineral density and body composition as part of the bone sub study conducted
annually for 10 years and then biennially twice (visits 11 and 12) by 2010-2011 on 2,335

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participants. Examination and analysis of this public data set demonstrates a heightened
ability to generalize the results of the current study to those women who fit within the
parameters of the SWAN study.
As published on the SWAN study website (www.swanstudy.org), the “SWAN
Public Use Datasets provide access to longitudinal data describing the physical,
biological, psychological, and social changes that occur during the menopausal transition.
Data collected from 3,302 SWAN participants from Baseline through the 10th Annual
Follow-Up visit are currently freely available to the public. SWAN researchers have
published over 300 manuscripts covering a wide range of topics related to menopause
and women’s health. Through the utilization of this public database, the current study
conducted an original analysis on the data collected through DXA scans at the Boston,
Detroit, Pittsburgh, Los Angeles, and Oakland sites on participants in the bone cohort not
previously considered by other researchers.

Participants’ Data
Complete details of the SWAN design and recruitment procedures are reported
elsewhere in the original study report (Sowers et al., 2000), however, a summary is
provided here. Baseline eligibility criteria included being aged 42 to 52 years, having a
uterus and at least one ovary, not being pregnant or lactating, not using oral
contraceptives or hormone therapy in the previous three months, and having at least one
menstrual cycle in the preceding months. Participants self-identified as AfricanAmerican (28%), Caucasian (47%), Chinese (8%), Hispanic (8%), or Japanese (9%).

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For the SWAN study, a total of 202,985 sampling units of households, telephone
numbers, or individual names of women were screened through either random digit
dialing or list sampling frames for potential participation in the SWAN cross sectional
study. Of these, 34,985 met the criteria and were defined as eligible and 16,065
completed the interview (cross sectional phase of the study), for an overall response rate
of 46.6%. Of these, 6,521 women were cohort-eligible and asked to participate in the
SWAN Longitudinal Study; a total of 3,306 women entered the cohort, for an overall
response rate of 50.6%. Of 2,413 participants at the five SWAN bone study sites, 2,335
were enrolled in the bone cohort at baseline. Their data formed the subject database for
this study. Procedures included annual examinations, questionnaires, blood draw, and
DXA Scan. Written informed consent was obtained from each participant and the study
was approved by the institutional review boards at each site (Appendix C1). Approval for
this study was granted by the Institutional Review Board at California University of
Pennsylvania (Appendix C2).

Instruments
The current study analyzed body mass data collected during the SWAN study at
the five U.S. sites utilizing dual energy x-ray absorptiometry – Boston, MA, Detroit, MI,
Los Angeles, CA, Pittsburgh, PA, and Oakland, CA. This data is available for free public
use and was used to determine the association of LBM to the development of VMS.
Body composition. Lean body mass was measured by dual energy x-ray
absorptiometry (DXA) using Hologic Instruments (Hologic Inc., Waltham, MA, USA).
Three sites (Boston, MA, Detroit, MI, and Los Angeles, CA) used Hologic model 4500A

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throughout the study period, and two sites (Pittsburgh, PA and Oakland, CA) upgraded
from the 2000 to 4500A model during follow-up but cross-calibrated the machines
(Solomon et al., 2015). Each DXA laboratory measured a Hologic anthropomorphic
spine phantom daily, and a phantom was circulated among laboratories for crosscalibration. Phantom measurements were analyzed by Synarc, Inc. (Waltham, MA,
USA) with calibration regression coefficients used by the study’s coordinating center to
adjust DXA measurements for minor temporal or geographic variations in densitometer
performance. Additional quality-control measures included review of every scan image
by a local site investigator and central review or a random subset of 5% of all scans and
all problem scans by Synarc, Inc. (Sowers et al., 2006; Solomon et al., 2015). DXA
measures fat tissue mass and lean tissue mass with very good precision (coefficient of
variation) of 4.7% and 1.5% respectively (Svendsen, Hassager & Christiansen, 1995).
Vasomotor symptoms. Hot flashes and night sweats were assessed via
questionnaire at each SWAN visit. In the end of the month survey, women responded to
two questions that separately asked them to record how often hot flashes and night sweats
were experienced in the two weeks prior to the annual visit (not at all, 1-5 days, 6-8 days,
9-13 days, everyday) at each of the 12 annual visits. To measure the accuracy of recall
for VMS among SWAN participants, Crawford et al. (2008) analyzed data from a sub
cohort of 880 women recruited into the Daily Hormone Study, with daily urine collection
for an entire menstrual cycle concurrent with completion of a daily symptom diary, and
compared the entries to responses in the end-of-month survey (as was completed by all
SWAN participants) over a three-year period. The authors found the overall sensitivity
of the two-week recall as compared to daily recall was high, ranging from 78% to 84%,

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and that the specificity was also high, ranging from 85% to 89% suggesting that this
method of data collection is acceptable for use in research.
Anthropometric measures. Height (m) and weight (kg) were measured in light
clothing, without shoes, using a standard protocol with a stadiometer for height and a
balance beam scale for weight. Waist circumference (cm) was measured at the level of
the natural waist or the narrowest part of the torso from the anterior aspect. (Gold et al.,
2017; Sowers et al., 2006).
Covariates. All covariates were selected on the basis of previously documented
associations with VMS (Gold et al., 2006) and body composition (Sowers et al., 2006),
and included age, educational level (less than high school, high school, some college,
college, or post baccalaureate degree), race/ethnicity (self-designated Caucasian, African
American, Japanese, or Chinese), physical activity, quality of life, and menopausal
transition stage. Race/ethnicity and educational level were self-reported in the SWAN
screening interview. SWAN participants were assessed for menopausal status
assignment based on annual reports about menstrual bleeding and its regularity. Premenopause was identified as no decreased regularity in menstrual bleeding during the last
year. Other classifications were early perimenopause (decreased menses regularity in the
three months before interview), late perimenopause (no menses for 3-11 months), and
postmenopause (no menses for 12 or more months) (Thurston et al., 2013). Surgical
menopause was defined by report of either hysterectomy or oophorectomy, and hormone
therapy (HT) use was reported as use of HT during the year (Thurston et al., 2013).
Physical activity was assessed using an adaptation of the Kaiser Physical Activity
Survey (Sternfeld, Ainsworth & Quesenberry, 1999). This survey is a self-administered

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questionnaire with high to very high established test-retest reliability (r = 0.79 to 0.91, p
<.01) and low to moderate validity (r = 0.28 to 0.64, p <.01) against activity records,
accelerometer recordings, and maximal oxygen consumption among White women
(Ainsworth et al., 2000) and concurrent validity among racial/ethnic minority women in
terms of body mass index and socioeconomic factors (Sternfeld et al., 1999). Originally
adapted from the Baecke questionnaire (Baecke, Burema & Fritjers, 1982), the version of
the Kaiser Physical Activity Survey used in SWAN consists of 38 questions with
primarily Likert-scale responses about physical activity in various domains, including
sports/exercise, household/caregiving, and daily routine (defined as walking or biking for
transportation and hours of television viewing) which are reverse-scored.
The Medical Outcomes Short-Form 36 (SF-36) was used to assess health related
quality of life (HRQL) using the original coding algorithm in which raw scores are
transformed to a 0 to 100 range. Scoring generates total scores for the mental health
dimension and physical health dimension in which higher scores indicate a higher quality
of life. The SF-36 is a generic HRQL measure yielding eight subscales, of which SWAN
used five that are considered the components of the physical dimension: bodily pain, role
limitations due to physical health, role limitations due to emotional problems, social
functioning, and vitality (Avis et al., 2003). Jenkinson, Wright and Coulter (1994) found
the internal consistency of the SF-36 to have a Cronbach’s alpha of >.8 for each of the
scales except social functioning (alpha = .76) and high internal reliability (alpha >.7) for
all except social functioning (alpha >.5), which is considered acceptable as it has fewer
items in the domain. The authors also showed Kruskal-Wallis tests indicated clear linear

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trends for all seven dimensions being strongly associated (p <0.0001) with patient
reported overall general health (Jenkinson et al., 1994).

Procedures
This section details the procedures followed in the SWAN study and further
outlines the procedure followed for this study. After approval from the California
University of Pennsylvania’s Institutional Review Board (IRB) (Appendix C2), the
current study examined data obtained from participants in the SWAN study. Multiple
variables within the existing public dataset available from the SWAN Study website
(https://www.swanstudy.org) including age, height, weight, menopausal status, education
level, race/ethnicity, LBM, fat mass, waist circumference, VMS (hot flashes, night
sweats), quality of life, and physical activity were accessed for each of the five sites
(Boston, Detroit, Pittsburgh, Los Angeles and Oakland) designated in the SWAN study
as participating in the bone cohort (with additional data collected using dual energy x-ray
absorptiometry), and downloaded for analysis.
SWAN study. A detailed description of the SWAN study design has been
previously published (Sowers et al., 2000). Each of the five sites selected for the current
study adhered to the presiding Institutional Review Board’s guidelines. The initial crosssectional study consisted of a 15- to 20-minute telephone interview (or face-to-face
interview in instances where no telephone number could be associated with a sampled
respondent). This interview was administered to 16,065 women aged 40-55 years who
were randomly selected from sampling frames established at each site and included a
verbal consent for interview. Women then provided information on sociodemographic,

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medical and menstrual history, symptoms, and psychosocial factors. Sites used a variety
of sampling frames and recruitment strategies to recruit a community-based sample of
local women belonging to four ethnic groups. Eligible women meeting the inclusion
criteria were invited to join the cohort and were seen within three months of the initial
survey for their baseline assessment, and a written informed consent was obtained.
Assessments consisted of questionnaires regarding medical history, medication,
menstrual history, lifestyle, psycho-social factors, physical and psychological symptoms,
and health-related quality of life, as well as blood and urine specimen collection and
physical measures. Questions were administered orally by trained staff or were part of a
paper-and-pencil form. For women enrolled in the bone cohort, body composition was
determined from dual energy x-ray absorptiometry (DXA) performed by trained and
certified technicians following a standardized protocol. Whole-body scan data at each of
the five sites was measured as kilograms of lean mass and fat mass. After enrollment in
1996-1998, eligible participants in the SWAN bone cohort, representing a potential
maximum of 2,335 for this study, were followed with annual in-person clinic visits that
included signed, written informed consent (Appendix C1). All measures described above
were repeated annually for 10 visits on the anniversary of the baseline assessment and
then biennially for visits 11 and 12. The timeline of the ongoing data collection which
has continued to the 15th visit is shown below in Figure 1(www.swanstudy.org).
However, only datasets up to and including the 10th visit, are currently available as part of
the public dataset.

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Figure 1. Timeline of data collection. Retrieved from www.swanstudy.org
Data Analysis
The following general aims were investigated:
•

LBM is associated with physical activity levels in postmenopausal women

•

The association of LBM with the cardiovascular risk factors for postmenopausal
women of increased waist circumference and body mass index

•

The correlation between development of VMS during the menopausal transition
with LBM

•

The association between LBM and self-reported quality of life

Specific hypotheses of the study included:
Hypothesis 1 (H1) - Lower concurrent LBM will be associated with greater
concurrent incident reporting of VMS
Hypothesis 2 (H2) - In longitudinal analyses, lower LBM over time, since
baseline, will be associated with greater incident reporting of VMS
Hypothesis 3 (H3) - In longitudinal analyses, lower LBM over time, since last
annual visit, will be associated with greater incident reporting of VMS

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Characteristics of the sample were described by means (standard deviation) and
frequency (%). At baseline, two VMS groups – any or none – were compared for group
differences in, and associations among, demographics (age, race/ethnicity, education),
quality of life (SF-36 score), physical activity score, and clinical characteristics (weight,
waist circumference, menopausal status, LBM ), and VMS was estimated using chi
square test (

) for categorical variables, and Kruskal-Wallis test for continuous

variables. A scatter plot matrix was used to examine linear correlations among variables.
Analysis of incident VMS included only women reporting no VMS in the prior two
weeks at baseline. A participant’s data was censored and omitted at initiation of hormone
therapy, at hysterectomy or bilateral oophorectomy, or at her last visit and visits
concurrent with pregnancy or breastfeeding.
To assess H1, incident VMS was modeled as a function of concurrent LBM using
logistic regression analysis. To address H2 regarding long term change in LBM, the
model was expanded to add within-woman percent change in LBM since baseline and to
address H3, regarding recent change in LBM, the model was expanded to add withinwoman percent change in LBM since prior visit (approximately one year earlier). The
overall association between LBM and VMS was estimated in binary logistic regression
models. Statistical analyses were one-tailed with an alpha level of 0.05and conducted
using SAS University Edition (© 2012-2018, SAS Institute Inc., Cary, NC).

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Results
Swan Study Data
At the time of data access and retrieval for this study, the SWAN data collected
via Dual Energy X-ray Scans was unavailable for public use. Therefore, this analysis

was limited to the use of body composition data collected at visits six through 10 at each
of the seven SWAN sites (Pittsburgh, PA, Boston, MA, Chicago, IL, Detroit, MI, Los
Angeles, CA, Newark, NJ, and Oakland, CA) using bioelectrical impedance analysis
(BIA) and included 2,533 women. BIA is based on measurement of the transmission
speed of a one-quarter volt electrical pulse between electrodes attached at the feet and
electrodes attached across the knuckles of the hand. Because fat-free mass is comprised
of water, proteins, and electrolytes, conductivity is greater in fat-free mass than in fat
mass (Lukaski, Johnson, Bolonchuk, & Lykken, 1985). Resistance and reactance are
used to estimate total body water, and by extension, fat mass and lean mass, with the
latter including bone (Boulier, Fricker, Thomasset, & Apfelbaum, 1990). The validity
and predictive value of BIA in menopausal women has been confirmed by a recent study
by Tanaka et al., (2015). The main finding of the study was that the SEE (3.6%), the
existence of systematic error (r = 0.447, p <.05), and the slope and the intercept of the
regression line between measurement values by DXA and BIA were not significantly
different for menopausal or premenopausal women. The authors determined that the
predictive accuracy of BIA for postmenopausal women would not be inferior to that for
premenopausal women. Skeletal muscle mass was calculated by the method of Janssen et
al. (2000), who subsequently indexed skeletal muscle mass to height for a skeletal muscle
index (SMI = skeletal muscle mass (kg) / height (m2)). Fat free mass, total body water,

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and percent body fat were all provided by RJL Systems and validated using NHANES III
data (Chumlea et al., 2002). Two new variables referred to as fat mass index (FMI) and
fat free mass index (FFMI) were constructed from available data to create a measure of
total body fat mass and a measure of total LBM, both on the same scale as the skeletal
muscle mass index (SMI) and used the same formula where FMI = total body fat (kg) /
(m2) and FFMI = fat free mass (kg) / (m2). Data regarding scores on the modified Kaiser
Physical Activity Survey were missing from available data sets for visits six through 10
and were therefore not included as a covariate in this study.

Study Sample Characteristics
At baseline (visit 6) there were 2,533 participants remaining in the SWAN study
who were on average 52 years old and comprised of 30% African-American, 49%
Caucasian, with the remainder of the sample consisting of Hispanic (11%), Chinese (9%)
and Japanese (2%) women (Table 1). Approximately 53% of participants reported
having night sweats and/or hot flashes (VMS) in the two weeks prior to visit 6.
Differences in group (none/any VMS) demographics at baseline were found for
race (p <.0001), education levels (p <.0007), and menopausal status (p <.0001).
Additionally, group differences were detected for all body composition measures except
for SMI (p = .1298). Quality of life scores were not significantly different between the
groups. At baseline, African-American participants (64%) were more likely to report
VMS and women who were either late perimenopausal or postmenopausal (61%) and
with less than a college degree (57%) were also more likely to report VMS. Those

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reporting VMS were significantly higher in body weight (p < .0001) and in all measures
of bodyfat (% body fat, total body fat (kg), fat mass index, hip waist ratio) with p <.0001.
Those reporting VMS also showed significant differences for measures of fat free mass
(FFMI, p = .0003; FFM, p = .0002; skeletal muscle mass, p = .0423). Only women
reporting symptoms at baseline were included in analyses for VMS with LBM (n=1179).
Table 1
Baseline Characteristics by symptom status, SWAN

Presence of Vasomotor Symptoms (VMS)
None
(% n )
Any (% n )
p
n
1179
(47%)
1354 (53%)
Age, yr (mean ± SD)
52.0 ± 2.7
52.0 ± 2.6
Race/Ethnicity (n )
<.0001
African-American
272
(36%)
494
(64%)
Chinese
118
(52%)
107
(48%)
Hispanic
144
(54%)
122
(46%)
Japanese
26
(67%)
13
(33%)
Caucasian
619
(50%)
622
(50%)
Education (n )
<.0007
≤ High School
225
(44%)
286
(56%)
Some College
349
(42%)
481
(58%)
≥College Graduate
595
(50%)
584
(50%)
Menopausal Status (n )
<.0001
Post menopausal
430
(42%)
591
(58%)
Late Perimenopausal
77
(32%)
163
(68%)
Early Perimenopausal
368
(50%)
372
(50%)
Premenopausal
54
(68%)
26
(33%)
Quality of Life Score (mean ± SD) 7.5 ± 1.7
7.4 ± 1.7
.0910
Body Composition (mean ± SD)
Weight (kg)
2

BMI (kg/m )
Body Fat (%)
Total Body Fat (kg)
Fat Mass Index (TBF/m2)
Fat Free Mass (kg)
Fat Free Mass Index (kg/m2)
Skeletal Muscle Mass (kg)
Skeletal Muscle Index (kg/m2)
Waist Hip Ratio (%)

74.3 ± 20.7
28.1 ± 7.3
36.8 ± 8.0
28.3 ± 13.2
10.75 ± 4.9
45.1 ± 7.4
17.1 ± 2.4
20.6 ± 3.3
7.8 ± 1.1
0.81 ± 0.07

78.4 ± 20.7
29.6 ± 7.3
39.1 ± 7.6
31.9 ± 13.9
12.03 ± 5.11
46.3 ± 7.6
17.5 ± 2.5
20.9 ± 3.3
7.9 ± 1.1
0.83 ± 0.07

<.0001
<.0001
<.0001
<.0001
<.0001
.0002
.0003
.0423
.1298
<.0001

Note. SWAN = Study of Women's Health Across the Nation; BMI = body mass index;
TBF = total body fat; FFM = fat free mass; SMM = skeletal muscle mass

**Data are presented as number (percentage), unless specified otherwise.
Numbers may not add up to reflect total n due to missing values at baseline.

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Correlation analyses. The continuous variables SMI, FMI, and FFMI at baseline
are summarized according to symptom group (0 = none, 1 = any) in Figure 2.

Figure 2. Association of body composition variables at baseline grouped by VMS with
symptoms = 1 represents any, and symptoms = 0 represents none.
SMI = skeletal muscle mass, FMI = fat mass Index, FFMI = fat free mass index

SMI showed a strong positive correlation to FFMI at baseline both for symptoms = none
(r0 (864) = 0.931, p <.0001) and symptoms = any (r1 (1143) = 0.933, p <.0001), and a

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moderate positive correlation to FMI (r0 (864) = 0.567, p <.0001) (r1 (1143) = 0.579,
p <.0001). FMI showed a strong positive correlation to FFMI at baseline for both groups
(r0 (864) = 0.820, p <.0001; r1 (1143) = 0.826, p <.0001). These results indicate that the
continuous variables are all positively correlated and specifically that there are no
significant differences in these relationships between the groups at baseline.
Association of means. Pearson correlation (Figure 33) of mean SMI to mean
FMI (calculated on a participant basis over visits 6 – 10) was moderately strong for
‘none’ (r0 (405) = 0.648, p <.0001) and relatively weak for ‘any’ with (r1 (611) = 0.559, p
<.0001). Mean FFMI was strongly correlated to mean FMI in both groups (r0 (405) =
0.860, p <.0001; r1 (611) = 0.829, p <.0001). Mean SMI was very strongly correlated to
both symptom groups for mean FFMI (r0 (405) = 0.942, p <.0001; r1 (611) = 0.923, p
<.0001). SMI, as nested within FFMI, continued to be very strong as expected and the
relationship of FFMI to FMI remained consistent over time. Deviation from baseline
correlations for SMI to FMI indicates the importance of time.

Figure 3. Correlation of Mean SMI to Mean FMI over visits 6-10 by symptom group
SMI = Skeletal Muscle Index, FMI = Fat Mass Index

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The average (mean) for variables SMI, FMI, and FFMI at each visit, depicted in
Figure 4, indicate the trend and association over the five-year time frame between the two
groups – any vs. none VMS.

Figure 4. Mean SMI, FMI, FFMI at each visit grouped by symptoms
SMI = skeletal muscle index, FMI = fat mass index, FFMI = fat free mass index

Hypothesis testing. Models were developed to address the three hypotheses and
are presented as odds ratios and 95% confidence intervals for incident VMS in Table 2.
Adjusted models included time-varying covariates of FMI, age, and menopausal status
and single-time variates of race/ethnicity and education. Quality of life scores were not
significant in any models and were not considered in further analyses. For H1 (VMS
associations with concurrent LBM) results are summarized in Model 1. LBM is
significant in both the unadjusted (p <.0001) and adjusted (p = .036) analyses. For H2
(VMS associations with LBM over time since baseline), within woman percentage

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change since baseline was added to Model 1 and results are presented as Model 2. Again,
LBM showed significance in both unadjusted (p = .001) and adjusted (p <.001)
modelling. For H3, (VMS associations with LBM over time since prior visit) within
women percentage change since prior visit was added to Model 1 and the results are
summarized as Model 3. LBM remained significant in unadjusted (p = .009) and
adjusted (p = .003) models respectively.

Additional analyses. Participant means for SMI and FFMI (calculated over visit
6-10) had significant associations for VMS (p = .007, p = .023) while still considering
mean FMI (over visit 6-10) as reported in Model 4. FMI was significant in adjusted
models 1 (p =.0003), 2 (p <.0001), 3 (p <.0001), and 4 (p =.0073). Race/ethnicity was
significant in Models 1 (p <.0001) and 2 (p =.0139) but failed to reach significance for

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Model 3 or 4. Menopausal status was significant (p =.0052) in Model 4 only. Age was
not significant in any model. In mixed regression models (Proc Mixed, SAS) of VMS
and LBM repeated for a fixed factor of time (symptoms as random effect), results were
significant (p < .0001) for least squares means at visit (time) 6, 7, 8, 9, and 10.
Additionally, significance (p <.0001) was found for the difference of least square means
(Tukey-Kramer method) at visit 6 (time effect 6-7, 6-8, 6-9, 6-10), and for time effect
visit 8-10 (p = .007) (data not shown). Further, estimated predicted probabilities were
examined to determine the likelihood of LBM to predict the development of VMS.
Predicted probabilities. Given the binary nature of the outcome variable
(VMS), binary logistic regression was used to estimate the predicted probabilities of
symptoms occurring specifically in relation to SMI (with FFMI as covariate and FMI
constant at the mean). Figure 5 illustrates the probabilities estimated for given levels of
SMI at each of the four visits following baseline, including the average (mean) over time,
for women reporting no VMS at visit 6 (n = 1179). The probability of reporting VMS
shows a negative correlation to increasing levels of SMI with overall association
decreasing from 71% to 37% as SMI increases from 4 to 14 (kg/m2).

Figure 5. Predicted probability of symptoms occurring at given levels
of SMI (adjusted for FFMI and FMI) in women with no VMS at baseline

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Discussion
This study is among the first to examine the relationship of LBM to VMS
longitudinally. Using the data for this large, multiethnic sample of mid-age women from
the SWAN study, we found that participants with higher relative levels of LBM were less
likely to develop VMS as they transitioned through menopause. This effect was found to
be independent of sociodemographic factors and levels of bodyfat.
Over the duration of the SWAN study period (visits 6-10), the average age of the
participants increased from 52.0 to 56.6 years and the number of women who were
postmenopausal increased from 40% (n = 430) at visit 6 to 72% (n = 733) at visit 10
(Figure 6). VMS are most likely to be experienced by women in the late perimenopausal
and postmenopausal period (Thurston & Joffe, 2011), thus the time frame used for this
research was able to document the movement through this important transition for the
majority of the cohort.

Figure 6. Menopausal status of study cohort by visit
(numbers do not add to 100% due to surgical menopause or HT use)

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The increasing levels of significance for the three models measuring VMS with
concurrent LBM, percent change since prior visit, and percent change since baseline,
suggests that the effect of time on the likelihood of VMS is considerable. At individual
visits after baseline, with only those women reporting VMS = none at visit 6, simple
models showed that the effect of LBM was not significant. It is only after the women had
continued to progress through to visit 10 with no symptoms that the effect of time
became significant, suggesting that it is the maintaining of higher levels of LBM that may
offer protection against the probability of VMS. In fact, as shown in Figure 4, the mean
SMI and mean FFMI levels per visit were inverted at baseline (VMS = any show slightly
higher mean levels than VMS = none). However, by visit 7, the VMS = none group
show consistently higher levels of lean mass than VMS = any and the groups begin to
significantly diverge by visit 10. After the cohort was regrouped for analysis at visit 6,
the percentage of women in the VMS = none group remained fairly steady around 60%.
Figure 7 depicts this shift in the VMS groups from visit 6 (n = 2005) through to visit 10
(n = 800) which suggests that the data is not skewed by the characteristics of the
remaining women in the cohort.

Figure 7. Percentage of women by VMS group at each visit

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To understand why lean mass may potentially act as a predictor of VMS, previous
research on both body fat and oxidative stress must be considered. Considerable recent
research has focused on the association of BMI and bodyfat with VMS and has suggested
a positive correlation between increasing BMI and the presence of VMS (da Fonseca et
al., 2013; Gallichio et al., 2014; Gjelsvik et al., 2011; Herber-Gast et al., 2013;
Kazlauskaite et al., 2015; Thurston et al., 2013). However, BMI is considered a poor
predictor of body mass as it is merely a measure of excess weight and does not
distinguish between body fat mass and fat free mass (Akindele, Phillips, & Igumbor,
2016). This study examined the individual components of body composition and found
lean mass, determined by both fat free mass and skeletal muscle mass (both indexed to
height), to have a significant effect on the likelihood of developing VMS over time while
still considering the potential impact of fat mass as illustrated by the significant gap in fat
mass seen between groups in Figure 4. A recent cross-sectional study of 758 women
found that trunk lean mass was an independent protective factor for moderate to severe
menopausal symptoms and that VMS were independently related to higher BMI and fat
mass (Zhou et al., 2018). Other research has shown substantial effects of adiposity on the
magnitude of hormone changes experienced during menopause (Wildman & Sowers,
2011), but the underlying mechanisms of the relationship between body composition and
VMS are not entirely clear due to the incomplete understanding of the physiology of
VMS (Moilanen et al., 2012).
Evidence is emerging on the role of oxidative stress in menopause and its
relationship to muscle mass. Lipoperoxide (LPO) levels are considered a measure of
oxidative stress and in postmenopausal women are found to be significantly higher than

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in premenopausal women suggesting that the depletion of estrogen is a risk factor for
oxidative stress (Sanchez-Rodriguez et al., 2012). A recent study showed that the loss of
muscle mass in menopause, due to declining estrogen levels, was negatively associated
with oxidative stress (LPO), but that skeletal muscle mass was positively associated with
serum uric acid which offered a protective role against oxidative stress due to its capacity
to clear reactive oxygen species (Zacarias-Flores et al., 2018). Cagnacci et al. (2015)
found that increased oxidative stress also impaired the ability of free oxygen radical
defenses (FORD) in menopausal women, and that VMS are negatively associated with
FORD. Together, these results suggest that declining estrogen in menopause contributes
to loss of muscle mass which simultaneously increases oxidative stress and decreases
antioxidant levels potentially leading to higher probability of VMS. The results of our
study add to the evidence that women with lower levels of LBM are more likely to
experience VMS. LBM and skeletal muscle have also been found to be protective
against weight-associated insulin resistance and metabolic abnormalities - conditions
which plague menopausal women (Fukushima et al., 2016; Takamura et al., 2017).
Research on resistance training as an intervention for menopausal women has
shown positive correlations to metabolic markers and quality of life as well as increases
in muscle mass (Alvarez & Campillo, 2013; Boganha et al., 2012; Conceicao et al., 2013;
Correa et al., 2015; Leite et al., 2010). Martins et al. (2018) were able to show, in a
randomized controlled study, that high intensity interval body weight training was as
effective as combined aerobic and resistance training in changes to muscle mass, physical
performance, inflammatory markers, and metabolic health in postmenopausal women.
This type of training is shown to be highly efficient and time saving helping to overcome

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barriers of lack of time or equipment and can also be configured for a lower motor skill
level compared to traditional high intensity interval (HIIT) training (Martins et al., 2018).
However, research on the impact of resistance training and increased muscle mass, most
specifically on VMS, is missing and the lack of longitudinal studies has left a gap in the
research. As the results from this study show, it may be the maintenance of lean mass
over time that proves optimal for prevention of VMS, thus further studies in this area are
needed.
The women in the SWAN study were between ages 42-52 when they were
recruited in 1996-97. The American College of Sports Medicine (ACSM) conducted the
National Health Interview Survey which analyzed resistance training trends over the
period of 1998-2004 (Kruger, Carlson, & Kohl, 2006). The study showed that women in
the age group 45-64 years increased their participation in weight training from 12.3% of
respondents to 17.6%, and while this was a significant increase, it was still well below the
national health objective for 2010 which was 30%. The current North American culture
finds that younger women today are more accepting of resistance training as an important
piece in maintaining their health (Hurley et al., 2018). This may translate into future
generations of women who are more likely to carry higher levels of lean mass into
midlife and potentially experience fewer menopausal symptoms. For those who did not
have the benefit of this culture shift and faced barriers to exercise in the past, a study by
Diniz et al. (2015) found that postmenopausal women who spent more than 150 minutes
per week in moderate-vigorous physical activity showed higher levels of lean body and
leg mass which suggests that for women currently entering menopause, adding strength
training to their exercise regimen could still provide benefits.

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Certain limitations need considered when interpreting results of the current study.
The use of BIA measurement involves several assumptions, and while skeletal muscle
mass was calculated using a validated equation by Janssen et al. (2000) and fat mass and
fat free mass were calculated with validated equations by Chumlea et al. (2002), this
allows for potentially differing interpretation of data supplied by the internal BIA system.
Additionally, VMS were assessed through responses to two questions regarding number
of days experiencing hot flashes or night sweats in the previous two weeks. This data
yielded limited information and is subject to recall bias although this population was
found to have high specificity and high sensitivity regarding VMS recall (Crawford et al.,
2008). Although frequently used in this type of epidemiological study, future research
should attempt to consider more objective measures of VMS. Multiple statistical
comparisons were made during this study. As a result, some observed associations may
have occurred by chance or represent other uncontrolled variables. Moreover, odds ratios
presented here should not be interpreted as measures of relative risk because VMS are not
a rare outcome and the risk may be overestimated.

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Conclusion
This study was the first to examine the longitudinal association of LBM to VMS,
providing new evidence that lean mass may provide protection against the development
of VMS as women transition through menopause. Using the longitudinal SWAN
database that encompasses a large, multiethnic sample of women from across the United
States, these findings are particularly relevant. Given the current backlash against
hormone replacement therapy as a means to mitigate symptoms, these results provide for
the possibility of symptom prevention through exercise. Importantly, the results of this
study suggest that the greatest contributing factor to mitigating symptoms was
maintaining LBM throughout the menopausal transition. This finding implies the need to
encourage all women, especially premenopausal to post-menopausal, to incorporate
resistance/strength training into their exercise programs on a regular and consistent basis,
and to continue with this type of training as long as they are healthy.

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Future Directions for Research
Further and more complete understanding of the etiology of VMS and the
underlying mechanisms will allow continued development of methods for managing
bothersome, and sometimes debilitating, symptoms during this transitional time in a
woman’s life. Additional longitudinal studies are needed to support and expand on the
benefits of lean mass for postmenopausal women which could also provide additional
benefits related to managing the impact of metabolic syndrome and insulin resistance
frequently seen in this population. Specifically focusing on research involving increased
lean mass through exercise, in early menopause or during child bearing years, could offer
untold opportunities for educating future generations on symptom management and
prevention.

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Appendix A
Review of the Literature

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Menopause is said to have occurred after twelve months of amenorrhea due to the
cessation of ovarian follicular function and decreasing estrogen levels (Greendale, Lee &
Arriola, 1999). Fluctuating estrogen levels in the years preceding menopause create an
impact on female physiology and women begin to experience a variety of physical and
psychological symptoms. These symptoms include hot flashes, nights sweats, sleep
disturbances, depression, irritability, headache, and urogenital issues, all of which can
disrupt daily functioning (Woods & Mitchell, 2011). These symptoms can persist over
the course of 20 years and can severely impact quality of life (Gjelsvik, Rosvold, Straand,
Dalen & Hunskaar, 2011; Huang et al., 2010; Sternfeld & Dugan, 2011). The aging of
the large baby boom cohort with a combination of low fertility and increasing life
expectancy has led to an aging female population in Canada (Statistics Canada, 2016).
This large cohort, born between 1946 and 1965, was 45 to 64 years old in 2010: They
made up about 28% of the overall female population in that year, representing almost five
million women (Statistics Canada, 2016). Given the potential long-term duration of
symptoms impacting this large at-risk population, there is a growing need to find nonpharmacological treatments for menopause symptoms. Exercise has been suggested as a
low-cost alternative that may also provide additional health benefits. Additionally, the
role of muscle mass and its impact on symptoms and the development of menopauserelated diseases is incompletely understood. This literature review will examine the
pathophysiology and clinical data regarding menopause, hormone therapy and the
association to disease, then more closely review the current literature on the effects of
exercise on menopause symptom management and the role of muscle mass in
menopausal women.

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Menopause

Pathophysiology and Reproductive Aging
Menopause is a significant event in most women’s lives as it marks the end of the
natural reproductive life. For most women, menopause will occur between the ages of 40
and 58 years with the average being 51 years (Zapantis & Santoro, 2003). The timing of
menopause globally is relatively constant; however, both the nature and severity of
symptoms varies substantially between women from different ethnicities and
geographical locations for reasons that are not completely understood (Roberts & Hickey,
2016). The Stages of Reproductive Aging Workshop +10 (Harlow et al., 2012) classifies
the transition through menopause into five stages through early menopause to late post
menopause.
The first two stages are early menopausal (variable length of time) and late
menopausal (1-3 years) which both precede the final menstrual period (FMP). The
menstrual cycle is beginning to vary in length and hormone levels can see extreme
fluctuations. Symptoms, including vasomotor symptoms, are likely to occur in the late
menopausal stage. The FMP marks the start of the postmenopausal stages. The first two
years of post-menopause, known as early post menopause, see a continued decline in
estradiol that will eventually stabilize toward the end of this stage. Symptoms are most
likely to occur during this stage and throughout the next stage of late post-menopause
which can last from 3-6 years. Late post menopause is the time when further hormonal

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changes are limited, and the effects of somatic aging become of more concern leading to
urogenital symptoms (Harlow et al., 2012).

Epidemiology and Prevalence of Symptoms
The most frequent symptoms during menopause are hot flushes, night sweats,
depression, irritability and sleep disorders (Skrzypulec, Dabrowska, & Drosdzol, 2010).
About 80-96% of women experience mild to severe physical or physiological
menopause-related complaints as they approach menopause due to declining estrogen
levels (Moilanen et al., 2012). A recent systematic review suggested that women with
more negative attitudes towards menopause in general report more symptoms during the
menopausal transition (Ayers, Forshaw, & Hunter, 2010). A longitudinal cohort study
found that smoking was significantly associated with more symptoms and an earlier
menopause age, while other life style factors, like body mass index (BMI) or physical
activity (PA) before menopause, have no significant correlation with the prevalence or
burden of menopausal symptoms (Gjelsvik, et al., 2011). Other changes that occur
during the menopause transition are changes in body composition with an increase in
abdominal fat mass, as well as associated alterations in cardiometabolic risks due to
hormone-related decreases in energy expenditure and fat oxidation (Lovejoy,
Champagne, de Jonge, Xie & Smith, 2008). Duval et al. (2013), found during the fiveyear longitudinal Montreal-Ottawa New Emerging Team (MONET) study, that
menopause is accompanied by a decrease in energy expenditure (EE) which is mainly
due to a decrease in physical activity EE and a shift to a more sedentary lifestyle. The
physiological complexity of the menopause transition and its many effects on the female

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body, as depicted in Figure 8, is not completely understood and warrants continued
research in this area.

Figure 8. Symptoms and deleterious effects of menopause.
LDL, low density lipoprotein; VLDL, very low-density
lipoprotein; TGL, triglycerides; HDL, high density lipoprotein;
IL-6, Interleukin 6; IL-1, Interleukin 1; TNF, tumor necrosis
factor. Adapted from R.D. Leite, J. Prestes, G.B. Pereira, G.E.
Shiguemoto, and S.E.A. Perez, 2010, Menopause: Highlighting
the effects of resistance training. International Journal of Sports
Medicine, 31, p. 762.

Physiology of Vasomotor Symptoms
Hot flashes and night sweats are the most common symptoms of menopause
(collectively referred to as vasomotor symptoms) and are reported as feelings of intense
warmth along with sweating, flushing (in the face, neck and chest) and chills (Gallichio et
al., 2014). The median duration of vasomotor symptoms (VMS) is about four years for
most women, however, some women experience them for as long as 20 years (Feldman,
Voda, & Gronseth, 1985). Research has suggested that lower levels of estrogen appear to

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narrow the thermoneutral zone – the internal temperature zone between sweating and
shivering (Freedman, 2014). Women will sweat more easily when their core temperature
rises if they have a narrow thermoneutral zone, but if all woman will experience a decline
in estrogen and not all women have hot flashes, this must not be the only mechanism to
trigger them (Kronenberg, 2010). Freedman (2014) suggested that an increase in brain
norepinephrine, which is also known to reduce the thermoneutral zone, in conjunction
with a decrease in estrogen, is part of the etiology of hot flashes. His theory hypothesized
that hot flashes are triggered by elevations in core body temperature acting within a
greatly reduced thermoneutral zone in menopausal woman (Freedman, 2014).

Treatments for Menopausal Symptoms
Per the most recent position paper of the North American Menopause Society
(NAMS, 2015), surveys suggest that 50-80% of menopausal women use non-hormonal
therapies for vasomotor symptoms. The paper recommends cognitive behavioral therapy,
clinical hypnosis, along with low- dose paroxetine salt, and FDA approved selective
serotonin reuptake inhibitors and serotonin-norepinephrine reuptake inhibitors as the only
methods supported by level 1 evidence (Oxford Centre for Evidence-Based Medicine,
2011). Exercise, while considered important to other domains of health and may prove to
be important in the treatment of other menopausal symptoms, is lacking in clinical
evidence to support use for VMS management (Daley, Stokes-Lampard, Thomas &
Macarthur, 2014).

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Measurement of Menopausal Symptoms
Although there are numerous variations available, the most frequently used
instruments for measuring menopause symptoms are the Menopause Specific Quality of
Life (MENQOL), Greene Climacteric Scale and the SF-36. The MENQOL is a validated
questionnaire for the assessment of degree of bother for menopausal women’s symptoms
and an effective instrument, although more research is needed to confirm validity in
different cultures and countries (Jenabi et al., 2015). The Greene Climacteric Scale is a
self-reported questionnaire that measures degree of bother for 21 menopause specific
physical and psychological symptoms (Greene, 2008) and the SF-36 is a 36-item shortform designed for general use in clinical practice and research, health policy evaluation
and general population surveys (Jenabi et al., 2015). The Menopause Symptoms’
Severity Inventory (MSSI-38) is a newer instrument that measures severity of symptoms
by evaluating both frequency and intensity of symptoms, but this instrument is not yet
validated for clinical samples (Pimenta et al., 2012). The Kupperman Index is a
numerical index that also scores 11 menopausal symptoms but does not address quality of
life or symptom burden. The Women’s Health Questionnaire (WHQ) was specifically
developed to measure the perceptions of a range of physical and emotional symptoms in
middle-aged women allowing perceived quality of life to be measured. It is important to
have specific instruments that are capable of measuring symptoms and health related
quality of life in menopausal women because perceived severity of symptoms and overall
burden may vary significantly between women and uniquely impact quality of life.

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At least one study was able to objectively measure severity of hot flashes. Bailey
et al. (2016) used tube-lined jacket and trousers to cover the entire body and exposed
participants to a mild heat stress by perfusing heated water through the suit. Heart rate,
core body temperature, blood pressure and local sweat rate were all recorded at baseline
and after 16 weeks of intervention. This method of physiological measurement requires a
significant lab setting and highly trained technicians but provides valuable research data
that is not always accurately captured through self-report diaries or survey instruments
which can be subject to recall bias.

Hormone Therapy Contraindications
In 2002, the National Institutes of Health put an early stop to randomized control
trials, called the Women’s Health Initiative (WHI), testing the association of menopausal
hormone therapy (MHT) with decreased risk of coronary heart disease due to
implications that there was an increased risk of breast cancer, coronary heart disease,
stroke and pulmonary embolism in women with an intact uterus (Rossouw, Manson,
Kaunitz & Anderson, 2013). Prescriptions for MHT dropped dramatically after the
findings received wide spread attention and continued to decline up to 2010 (Ettinger et
al., 2012). In the most recent update to the study’s findings, the current recommendation
is that short term, low dose MHT (oral or transdermal) can be used for treatment of
moderate to severe VMS in healthy women with an intact uterus soon after menopause
onset (Ettinger et al., 2012).

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Association of Menopause with Health Conditions

Metabolic Syndrome
The menopausal transition is accompanied by metabolic changes and the
increasing prevalence of metabolic syndrome (MetS) which is defined by the cooccurrence of abdominal obesity, hypertension, dyslipidemia and insulin resistance
(Stefanska, Bergmann, & Sypniewska, 2015). MetS is similarly associated with
development of type 2 diabetes and cardiovascular disease (O’Neill & O’Driscoll, 2015).
Globally it is estimated that 30-55% of post-menopausal women fulfill the diagnostic
criteria for MetS (Stefanska et al., 2015). This high incidence of MetS is induced by
several factors including sedentary lifestyle, diminished exercise capacity, weight gain
and central obesity, and is facilitated by low levels of estrogens, dyslipidemia, low
glucose tolerance, elevated blood pressure and an increase in the pro-inflammatory state
(Wang et al., 2014). There is still debate as to whether the increased incidence of MetS is
due to the hormonal changes which accompany menopause or normal aging; however, a
recent large population-based study showed that chronological age and menopausal status
are independently associated with cardiovascular risk factors (de Kat et al., 2017).
As they transition through menopause, women will experience a decrease in basal
metabolic rate and a loss of lean muscle tissue which increases the risk of weight gain
and obesity (Lovejoy, 2009). Weight gain at midlife is frequently reported by women;
studies have shown that an increase in subcutaneous adipose tissue is related to age
whereas the increase in visceral and total body fat is related to the abrupt decline in
estrogen (Abdulnour et al., 2012, Lovejoy et al., 2008). This accumulation of abdominal

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fat in postmenopausal women appears to be a critical factor in the development of insulin
resistance, which in turn is a major risk factor for type 2 diabetes (Lobo et al., 2014). In
their longitudinal study, Kabat et al. (2014), found that BMI, waist circumference and the
waist circumference to height ratio robustly predicted change in cardiometabolic risk in
post-menopausal women with the strongest associations seen for increased triglycerides,
glucose, and reduced high-density lipoprotein (HDL) cholesterol.
For postmenopausal women, chronic systemic inflammation, oxidative stress,
abdominal visceral adipose tissue, dyslipidemia, sarcopenia, and a sedentary lifestyle are
all risk factors for MetS (Mendoza et al. 2016). The complicated interactions between
these risk factors are illustrated in Figure 9. However, both aerobic and strength training
have been shown to partially counteract these changes by increasing anti-inflammatory
responses, antioxidant enzyme expression, and insulin sensitivity, and by reducing
adipose tissue (Glouzon et al, 2015).

Figure 9. Relationship between MetS components and cardiometabolic
complications. Adapted from Stefanska, A., Bergmann, K., & Sypniewska, G.
(2015). Metabolic Syndrome and Menopause: Pathophysiology, clinical and
diagnostic significance. Advances in Clinical Chemistry, 72(1) p. 9

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In the Dose-Response to Exercise in Women (DREW) control study, authors
found a dose-response relationship between weekly exercise energy expenditure,
prevalence of MetS and overall cardiometabolic risk factors in a large, randomized
population of postmenopausal women (Earnest et al., 2013). The results of this study
indicated that exercise training at or above (100% or 150%) the public recommendations
for physical activity improved the components of MetS in postmenopausal women and
that even at a reduced level of activity (50%), small improvements were recorded
(Earnest et al., 2013).

Cardiovascular Disease
In their systematic review and meta-analysis, Muka et al. (2016) concluded that
women who experience VMS and other menopausal symptom are at a greater risk for
developing cardiovascular disease. The cardiovascular risk factors are generally
considered to include: total cholesterol levels, hypercholesterolemia, systolic and
diastolic blood pressure, hypertension, and BMI (Stefanska et al., 2015). In a populationbased study involving 5,523 women, Gast et al., (2008) found that women with
vasomotor complaints have an unfavorable cardiovascular risk profile with increased
cholesterol levels, diastolic and systolic blood pressures and BMI. Many studies to date
have reviewed the association between VMS and cardiovascular disease, however, a
2012 study examined whether menopausal symptoms other than VMS were associated
with biochemical and biophysical risk factors (Cagnacci et al., 2012). The data seemed
to indicate that menopausal symptoms may represent stressful events associated with
endocrine modifications, such as an increase in cortisol, leading to insulin resistance and

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to an increased metabolic risk for cardiovascular disease (Cagnacci et al., 2012). A more
recent study by Cagnacci et al. (2015) found that the symptoms of menopause may be
associated with biochemical risk factors which begin to appear early in menopause and
are also related to cardiovascular disease. Using multivariate analysis, the study
compared the scores of 590 participants on Greene’s climacteric scale to cardiovascular
risk factors which included blood pressure, fasting glucose, fasting lipids.

Sarcopenia
Loss of muscle mass and function normally associated with aging is referred to as
sarcopenia. In women, sarcopenia develops and is highly prevalent during menopause,
and is thought to be related to the decline in estrogen with subsequent detrimental effects
on skeletal mass and strength (Lee & Lee, 2013). A review by Messier et al. (2011)
concluded that after the fifth decade of life, declining estrogen may play a role in
declining muscle mass in postmenopausal women, however, sarcopenia is complicated in
that it involves hormonal, biological, nutritional and physical activity mechanisms.
Figure 10 represents the complex association of menopause related health changes and
sarcopenia.

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Figure 10. Menopause-related changes on muscle mass and its impact on
functional status. Adapted from Messier, V., Rabasa-Lhoret, R., Barbat-Artigas,
S., Elisha, B., Karelis, A.D., Aubertin-Leheudre, M. (2011). Menopause and
sarcopenia: A potential role for sex hormones (2011). Maturitas 68, pg. 334.

A recent cross-sectional study of 148 women assessed biomarkers of metabolic
risk through blood tests and used Kupperman Index scores to measure symptom severity
then correlated those scores with muscle mass determined by Dual-energy X-ray
Absorptiometry and hand grip strength measured with isometric dynamometry (Lee &
Lee, 2013). Muscle quality was calculated as the ratio of strength to muscle mass in the
upper extremities. The authors found that muscle strength and quality (but not muscle
mass) may be related to the severity of menopausal symptoms due to declining sex
hormones and the resulting increase in oxidative stress (Lee & Lee, 2013). The authors
also suggest that there is a vicious cycle of increased oxidative stress during menopause
that may induce the loss of muscle strength, and the loss of muscle strength, in turn,
increases oxidative stress (Lee & Lee, 2013). Postmenopausal women have been shown

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to have significantly higher oxidative stress blood marker levels and lower antioxidant
capacity relative to premenopausal women (Signorelli et al, 2006) which is thought to be
correlated to their higher body weight (Mittal & Kant, 2009). A review by Maltais et al.
(2009) found that the menopausal transition is associated with a decline in estrogen,
growth hormone, IGF-1, and DHEA, a decrease in muscle protein synthesis, and an
increase in catabolic factors such as inflammation. The authors concluded that low
physical activity, inadequate protein intake, and elevated oxidative stress are the greatest
contributors of sarcopenia in postmenopausal women and suggest that resistance training
may be the key strategy to maintain muscle mass (Maltais et al., 2009).
Interestingly, untrained obese women with low muscle strength (as measured by
hand grip strength) were shown to have lower fasting glucose, lower fasting insulin,
lower diastolic blood pressure and insulin sensitivity significantly higher than those with
normal muscle strength, although both groups had similar lean body mass (BarbatArtigas et al., 2012). Glouzan et al. (2015) also determined that muscle mass in
overweight-to-obese postmenopausal women is correlated with insulin resistance in a
study involving forty-eight sedentary women who followed a six-month mixed exercise
intervention. However, the implication is not that resistance training is not beneficial, but
that genetically determined lean body mass may be related to an android pattern of body
fat, and that the benefits of exercise-induced increases in lean body mass on insulin
sensitivity would outweigh any adverse effects of genetic predisposition of lean body
mass.
In considering the prevention of sarcopenia and interventions directed at
menopausal women, methods of identifying at-risk women become important. A large

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cross-sectional study of 2,152 women positively correlated leukocyte counts (measure of
inflammation) with higher risk of sarcopenia in postmenopausal women noting that
higher leukocyte counts are also associated with cardiovascular disease, type 2 diabetes
and MetS (Chung et al., 2016). The importance of muscle strength in everyday life
would suggest the need to find effective strategies to prevent its loss. Evidence suggests
that resistance exercise and proper nutrition are important to maintain or prevent the
accelerated loss of muscle strength due to aging and potentially, menopausal status
(Maltais et al., 2009)

Role of Physical Activity During Menopause

Benefits of Physical Activity
Physical activity (PA), including regular exercise, has a range of health benefits
including improved cardiovascular function, reduced blood pressure, reduced blood
triglyceride levels and improved blood glucose metabolism, reduced abdominal fat, and
improved weight control (World Health Organization, 2010). The mechanism through
which PA may be beneficial for menopausal symptoms is not well understood, however,
Dugan and Sternfeld (2011) proposed several possible mechanisms which include
neuroendocrine, thermoregulation, body composition, and psychological effects. These
mechanisms have been examined in various studies.
A recent randomized controlled trial examined the effects of a long term (+12
months) mixed exercise program in 158 postmenopausal women and found positive

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changes in basal metabolic rate, skeletal muscle mass, percent body fat and other
anthropometric and body composition variables with no significant interactive effects
between exercise and menopausal symptoms (Aragão et al., 2014). In another
randomized controlled trial of 325 postmenopausal women, a greater reduction in waist
circumference occurred in participants with higher habitual physical activity combined
with aerobic training than those with a lower habitual physical activity (Swift et al.,
2012).
Physical activity has also been shown to be effective in attenuating symptoms in
the emotional and psychological aspects of menopause as measured by health-related
quality of life (Kishida & Elavsky, 2015; Moratalla-Cecilia et al., 2016; Skrzypulec et al.,
2010; Sternfeld & Dugan, 2011). In a cross-sectional study, menopausal women who
were physically active reported less severe symptoms in the areas of mood, energy and
concentration (Haimov-Kochman et al., 2013). This study also suggested that women
who exercised regularly felt better and had fewer complaints in a dose-dependent
manner, in that the more frequent the exercise, the less severe the reported symptoms
(Haimov-Kochman et al., 2013).
In their longitudinal study, Kishida and Elavsky (2015) found that menopausal
women who engaged in physical activity experienced greater positive affect and
enhanced coping efficacy which helped to reduce the symptom burden and improve
quality of life. Additionally, in an eight-year follow up study of 1,165 women aged 4564, global quality of life improvement was correlated with stable or increased levels of
physical activity (Moilanen et al., 2012). Evidence suggests that regular physical activity
is beneficial to menopausal women and using data from a six-month randomized

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controlled trial (n=151), Kolu et al. (2015) found that it is also cost-effective for
improving cardiorespiratory fitness, lean muscle mass, and QALY (quality adjusted life
years) in relation to exercise cost (gym membership, trainer fees, etc.), health care cost
and productivity cost.

Barriers to Participation in Physical Activity
Unfortunately, as women age, PA tends to decline, with Statistics Canada
reporting that in 2012/2013 only 18% of women aged 40-59 years were meeting the
recommended guidelines of 150 minutes of moderate to vigorous intensity aerobic
physical activity per week (Statistics Canada, 2016). A Canadian study compared the
quality of life in postmenopausal women of those achieving the recommended amount of
PA versus those who did not and found that women meeting the public health guidelines
had higher scores in health-related quality of life and satisfaction with life and reported
fewer symptoms of depression and anxiety (Vallance et al., 2010). Another study of
2,606 Finnish women also reported that women who meet the public recommendation for
leisure time physical activity had better menopausal and global quality of life
(Mansikkamaki et al., 2015).
The increasingly sedentary lifestyle among menopausal women is often ascribed
to their perceived barriers to exercise. Common barriers for middle-aged women
considering initiation of exercise are lack of time, safety concerns about exercising
outdoors, weather, and not having a family member or friend to exercise with (Grindler &
Santoro, 2015). The three most common barriers interfering with adherence to exercise

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were disruptions in daily structure, competing demands on time, and self-sacrifice
(Grindler & Santoro, 2015; McArthur, Dumas, Woodend, Beach & Stacey, 2014).
Interestingly, McArthur et al. (2014) did not find that experiencing menopausal
symptoms itself, was considered a factor in exercise adherence but that the majority of
factors were related to the psychosocial aspects of this mid-life stage. A cross-sectional
study conducted in Australia found that positive perceptions about the benefits of
exercise, self-efficacy for exercise, and physical and mental well-being were the key
factors that predict postmenopausal women’s perceptions of barriers (McGuire et al.,
2016). A prospective cohort study of 216 women followed for five years showed the
most frequently cited barriers to exercise were lack of time and lack of motivation at
baseline, but at the five-year mark the lack of time barrier was no longer a significant
factor (Ball et al., 2016). The authors suggest the change over time correlated to the age
of the women as most were near retirement age at baseline which may have led to more
flexibility in schedules of the participants. Ball et al. (2016) also found that women with
a higher BMI at baseline were more likely to reduce their BMI over time and those at
normal BMI at baseline were more likely to gain weight although there was no
statistically significant difference in changes to physical activity.
Menopausal women’s adherence to regular exercise is the result of a complex
interplay of social, emotional, environmental, and psychological factors (McArthur et al.,
2014). As the presence of symptoms itself does not appear to be a barrier, women should
be encouraged to make exercise a priority through assessment of the barriers and
enabling factors that are unique to them. Most women already believe that they “should”
be more active so the challenge is to translate that into motivation for action as opposed

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to it being perceived as another demand on their time and then manifests internally as a
lack of ability to follow through.

Exercise Training for Symptom Management

Cardiovascular (Aerobic) Training
As noted above, physical activity is a beneficial and cost-effective method to help
women cope with the symptoms of menopause. The evidence regarding the effectiveness
of exercise to ameliorate symptoms, however, has been contradictory and there is a clear
delineation in the literature between VMS and other symptom types.
First, we will examine the studies which did not find exercise effective for VMS.
A 2014 random control trial of 157 postmenopausal women reported reductions in most
symptoms, excluding VMS, with improved blood lipid profiles after 12 weeks of walking
with strides three times per week (Zhang et al., 2014). The study authors developed a 16item questionnaire that, together with the modified Kupperman Index, was completed by
participants at weeks 4, 8, and 12. The study found significantly lower (P < 0.05) scores
at week 12 for the Kupperman Index, weight, BMI, waist circumference, triglycerides
and total cholesterol as compared to baseline and controls. A second random control trial
of 248 women was conducted as one arm of a 3 x 2 factorial trial called the Msflash
study. The exercise intervention consisted of individual, facility-based aerobic training
(treadmill, elliptical or bike) three times per week for 12 weeks. Target heart rate was
monitored throughout with a goal of 50-60% of heart rate reserve for the first month and

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60-70% for the remainder of the intervention. VMS frequency and bother were recorded
in daily diaries for seven days at baseline, and weeks six and twelve. Sleep quality and
sleep disturbances, insomnia symptoms, depressive symptoms and anxiety were
measured at baseline and week 12 through validated generalized self-report surveys. The
authors found that the 12 weeks of aerobic exercise did not result in a statistically
significant reduction in VMS but led to small improvements in sleep quality, insomnia
and depression (Sternfeld et al., 2014).
A further random control trial involved 261 postmenopausal women randomized
to one of two exercise groups or control for a 24-week study. Participants were given an
initial goal of progressing towards 30 minutes of moderate intensity exercise on at least
three days per week. The definition of moderate intensity was an increase in breathing
rate, an increase in heart rate to the level where they could feel their pulse and an increase
in body temperature and they were given examples of what types of activities would
constitute moderate intensity. For weeks 12-24, participants were encouraged to increase
activity to 30 minutes on 3-5 days per week. Both exercise groups received counselling;
one group also received a DVD of case studies on women with hot flashes and their
motivation to exercise, while the other group were invited to take part in three exercise
support groups. The primary outcome was the frequency of hot flashes and was
measured at baseline and 6 months using the hot flush rating scale. All participants were
asked to complete prospective seven-day hot flush/night sweat diaries at weeks 7, 14, and
21 recording both frequency and severity, and exercise groups were also asked to
complete 7-day prospective exercise logs that detailed amount and type of exercise
completed. A random 50% sample of all participants were asked to wear heart rate

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monitors for five consecutive days at baseline and at 6 months to provide objective data
on physical activity. A total of 50% of the hot flush diaries were completed and returned
and a total of 47% of the exercise diaries were completed and returned with the most
common type of exercise being walking, aerobics classes and swimming. The authors
concluded that 30 mins of moderate intensity exercise three days per week did not result
in significantly fewer hot flashes/night sweats at six-month or 12-month follow ups
(Daley et al., 2015).
A 2014 Cochrane Review found only five eligible studies reviewing the effect of
exercise on VMS (Daley et al., 2014). Studies that had been included in the previous
edition of this review were excluded based on having participants at baseline who were
not symptomatic. The five included studies for this review were all parallel-group
randomized controlled trials and included interventions of walking versus yoga versus
control, aerobics versus MHT, walking plus soy versus soy versus control, cardiovascular
conditioning versus yoga versus control, and walking plus education versus control plus
education. All studies assessed VMS by self-report with one using the Women’s Health
Questionnaire, another using the Greene Climacteric Scale, and two using the
Kupperman Index. Overall, the studies were considered to be of poor quality due to
limitations in study design, inconsistency and imprecision. The authors therefore
concluded that there was insufficient evidence to determine the effectiveness of exercise
on vasomotor symptoms (Daley et al., 2014).
In contrast to these, several studies have shown benefits of exercise on VMS and
other symptom domains. A longitudinal study randomized 176 postmenopausal women
in Finland to six months of an aerobic training intervention or control. The participants

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were asked to include unsupervised aerobic training for 50 minutes four times per week.
Instructions were given to exercise at rates of perceived exertion corresponding to 13-16
on a scale from 6-20 (approximately 64%- 80% of maximal heart rate). At least two
sessions had to include walking and the other two could include walking, Nordic
walking, jogging, cycling, swimming, skiing, aerobics or other gymnastic exercise.
Participants wore heart rate monitors during training and received feedback every two
weeks from an instructor based on data transferred from the monitors. Hot flushes were
the primary outcome as measured by the WHQ and secondary outcomes of all other
reported symptoms and their perceived disturbance level were also collected. Both
primary and secondary outcome data was captured using mobile phone questionnaires
twice per day thus minimizing recall bias (telephone-based questionnaire was not
validated but questions used were dichotomous). The study authors concluded that
aerobic training for six months reduced prevalence for all symptoms except vaginal
dryness including a statistically significant decrease in night sweats, mood swings, and
irritability (Moilanen et al., 2012b).
Additionally, in a four-year follow up to this study, 95 of the women agreed to
participate in anthropometric measurements, wear an accelerometer to record PA and
complete a 2-km walk test. Participants completed a questionnaire and kept a one-week
diary on physical, menopausal symptoms and sleep quality. The study showed that
participants in the intervention group had positive long-term effects of exercise training
on hot flash scores relative to the non-exercise group (Mansikkamaki et al., 2016).
Supporting these studies, Luoto et al. (2012) also reported from the same study that six

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months of aerobic exercise training resulted in decreased frequency of hot flashes and
improved quality of life in the 176 participants.
To date, the frequency, duration, and severity of VMS have been measured
through subjective recall diaries which are subject to bias. However, as discussed
previously, a recent preference control trial was the first to objectively measure the effect
of exercise on the severity of hot flashes. The authors used a tube lined jacket and pants
in a small study involving 21 postmenopausal women to quantify the acute physiological
thermoregulatory and cerebrovascular changes that occur during hot flashes prior to and
following a 16-week exercise intervention. The intervention consisted of supervised
weekly exercise sessions which progressed from 30 min of moderate intensity aerobic
exercise (30% heart rate reserve (HRR)) three times per week to 4-5 times per week at 45
min per session at 60% HRR by week 12. At baseline and follow-up, participants
completed a seven-day hot flush frequency and severity diary as well as the physiological
hot flush assessment. The authors observed that 16 weeks of moderate intensity exercise
improved cardiorespiratory fitness and attenuated cutaneous vasodilation, sweating, and
reductions in cerebral blood flow during hot flashes thus reducing the severity of hot
flushes (Bailey et al., 2016).
In their previous work, Bailey et al. (2015) found that exercise training improves
thermoregulatory control and enhances the function of the cerebral and cutaneous
circulations to passive heating alongside improvements in the frequency of menopausal
hot flushes. More specifically, they found that exercise training mediated a reduction in
resting core temperature that enabled earlier onset of sweating and cutaneous
vasodilation, improved sensitivity of sweating and attenuated reductions in cerebral blood

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flow during passive heating. It has also been hypothesized that exercise may have
ameliorating effects on vasomotor symptoms by increasing the presence of hypothalamic
and peripheral ß-endorphin production which may help stabilize the thermoregulatory
center and diminish the risk of hot flushes (Moilanen et al., 2012a) but these mechanisms
will require further invasive investigation by clinical technicians with substantial,
research-validated equipment.
The above noted studies all used moderate intensity continuous training (CT) as
the method of cardiovascular exercise for the intervention. There is however, emerging
evidence regarding the use of high intensity training (HIT) as a more efficient and
effective method of exercise for postmenopausal women.

High Intensity Training (HIT) Protocol
Klonizakis et el. (2014) conducted a two-arm, parallel-group, randomized trial
involving 22 postmenopausal women. The purpose of the trial was to compare the
effects of a low-volume HIT protocol and a higher-volume moderate intensity CT
protocol on cardiopulmonary function and macrovascular endothelial function (measured
via brachial artery flow-mediated dilation (FMD)). Baseline data was collected for
macrovascular function (ultrasound of brachial artery, resting blood pressure, resting
heart rate), microvascular function (laser Doppler flowmetry of forearm), and
cardiopulmonary function (VO2peak) and all tests were repeated at two-week follow up.
The exercise protocol consisted of six sessions over two weeks on alternating days with
all sessions completed on an electronically braked cycle ergometer. For the HIT group,

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training consisted of 10 x 1-minute intervals at 100% of peak power, interspersed with 1
minute of light active recovery and workload was increased as tolerated by participants.
The CT group performed 40 minutes of continuous cycling at an intensity of 65% peak
power. All exercise sessions were supervised by an exercise physiologist. The results of
the study suggest that the low-volume HIT protocol promotes rapid increases in
cardiopulmonary function but not vascular function in postmenopausal women
(Klonizakis et al., 2014). This type of protocol may therefore provide an alternative
training strategy for those with limited time available for exercise.
Three studies have examined the effects of HIT on anthropometric measures and
body composition. Grossman and Payne (2016) conducted a small, randomized
controlled pilot study with 18 postmenopausal women to compare the effects of shortduration, HIT and traditional exercise on body weight, body composition, and
anthropometric measures. Data was collected at baseline, week 6 and week 12 and
included height, weight, measures of waist, hips, abdomen, right bicep and right thigh,
bodyfat % (DEXA Scan) and demographic information. The participants were
randomized into either Standard Condition (SC) or HIT. The SC group were instructed
to walk at an intensity level of 65% of their age-predicted maximal heart rate. For the
first week participants were instructed to walk 10 mins each day for five days per week
and 5 mins were added to each day for every subsequent week until they had reached 200
mins per week (week 6) and this was maintained for weeks 7-12. Exercise intensity was
measured using a heart rate monitor and calorie wristband device. The HIT group
received the “10-min Trainer” DVD and were instructed to exercise 10 mins per day for
five days each week for the entire 12-week intervention. The DVD set included five

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different groups of exercises ‘total body’, ‘cardio’, ‘lower body’, ‘abs’, and ‘yoga flex’
which were completed in random order, one each day. The DVD exercises were 1
minute in length, with 10 different exercises within each segment. The results showed
both groups achieved decreases in four of five anthropometric measures with both groups
seeing increases in the bicep measure. Additionally, both groups experienced decreases
in body weight, body fat %, fat mass and BMI as well as increases in fat free mass.
There was a greater increase in fat free mass and a greater decrease in body fat % in the
HIT group but the results were not statistically significant (Grossman & Payne, 2014).
This may have been due to the small sample size and the short duration of the
intervention. This study was the first to compare HIT and CT in postmenopausal women
and provides a foundation for future study in this area.
A systematic review and meta-analysis by Weston, Wisloff and Coombes (2014)
found that HIT significantly increased cardiorespiratory fitness by almost double (19.4%
vs 10.3%) over moderate-intensity CT in patients with lifestyle-induced cardiometabolic
disease. As women are at a greater risk for cardiometabolic disease as they transition
through menopause, there is an increased need to explore this modality of training for this
population. Mandrup et al. (2017) recently conducted a three-month parallel group study
with 79 healthy, non-obese late premenopausal (n=40) and postmenopausal (n=39)
women. The aim of the study was to investigate the effect of a well-controlled highintensity aerobic training program on risk factors for type II diabetes and cardiovascular
disease between late premenopausal and postmenopausal (age difference of 4 years).
Data collected at baseline and three months included height, weight, BMI, waist
circumference, body composition (DEXA scan), blood samples, blood pressure, resting

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heart rate, oral glucose tolerance test, and VO2-max.

The intervention consisted of three

months of high-intensity spinning conducted three times per week for one hour. Two of
the weekly sessions were conducted in the research exercise facility by instructors from
the research group and one session took place in a local fitness center. The intensity of
the training sessions increased gradually during the 3-month period. Participants wore
heart rate monitors for all sessions. The results indicate that 3-months of high intensity
training induced substantial beneficial effects on aerobic fitness and a number of risk
factors and that postmenopausal women experienced the same positive adaptations to
training as premenopausal women (Mandrup et al., 2017). Both groups had decreases in
BMI, weight, waist circumference, fat mass, fat percentage and lean body mass increased
in both groups. Both groups also showed reductions in total cholesterol and fasting
insulin, however, the postmenopausal group had a better response to the intervention as
they had lower fasting glucose (27% vs 3%). The postmenopausal group also had
significantly lower systolic BP while the premenopausal women had a slight elevation
(nonsignificant) suggesting high intensity exercise helped to normalize vascular function
for postmenopausal women. The results of this study show the importance of
recommending physical activity to postmenopausal women to mitigate risk factors
beyond weight loss.
Physical activity has been shown to be important in the psychological wellbeing
of menopausal women alongside the physical adaptations experienced. Freese et al.
(2014) conducted a small, randomized controlled trial of 47 women pre-and
postmenopausal, who were considered to be at risk of developing MetS. The purpose of
the study was to determine whether six weeks of sprint interval training (SIT) is

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associated with changes in mood and perceived health in women at risk for MetS.
Participants were randomized to either six weeks of SIT or control and all participants
met weekly with a nutritionist to ensure weight remained stable throughout the trial.
Participant characteristics of age, weight, height, BMI, waist circumference, and bodyfat
were all recorded at baseline and after the 6-week intervention. Mood and perceived
health was measured using the 30 item Profile of Mood States – Brief (POMS-B)
questionnaire both at baseline and again at six weeks. The SIT group completed three
exercise session per week for six weeks. Training sessions were completed in the
laboratory under supervision and consisted of repeated 30 second all-out cycling sprints
(4-8 sprints per session) against a fixed resistance of 0.09 kg per kilogram of fat free
mass. Participants completed four sprints per session for the first two weeks and then the
number of sprints was increased by one sprint per session in subsequent weeks until
completing eight sprints per session in week six. The results of this study show that six
weeks of HIT led to significant increases in role-physical scores (ES=0.64) and vitality
(ES=0.52) suggesting increased feelings of vitality and perceptions of having fewer
physical limitations and that HIT does not produce mood disturbances (Freese et al.,
2014). The conclusion we can draw from the research on HIT would suggest that the
low-volume nature makes for an attractive option for postmenopausal women and has
shown to be effective in producing positive physiological and psychological outcomes.

Resistance Training
Given the impact of sarcopenia in menopause and the resulting decrease in muscle
strength, resistance training is an important factor for women to maintain physical

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functioning through aging. The most common muscle associated changes in menopause
include: increase in connective tissue and intramuscular fat, decrease of type II fibers,
and decrease of estrogen receptors (Maltais et al., 2009). Since muscle loss is influenced
by several factors such as age, low levels of physical activity and inflammatory processes
the impact of menopause specifically on muscle wasting mechanisms remains unclear
(Maltais et al., 2009). Resistance training has been growing in popularity due to its
efficacy and positive results with several pathologies including osteopenia, sarcopenia,
diabetes, and cardiovascular disease, however, it is still not routinely prescribed to
prevent or reduce the symptoms and effects of menopause (Leite et al., 2010). The
literature on this modality of exercise is sparse but there have been a few significant
studies.
A small, randomized controlled trial assigned 20 postmenopausal women to a
resistance training program or control group. Blood samples, blood pressure and
anthropometric measures were taken at baseline and at week 16. Maximal strength tests
were also conducted at baseline and again at week 16. Subjects performed three training
sessions per week on alternate days. During the first 8-week period they performed leg
press, leg extension, leg curl, bench press, latissimus pulldown, lateral raise, triceps
pushdown, arm curl and abdominal crunches. The training consisted of three sets of ten
repetitions maxima, with 60 seconds’ rest between sets. For the second 8-week period,
the same exercises were performed but with eight repetition maxima and a 90-second
rest. This protocol is characterized by high volume/low intensity in the first 8 weeks
followed by low volume/high intensity for the second 8 weeks. Workloads were
increased by 1 kg for lower body and 0.5 kg for upper body for each repetition over the

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established protocol participants were able to perform in the previous week. Study
authors found that the 16 weeks of training reduced the risk of metabolic syndrome as
measured by the metabolic syndrome z-score (P=0.0162). Results also showed
significant decreases in fasting blood glucose (-13.97%), body fat % (-6.75%) and body
fat mass (-7.06%) and showed increases in lean body mass (+2.46%) and muscle strength
(leg press +41.29% & bench press +27.23%) when compared to baseline (Conceiçao et
al, 2013).
Alvarez and Campillo (2013) conducted a parallel-group study comparing
overweight and obese premenopausal women and overweight and obese postmenopausal
women. Thirty-five women were divided into these four groups: 8 overweight
premenopausal, 9 obese premenopausal, 8 overweight postmenopausal, 10 obese
postmenopausal. Data was collected at baseline and weeks 4 and 8 including body
weight, height, BMI, waist circumference and skinfolds. Additionally, at baseline and
end of week 8, maximal strength (1RM) was assessed for squat, bent row, shoulder press
and bicep curl. Subjects attended 24-40-minute supervised sessions twice weekly for the
8 weeks. During weeks 1-3, subjects completed three sets for each of the four exercises
with 20%1RM. For weeks 4-6, subjects completed four sets for each exercise at 25%
1RM and for the final two weeks, subjects completed five sets for each exercise at 30%
1RM. Repetitions in each set were to failure (voluntary exhaustion) with a rest period of
one minute between sets and exercises. This type of protocol is considered low
volume/high intensity. The authors found this eight-week program resulted in significant
reductions in body weight, BMI, waist circumference and skin fold measurements in all

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groups with no significant between group differences. Additionally, all four groups
experienced significant increases in maximal strength on all exercises.
A smaller, non-random controlled study of 32 women looked to evaluate the
effect of 16 weeks of resistance training on the subjective perception of QoL and
secondary outcomes of anthropometric measures (height, weight, BMI, body fat %, skin
folds) and muscle strength (1RM for bench press, leg press & leg curl). Both primary
and secondary outcomes were measured at baseline and week 16. Quality of life was
measured using the World Health Organization questionnaire (WHOQOL-Bref). The
resistance training program lasted 16 weeks and was divided into two stages (E1 & E2).
E1 consisted of alternating upper and lower body exercises for three sets of ten reps with
a rest period of one minute between sets. E2 consisted of alternating upper and lower
exercises for three sets of eight reps and 90 secs of rest between sets. The work load for
all exercises was 70-85% of 1 RM and exercises included were leg extension, leg curl,
bench press, lat pulldown, arm curl, triceps extension, leg press, sit-ups, side plank and
calf raise (for abdominal and calf exercises, three sets of 15 reps were used for entire 16week program). Participants performed three, weekly, supervised, non-consecutive
sessions for 60 mins. The authors found that the 16-week program resulted in significant
changes in muscle strength indicators (P = 0.001), but there were no significant
differences in QoL (except the energy facet of physical domain) or body composition
measures (Boganha et al, 2012).
In determining the optimal training protocol for postmenopausal women, Correa
et al. (2015) is the first study to compare the effects of low volume resistance training
(LVRT) to high volume resistance training (HVRT) on postmenopausal women. The

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objective of their study was to compare the effects of 11 weeks of LVRT and HVRT on
postprandial lipaemia, EE, basal metabolic rate (BMR), post exercise oxygen
consumption (EPOC), muscle strength and muscle thickness. Thirty-six women were
randomly divided into HVRT, LVRT and control group. Both at baseline and post
intervention, data was collected including anthropometric measures, VO2peak test, leg
extension 1 RM test, muscle thickness (B-mode ultrasound), dietary records for three
days preceding pre/post training test, BMR (indirect calorimetry), EE (ergo spirometry)
and oral fat tolerance test, and blood samples. Participants completed 11 weeks of
training performing eight exercises (bench press, bicep curl, triceps extension, one arm
row, leg press, leg extension, leg curl, & crunch) three times per week. Both LVRT and
HVRT groups were required to perform 15 max reps with 40 secs of rest between sets
and exercises. The LVRT group performed one set of the eight exercises ( ̴ 15 mins) and
the HVRT group performed three sets ( ̴ 45 mins). The major finding of this study was
that 11 weeks of HVRT led to decreases in baseline triglycerides and the total serum
triglycerides and both resting fat oxidation and muscle thickness were significantly
increased post training. These results are the first to suggest that resistance training in
postmenopausal women may help reduce the risk of coronary heart disease (Correa et al.,
2015).
The largest and most recent study was a randomized controlled trial with 80
postmenopausal women aged 40-65 years looking to investigate the influence of a 12week resistance training program on perceived QoL. Researchers collected
anthropometric data (height, weight, BMI) and sociodemographic information at baseline
and participants completed the SF-36 for QoL measures at both baseline and post

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intervention. The exercise group participated in training sessions three times per week
for 12 weeks. The exercise intervention included warming-up exercises, walking,
stretching, strengthening exercises with an elastic band, and a cool down. The results
show that the 12 weeks of resistance training resulted in a positive change in all domains
of the SF-36 and statistically significant improvement in QoL scores in the domains of
vitality (P = 0.0046) and mental health (P = 0.0052) (Dabrowska et al, 2016).
While these studies show promising results for the effectiveness of resistance
training for postmenopausal women, the research in this area is still lacking and to date
there have been no large randomized controlled trials measuring the effects of low
volume-high intensity resistance training on menopausal symptoms.

Yoga & Pilates Interventions
Recent focus on the impact of stress on quality of life and menopausal symptoms
has led to studies examining the efficacy of complementary therapies. Reed et al. (2014)
conducted a randomized controlled factorial study comparing yoga and aerobic exercise
interventions combined with a placebo-controlled omega-3 supplement with 355 women
aged 40-62 years. The purpose of the study was to determine the efficacy of three
nonhormonal therapies for the improvement of menopause-related quality of life in
women with VMS. Data was collected at baseline and at 12 weeks using the MENQOL
and secondary outcomes measures included demographic information, weight, height,
BMI, and additional questionnaires (7-item Insomnia Severity Index, Pittsburgh Sleep
Quality Index, 8-item Patient Health Questionnaire for depressive symptoms, and 7-item

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Generalized Anxiety Disorder scale). Frequency and severity of VMS were recorded
retrospectively in daily diaries. The yoga intervention group completed 90 minutes
instructed sessions and daily home practice of 20 minutes was expected. The exercise
intervention group completed three individual cardiovascular sessions per week at a local
facility that were supervised by trainers. Participants exercised 40-60 minutes per session
at a target heart rate of 50-60% of HRR for month one and 60-70% HRR for second
month. Participants also randomly received omega-3 capsules or placebo. The authors
found that relative to usual activity, only the yoga group experienced small (nonsignificant) improvement on MENQOL scores. The exercise group improved scores in
the physical function domain, but it was not enough to improve overall MENQOL scores
(Reed et al., 2014). A second random control trial of 88 postmenopausal women
compared control to stretching exercises to hatha yoga for 12 weeks to investigate the
psychophysiological effects of Hatha Yoga regular practice. Saliva, blood samples and
questionnaires (Lipp Stress Symptom Inventory (LSSI), MRS, WHOQOL-brief, Beck
Depression Inventory (BDI), & State and Trait Anxiety Inventories (STAI)) were
collected at baseline and 12 weeks. The yoga and exercise group attended 75 minutes of
supervised activity twice per week for twelve weeks. Both groups were instructed by a
single, certified Hatha Yoga instructor. Yoga consisted of 45 minutes of asanas followed
by breathing (10 mins) relaxation (10 mins) and meditation (10 mins). Exercises
consisted of stretching for the shoulder girdle and cervical muscles and lower legs. The
main findings of the study were that menopause symptoms, stress symptoms and
depression are reduced and quality of life increased after 12 weeks of yoga (Jorge et al.,
2016).

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Similar to yoga as a complementary therapy, Pilates has been the subject of
research for postmenopausal women as well. Lee et al. (2016) designed a randomized
controlled trial with 74 postmenopausal women to investigate the effects of an 8-week
Pilates exercise program on menopausal symptoms and lumbar strength and flexibility.
Baseline data included a 44-item menopausal symptom questionnaire, lumbar strength
(lumbar extension machine), sit-and-reach, and truck lift tests. All tests were repeated at
the end of the 8-week intervention. The Pilates group completed a series of exercises
adopted from Pilates Academy International for approximately 60 minutes (location and
supervision not indicated in study). The results found the intervention resulted in
decreased VMS, psychological and physical symptoms compared to controls and
significant increases in both lumbar strength and flexibility (Lee et al., 2016).
Complementary therapies like yoga and Pilates have the potential to impact the
psychosocial component of menopausal symptoms but more high-quality research is
needed to confirm the importance of adding this protocol to the exercise prescription for
menopausal women. Exercise, in its various forms, has been shown to ameliorate some
menopausal symptoms or at the very least, alter the psychological impact on the
perceived quality of life in postmenopausal women. However, the development of
symptoms in some women but not others, given that all women experience a decline in
estrogen, suggests that other factors are at play.
The role of muscle mass and oxidative stress has been considered as a possible
mechanism in menopausal symptoms and menopausal-related diseases, specifically
MetS. For postmenopausal women, chronic systemic inflammation, oxidative stress,
abdominal visceral adipose tissue, dyslipidemia, sarcopenia, and a sedentary lifestyle are

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all risk factors for metabolic syndrome (Mendoza et al., 2016). The impact of obesity
and muscle mass as possible risk factors for both MetS and VMS must be examined.

Impact of Body Composition on Postmenopausal Women

Obesity as a Risk Factor for MetS
Abdominal obesity is a known risk factor for metabolic and cardiovascular
diseases (Berrington de Gonzalez, Hartge, & Cerhan, 2010; Chan et al., 1994; Flint et al.,
2010). Women transitioning through menopause frequently experience weight gain but
studies across differing populations suggest weight gain is influenced by age (Lobo et al.,
2014) and the abrupt decline of estrogen during menopause is characterized by increased
subcutaneous abdominal and visceral fat. This results in fat distribution shifting from a
gynoid to an android pattern and an increase in total body fat which has been documented
across ethnicities and in non-obese as well as obese women (Abdulnour et al., 2012;
Kazlauskaite et al., 2015). A small longitudinal study by Franklin, Ploutz-Snyder and
Kanaley (2009) was the first to use magnetic resonance imaging (MRI) to measure total
abdominal, subcutaneous, and visceral fat in premenopausal women and then repeat the
measures 8 years later when they had entered postmenopause. The study found that the
absolute volume of visceral fat increased which may contribute to the increase in
cardiovascular risk after menopause. Total abdominal fat mass increased by 35% (with
no change in body weight) and was due to changes in both subcutaneous and visceral fat
mass and possibly a decrease in abdominal muscle mass (Franklin et al., 2009).

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Abdominal fat mass measured anthropometrically has also been shown to
influence cardiometabolic risk factors. Kabat et al. (2014) used data from a subsample of
women who participated in the Women’s Health Initiative Study (n=2672) and compared
fasting blood samples and anthropometric measurements taken at multiple times over
12.8 years of follow-up. Blood samples provided measures of cardiometabolic risk
including blood glucose, insulin, total cholesterol, low-density lipoprotein cholesterol,
high-density lipoprotein cholesterol (HDL-C) and triglycerides. The purpose of the study
was to examine the association of change in BMI, waist-to-hip ratio (WHR), waist
circumference (WC), and waist circumference-to-height ratio (WCHtR) to the change in
markers of cardiometabolic risk (Kabat et al., 2014). The authors found that an increase
in BMI, WC and WCHtR strongly predicted change in serum triglycerides and glucose,
and reduced HDL-C.

Obesity and VMS
Vasomotor symptoms and MetS share a common nominator of sympathetic
overdrive (Tuomikoski & Savolainen-Peltonen, 2017). Menopausal women exhibiting
VMS may be at risk for MetS and conversely, MetS may exacerbate sympathetic
overdrive thus worsening VMS. Obesity is a mediator both in VMS and MetS and thus
has been the subject of much research. Gold et al. (2017) examined data from the Study
of Women’s Health Across the Nation (SWAN) from 1,546 participants who reported no
VMS at baseline. SWAN is a prospective cohort study that enrolled 3,302 women in
1996-1997 and have recorded follow up data for over 15 years. The current study looked
to analyze whether concurrent BMI or waist circumference and/or changes in weight or

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waist circumference predicted incident VMS using discrete survival analyses, adjusting
for covariates. The authors found that concurrent BMI and waist circumference were
positively related to incident VMS (Gold et al., 2017). In contrast, Gallichio et al. (2014)
analyzed data from an ongoing 5-year cohort study of 631 women to examine the
associations between BMI, change in BMI, and change in weight with hot flashes and
determined there was no relationship between the variables. Thurston et al. (2009)
examined data from the SWAN study that included 1,659 women assessed annually from
2002-2006. For this longitudinal study measures of body fat (bioelectrical impedance),
reproductive hormones (fasting blood sample) and VMS (self-report) were analyzed. The
authors found that gains in body fat over three years were associated with increased
reporting of hot flashes (Thurston et al., 2009). This result echoed their previous findings
of 461 participants from the SWAN study which examined the association of abdominal
adiposity, measured via computed tomography (CT), and hot flashes (Thurston el al.,
2008). This study also found that increased abdominal adiposity is associated with
increased odds of hot flashes. In a third study, Thurston et al. (2008) examined sample
data from 1,776 women in the SWAN study who completed bioelectrical impedance
analysis for assessment of body composition at the sixth annual study visit (2002-2004).
The results of this study indicated that a higher percentage of bodyfat was associated with
increased odds of reporting VMS (odds ratio = 1.27). Finally, in a 2013 longitudinal
study of 536 participants from the SWAN study, Thurston et al. (2013) were the first to
test the relationship between adipokines, adiposity and VMS from data collected over an
8-year period. The authors found that an adverse adipokine profile was associated with
more VMS in early stages of menopause but not later stages.

LEAN BODY MASS IN MENOPAUSE

Association of Lean Body Mass
As menopause leads to a significant decrease in circulating levels of estrogen, it
has been hypothesized that reduced estrogen levels may also influence substrate
utilization (Abildgaard et al., 2013). Decreased fat oxidation and mitochondrial
dysfunction are important factors preceding obesity and type 2 diabetes (Kelley, He,
Menshikova, & Ritov, 2002), thus a change in substrate utilization could explain the
changed metabolic profile after menopause (Abildgaard et al., 2013). Further,
menopause has been shown to be associated with changes in skeletal muscle mass
(Douchi et al, 2002) and muscle mass has proven to play a critical role in whole-body
metabolism. Muscle mass is the most abundant insulin-sensitive tissue (Baron et al.,
1988) and is responsible for up to one third of the oxygen consumption at rest (Zurlo et
al., 1990). Abildgaard et al. (2013) conducted a cross sectional study of 41 healthy
women and found that menopause is associated with lower whole-body fat oxidation and
energy expenditure during exercise. Additionally, the authors found that decreased LBM
seems to be the most important contributor to the observed changes in metabolism.
In a recent study, Takamura et al. (2017) tested the hypothesis that preserved
muscle mass is protective against obesity-associated insulin resistance and metabolic
abnormalities. A total 195 healthy, non-diabetic subjects (88 male: 107 female)
participated in fasting blood samples, anthropometric measures, bioelectrical impedance
and CT of specific skeletal muscles. The authors found that weight-adjusted lean body
mass is protective against weight-associated insulin resistance and metabolic
abnormalities (Takamura et al., 2017). Another recent cross-sectional study examined

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the relationship between muscle mass and metabolic syndrome in a community-based
sample of 394 (138 male: 256 female) mid aged participants (Ou et al., 2016). The
authors concluded that relative lower levels of skeletal muscle mass (mass/weight) had a
higher risk of MetS, particularly in females.
Lee and Lee (2013) conducted a cross-sectional study investigating the
relationship between Kupperman Index scores for symptom severity and muscle strength
and quality in 148 menopausal women. Biomarkers of metabolic risk factors were
assessed along with muscle mass (measured with dual-energy x-ray absorptiometry), and
muscle strength (measured with isometric dynamometry). Muscle quality was calculated
as the ratio of strength to muscle mass in upper extremities. The authors found that the
severity of menopausal symptoms correlated with muscle strength and quality (Lee &
Lee, 2013). Although the precise mechanism to explain the association is unclear, the
authors suggest decreasing levels of all the sex hormones (estrogen, testosterone and
DHEAs) play a role as well as oxidative stress.

Role of Oxidative Stress
Oxidative stress (OS) is characterized by the imbalance between the reactive
oxygen species produced and the effective action of the antioxidant system (SanchezRodriguez et al., 2016). Oxidative damage contributes to loss of muscle strength and a
decline in physical activity, due to the loss of muscle strength, induces oxidative stress
creating a vicious cycle. MetS is also known to be related to aggravated OS in
postmenopausal women due to hyperinsulinemia and the lack of control of glycaemia

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(Park, Kim, Lee & Park, 2011). Furthermore, menopausal women with VMS have
increased oxidative stress, and symptomatic women have lower antioxidant activities as
estrogen is lost as a protective factor (Van Der Schouw & Grobbee, 2005). In a crosssectional study of 94 premenopausal and 93 postmenopausal, Sanchez-Rodriguez et al.
(2012) found that menopause is a risk factor for oxidative stress by examining lipoprotein
levels suggesting that decreasing levels of estrogen contributes to increases in oxidative
stress. In a cross-sectional study of 50 postmenopausal women, Cagnacci et al. (2015)
evaluated the relationship between menopausal symptoms and oxidative stress and found
that abdominal adiposity and hypoestrogenism increase oxidative stress. The authors also
found that VMS markedly reduce antioxidant defenses. However, Bonaccorsi et al.
(2015) conducted a cross-sectional study on 245 peri-and postmenopausal women and
found no association between VMS and oxidative stress levels. The discrepancies
between studies may be due to differences in the potential markers of oxidative stress
measured in each study.
The role of muscle mass and its relationship to oxidative stress in menopausal
women is complex and incompletely understood. Further research is needed to examine
the longitudinal impact of lean body mass on oxidative stress and menopausal symptoms.

LEAN BODY MASS IN MENOPAUSE

Conclusion

Menopause is the period in a woman’s life that is characterized by hormonal
changes that lead to a variety of symptoms that may compromise quality of life. Hot
flashes, night sweats, depression, irritability, sleep disorders, increases in abdominal fat
mass, increased prevalence of metabolic syndrome and increased risk of cardiovascular
disease will affect all women to some degree and may last for long periods of time
(Stefanska et al., 2015). While hormone therapy is available and recommended for
severe symptoms (NAMS, 2015), the majority of women prefer to use non-hormonal
treatments, and exercise is a cost-effective method. Regular physical activity has been
shown in the research to ameliorate symptoms in the emotional and psychological aspects
of menopause, and positively affect perceived ability to cope as well as quality of life
(Elavsky & Kishida., 2015; Haimov-Kochman et al., 2013; Moilanen et al., 2012;
Moratalla-Cecilia et al., 2016; Skrzypulec et al., 2010; Sternfeld & Dugan, 2011). The
research is limited in this area with focus on low-moderate intensity cardiovascular
exercise (Bailey et al., 2016; Daley et al., 2014; Luoto et al., 2012; Sternfeld et al., 2104;
Zhang et al., 2014) where the evidence has shown mixed results. Research regarding
resistance training is very limited (Alvarez & Campillo, 2013; Boganha et al., 2012;
Conceição et al., 2013; Correa et al., 2015; Leite et al., 2010), but has shown a positive
correlation with some metabolic markers and quality of life. Additionally, Abildgaard et
al. (2013) found that decreased lean body mass correlates to low whole- body fat
oxidation and energy expenditure which in turn are associated with high visceral fat mass
and low insulin resistance. Obesity is a risk factor for metabolic syndrome and women

LEAN BODY MASS IN MENOPAUSE

transitioning through menopause are known to experience an increase in abdominal
adiposity (Abdulnour et al., 2012; Franklin et al., 2009; Kabat et al., 2014; Kazlauskaite
et al., 2015). Obesity is also a mediator for both VMS and MetS and it has been
demonstrated that there is a positive relationship between BMI and reporting of VMS
among menopausal women (Gold et al., 2017; Thurston et al., 2009; Thurston et al.,
2008; Thurston et al., 2013). Menopause has also been shown to be negatively associated
with changes in muscle mass (Abildgaard et al., 2013; Baron et al., 1988; Douchi et al.,
2002) but recent studies indicate that preserved muscle mass is protective against
metabolic abnormalities characteristic of MetS (Lee & Lee, 2013; Ou et al., 2016;
Takamura et al., 2017). Loss of muscle mass and increased oxidative stress in
menopause are also related to the development of MetS (Park et al., 2011; SanchezRodriguez et al., 2016; Sanchez-Rodriguez et al., 2012) and there is mixed evidence
regarding association of oxidative stress to VMS (Bonaccorsi et al., 2015; Cagnacci et al.,
2015). There is a complicated relationship whereby menopause increases oxidative
stress, oxidative stress leads to MetS, and MetS is associated with VMS. As lean body
mass protects against MetS, then there is potentially an association between lean body
mass and the development of VMS that is worthy of exploration.

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Appendix B
Problem Statement

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Menopause is said to have occurred after twelve months of amenorrhea due to the
cessation of ovarian follicular function and decreasing estrogen levels (Greendale, Lee &
Arriola, 1999). The transition through menopause can last up to 20 years and is
accompanied by a variety of bothersome vasomotor, psychological, psychosocial, and
urogenital symptoms (Gjelsvik et al., 2011; Huang et al. 2010; Sternfeld & Dugan, 2011)
that are uniquely experienced and impact quality of life for menopausal women. Muscle
loss (sarcopenia) during menopause is of great concern and is primarily due to an
imbalance between muscle protein synthesis and breakdown contributed to by an increase
in oxidative stress, pro-inflammation markers and hormonal changes (Lee & Lee, 2013;
Messier et al., 2011). There is also an overall reduction in blood flow and oxygen
delivery with age (Nyberg et al., 2015), due to structural alterations in the vasculature,
reduction in muscle mass and quality, increased muscle sympathetic outflow and balance
alterations in the vasodilators/vasoconstrictors (Proctor & Parker, 2006) which could
potentially impact vasomotor symptoms. Thus, higher levels of muscle mass during
menopause may potentially protect against the deleterious effects of sarcopenia, mitigate
vascular disturbances and possibly ameliorate the symptom burden experienced by
women leading to improved quality of life.
The purpose of the research was to examine the association of relative lean body
mass to the development of menopausal symptoms over time and the potential correlation
to perceived quality of life through a quantitative descriptive design. This study will be
conducted utilizing the existing dataset from a previous national study (Study of
Women’s Health Across the Nation) to identify, analyze, and describe these factors.

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Appendix C
Additional Methodology

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Appendix C1
Informed Consent
Consent Form - Massachusetts General Hospital SWAN study
(Visit 15)

Partners HealthCare System
Research Consent Form

Subject Identification

General Template
Version Date: October 2014

Protocol Title: The Study of Women's Health Across the Nation: Annual evaluation (VISIT 15)

Principal Investigator: Joel S. Finkelstein, M.D.

Site Principal Investigator:

Description of Subject Population: Pre- or perimenopausal women, aged 42-52 at baseline

About this consent form
Please read this form carefully. It tells you important information about a research study.
A member of our research team will also talk to you about taking part in this research
study. People who agree to take part in research studies are called “subjects.” This term
will be used throughout this consent form.

Partners HealthCare System is made up of Partners hospitals, health care providers, and
researchers. In the rest of this consent form, we refer to the Partners system simply as
“Partners.”
If you have any questions about the research or about this form, please ask us. Taking
part in this research study is up to you. If you decide to take part in this research study,
you must sign this form to show that you want to take part. We will give you a signed
copy of this form to keep.

Why is this research study being done?
We would like permission to enroll you as a participant in a research study. This research is
part of a multi-center study called SWAN sponsored by the National Institutes of Health
Page 1 of 12

Partners HealthCare System
Research Consent Form

Subject Identification

General Template
Version Date: October 2014

(NIH) and is being conducted at Massachusetts General Hospital and 6 other teaching
hospitals across the United States. The overall goal of the study is to investigate a variety of
aspects related to women's health at mid-life in multiple ethnic groups. Topics to be studied
include symptomatology, psychosocial issues, sexuality, health care utilization, racial
differences in bone density and rates of bone loss, potential genes related to osteoporosis,
new blood tests for ovarian function, and risk factors for arteriosclerosis and heart disease.
You have already participated in the first year of data collection for SWAN and may have
participated in multiple follow-up visits to this study; this form describes the procedures for
this year’s follow-up visit in the continuation of the study.

How long will I take part in this research study?
This is a longitudinal study that has been ongoing since 1996 and is scheduled to last for an
additional 2 to 3 years.

What will happen in this research study?
You may be given the choice of having the blood and urine test at home but the remaining tests
(listed below) must be performed at Massachusetts General Hospital. No treatments are involved
in this study and your participation will not affect your ability to receive any form of therapy that
your own physician might recommend. All the tests are non-invasive, and the entire study visit
should take about 4 to 5 hours. Hospitalization is not required in this study.
During some visits, not all of the above tests may be done. In addition, you have the option of
declining any portion of the protocol that you do not feel like doing. You will be asked to sign
the consent form each year you are in the study.
If you agree to continue to participate in this study, you will be asked to participate in:
1) Physical Measures: the research assistant on the study will take measurements of your blood
pressure, height, weight, waist and hip sizes.
2) Blood Draw: The research assistant will take a blood sample from a vein in your arm. The
blood sample will be used to measure your levels of blood fats (e.g. total cholesterol, LDL, and
HDL), glucose (e.g. blood sugar), insulin (a hormone that controls your sugar level), thyroid
stimulating hormone, blood clotting factors, reproductive hormones (such as FSH, estradiol,
estrone, testosterone, and DHEA-S), risk factors for heart disease (e.g. C-Reactive Protein, PAI-1,
Lp(a), and markers of ovarian aging (Mullerian Inhibiting Substance and inhibin B). In any given
year, not all of these tests may be performed. In addition, some blood will be saved for possible
future measurements of factors that help regulate bone metabolism (e.g. Vitamin D and
sclerostin), factors that help regulate body weight (such as leptin), or other factors that have not
yet been determined. However, the additional lab tests may not be performed for a considerable
period of time.
Page 2 of 12

Partners HealthCare System
Research Consent Form

Subject Identification

General Template
Version Date: October 2014

3) Cognitive Assessment: We will administer a Cognitive Functioning Evaluation, which will
measure activities of the mind, such as thinking, reasoning, and remembering

4) Physical function assessment: You will be asked to perform a set of physical functioning
tests including: a) measurement of the time it takes you to rise from sitting in a chair to a
standing position 5 times, b) measurement of the time it takes you to walk 40 feet, c)
measurement of your grip strength by squeezing an instrument called a dynamometer 3 times
with each hand, d) measurement of the time it takes you to walk 4 meters, and e) seeing if you
can stand with your feet in different positions for at least 10 seconds. These tests will take about
10 minutes.

5) Questionnaires: You will have interviewer administered and self-administered questionnaires
that include items about your medical history, medications, alternative medicine usage, menstrual
history, reproductive history, sleep quality, health care utilization, symptoms, mood, social
support, sexual activity, physical activity, childhood trauma, and other lifestyle factors. These
questionnaires should take about 60 minutes to complete. You will have the option of skipping
any questions that you choose not to answer. The questionnaires include some questions that may
be considered sensitive. The questionnaires will be stored in locked files and your name will not
be included with your questionnaires. The questionnaires will be assigned with an ID number
and the code that links the ID numbers to specific individuals will be stored in a separate, locked
file. The questionnaire that includes items related to your sexual activity will be selfadministered and then placed immediately into a sealed envelope so that the research assistant
will not see the responses. To ensure high quality data, a small number of randomly-selected
interviews may be audio taped for later review.

6) Body Composition by Bioelectrical Impedance: You will have your body composition (i.e.
amount of fat and muscle) measured by a technique called bioelectrical impedance. The
bioelectrical impedance test requires that a small clip called an electrode is attached to your skin
through which an electrical current is passed for a few seconds. The electrical current is very
weak, and you will not be able to feel it.

7) Collection of a urine sample: The urine sample will be used to measure markers of bone
breakdown.

Page 3 of 12

Partners HealthCare System
Research Consent Form

Subject Identification

General Template
Version Date: October 2014

8) Bone Density and Vertebral Morphometry by DXA: Bone density of your spine, hip, and
total body will be measured by a technique called DXA. Bone density tests reflect the strength of
your bones and your risk of having a broken bone (fracture). The vertebral morphometry test
assesses the shape of your bones to check for possible broken bones in your back. Vertebral
morphometry test may not be done at this particular visit. DXA tests require you to lie on a
cushioned table without moving and are painless. The DXA and vertebral morphometry take 20
minutes to complete.

9) Monofilament Testing: We will do monofilament testing, which is a simple, noninvasive
screening test to identify people who have lost protective sensation and who are at risk of
developing ulcers. You will be asked to complete a self-administered questionnaire that includes
questions about foot sensation including pain, numbness and temperature sensitivity. We will also
administer monofilament testing, which involves evaluating touch-pressure sensation using a
nylon thread on your feet. The test is painless.

10) Vaginal Sample Collection: We will ask if you would be willing to participate in SWAN’s
vaginal health study that should take about 15-20 minutes.

Many women after menopause have vaginal and bladder problems such as vaginal dryness or
irritation, frequent or an urgent need to urinate, or pain with sex. In SWAN, we want to gain a
better understanding of what may cause some of these bothersome symptoms. We are looking for
women with and without vaginal or bladder problems.

We will be studying two different things: (1) the pH (acidity) of the vagina and (2) the types of
normal bacteria that are also important in keeping the vagina healthy.

To participate, we would like you to self-collect two samples from your vagina in a private space.
We provide you collection kits with instructions that you take into the bathroom by yourself. The
kits have soft swabs (like long q-tips) that you will insert (one by one) about 2 inches (the length
of your little finger) into your vagina. SWAN will reimburse you $50 for these two self-collections
and for completing a one-page questionnaire.

I would like to participate in the optional vaginal sample collection procedure at this time:

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Partners HealthCare System
Research Consent Form

Subject Identification

General Template
Version Date: October 2014

Yes

No Initials___________

11) Actigraphy: We will ask if you would be willing to participate in additional testing that
involves measuring your sleep and physical activity over a span of 7 nights (sleep) and 8 days
(activity). We will ask you to wear a watch that monitors your sleep and an accelerometer that
you would wear around your waist during the day. You will also need to record additional sleep
and physical activity information in a diary throughout the 7 days. You will be asked to return the
monitors and diaries to the SWAN study office in a pre-stamped envelope. You will not be
financially responsible for lost or broken equipment. If you complete this protocol, you will be
provided with your results.
I would like to participate in the optional actigraphy procedure at this time:
Yes

No Initials___________

12) Xtreme CT scan: We will ask you to participate in additional testing to measure your inner
bone structure by an imaging study called an Xtreme CT scan. The Xtreme CT scan requires that
you put your arm and leg in a machine and hold still for several minutes. The Xtreme CT scan
does not require any injections and is painless.
I would like to participate in the option Xtreme CT scan at this time:

Yes

No Initials___________

We would like to store some of your samples and health information for future research related to
women’s health. We will label your samples and health information with a code instead of your
name. The key to the code connects your name to your samples and health information. The
study doctor will keep the key to the code in a password protected computer/locked file.

Do you agree to let us store your samples and health information for future research related to
women’s health?

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Partners HealthCare System
Research Consent Form

Subject Identification

General Template
Version Date: October 2014

Yes

No Initials___________

A notation that you are taking part in this research study may be made in your electronic
medical record. Information from the research that relates to your general medical care
may be included in the record (for example, list of allergies, results of standard blood
tests done at the hospital labs).

What are the risks and possible discomforts from being in this
research study?
The risks from this study are minimal. Blood drawing may cause a small amount of pain. In
addition, a temporary bruise or “black and blue” mark may develop. Rarely, people faint after
blood drawing. Very rarely, the vein in which the needle has been inserted may become inflamed
or infected, which can be treated. As a result of your participation in this study, you will be
exposed to radiation from the DEXA and Vertebral Morphometry, scans used to evaluate the
composition of your bones. The total amount of radiation exposure from all of the bone scans is
less than 10% of the annual natural background radiation from the earth and sky. There are no
known health risks associated with such a dose. However, if you are pregnant there might be risk
of harm from any radiation on your unborn child. If you are a woman of childbearing potential,
you will have a pregnancy test performed free of charge immediately before undergoing the
annual bone density tests. The bioelectrical impedance test involves attaching electrodes to your
skin and then passing a very low level of electricity through your body for a few seconds. You
will not feel anything and there is no known danger to the test. These tests involve no significant
risk to you.

If you receive the Xtreme CT scan, the amount of radiation exposure you will receive from is
equal to a whole-body exposure of 0.01 milliSieverts (mSv). A mSv is a unit of radiation dose.
This amount of radiation is about the same as you would normally get in 1 day from natural
background sources from the earth and sky. Please note that this radiation is not necessary for
your medical care and is for research purposes only. Scientists disagree on whether radiation
doses at these low levels are harmful. A possible effect that could occur at doses used in this
study is a slight increase in the risk of developing cancer later in life. If you are pregnant or breast
feeding, you may not be able to participate in this research study.

What are the possible benefits from being in this research study?

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Partners HealthCare System
Research Consent Form

Subject Identification

General Template
Version Date: October 2014

By participating in this study, you will receive free information about your blood pressure,
cholesterol levels, physical activity and a thorough evaluation of your bone density (which will
help predict your future risk of breaking bones). Furthermore, others may benefit in the future
from the information derived from this study.

The physicians in this study will not be providing you with any medical care. However, the results
of your blood pressure, cholesterol panel, thyroid test, estrogen level (the major hormone made by
the ovaries), FSH level (a test often used to determine if a woman is pre- or postmenopausal), and
bone density tests will be sent to you each year that these tests are performed. You will be informed
about any abnormal test results that are felt to be clinically important and, if necessary, one of the
study physicians will communicate this information directly to your doctor. If you do not have a
doctor, you will be referred either to a doctor in your area or to the Physicians Referral Service at
the MGH. If any test results require immediate attention, this information will be telephoned
immediately to you or your physician.

Can I still get medical care within Partners if I don’t take part
in this research study, or if I stop taking part?
Yes. Your decision won’t change the medical care you get within Partners now or in the future.
There will be no penalty, and you won’t lose any benefits you receive now or have a right to
receive.
Taking part in this research study is up to you. You can decide not to take part. If you decide to
take part now, you can change your mind and drop out later. We will tell you if we learn new
information that could make you change your mind about taking part in this research study.

What should I do if I want to stop taking part in the study?
If you take part in this research study, and want to drop out, you should tell us. We will
make sure that you stop the study safely. We will also talk to you about follow-up care,
if needed.

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Partners HealthCare System
Research Consent Form

Subject Identification

General Template
Version Date: October 2014

Also, it is possible that we will have to ask you to drop out of the study before you finish
it. If this happens, we will tell you why. We will also help arrange other care for you, if
needed.

Will I be paid to take part in this research study?
You will be paid $135 if you come to the MGH SWAN center and complete the
interviews, physical measures, blood draw, and bone density tests. In addition, you will
be reimbursed an additional $15 for transportation each time you come to the MGH. If
you have a home SWAN visit, which will include the interviews, some physical
measures, and a blood draw, you will be paid $75. If you complete your SWAN
interviews over the telephone but do not have the bone density tests, physical measures,
or blood tests, you will be paid $50. If you complete the optional vaginal swab collection
protocol, you will be compensated an additional $50.

What will I have to pay for if I take part in this research study?
Neither you nor your insurance company will be charged for any of the procedures that
are performed as part of this research study.
What happens if I am injured as a result of taking part in this research study?
We will offer you the care needed to treat any injury that directly results from taking part
in this research study. We reserve the right to bill your insurance company or other third
parties, if appropriate, for the care you get for the injury. We will try to have these costs
paid for, but you may be responsible for some of them. For example, if the care is billed
to your insurer, you will be responsible for payment of any deductibles and co-payments
required by your insurer.
Injuries sometimes happen in research even when no one is at fault. There are no plans to
pay you or give you other compensation for an injury, should one occur. However, you
are not giving up any of your legal rights by signing this form.
If you think you have been injured or have experienced a medical problem as a result of
taking part in this research study, tell the person in charge of this study as soon as
possible. The researcher's name and phone number are listed in the next section of this
consent form.
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Partners HealthCare System
Research Consent Form

Subject Identification

General Template
Version Date: October 2014

If I have questions or concerns about this research study,
whom can I call?
You can call us with your questions or concerns. Our telephone numbers are listed
below. Ask questions as often as you want.
Dr. Joel Finkelstein is the person in charge of this research study. You can call him at
(617) 726-3296 Monday through Friday from 9am-5pm.
You can also call Dr. Burnett-Bowie at 617-724-5594 Monday through Friday from 9am5pm or 24 hours a day by calling 617-726-2066 with questions about this research study.
If you have questions about the scheduling of appointments or study visits, call the study
coordinator, Karin Darakananda at (617) 724-2033.
If you want to speak with someone not directly involved in this research study, please
contact the Partners Human Research Committee office. You can call them at 617-4244100.

You can talk to them about:
▪
▪
▪

Your rights as a research subject
Your concerns about the research
A complaint about the research

Also, if you feel pressured to take part in this research study, or to continue with it, they
want to know and can help.

If I take part in this research study, how will you protect my privacy?
During this research, identifiable information about your health will be collected. In the
rest of this section, we refer to this information simply as “health information.” In
general, under federal law, health information is private. However, there are exceptions
to this rule, and you should know who may be able to see, use, and share your health
information for research and why they may need to do so.

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Partners HealthCare System
Research Consent Form

Subject Identification

General Template
Version Date: October 2014

In this study, we may collect health information about you from:
•
•

Past, present, and future medical records
Research procedures, including research office visits, tests, interviews, and
questionnaires

Who may see, use, and share your identifiable health information and why
they may need to do so:
•
•
•
•
•
•
•
•
•
•
•

Partners research staff involved in this study
The sponsor(s) of this study, and the people or groups it hires to help perform this
research
Other researchers and medical centers that are part of this study and their ethics boards
A group that oversees the data (study information) and safety of this research
Non-research staff within Partners who need this information to do their jobs (such as for
treatment, payment (billing), or health care operations)
The Partners ethics board that oversees the research and the Partners research quality
improvement programs.
People from organizations that provide independent accreditation and oversight of
hospitals and research
People or groups that we hire to do work for us, such as data storage companies, insurers,
and lawyers
Federal and state agencies (such as the Food and Drug Administration, the Department of
Health and Human Services, the National Institutes of Health, and other US or foreign
government bodies that oversee or review research)
Public health and safety authorities (for example, if we learn information that could mean
harm to you or others, we may need to report this, as required by law)
Other:
Some people or groups who get your health information might not have to follow the
same privacy rules that we follow and might use or share your health information without
your permission in ways that are not described in this form. For example, we understand
that the sponsor of this study may use your health information to perform additional
research on various products or conditions, to obtain regulatory approval of its products,
to propose new products, and to oversee and improve its products’ performance. We
share your health information only when we must, and we ask anyone who receives it
from us to take measures to protect your privacy. The sponsor has agreed that it will not
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Partners HealthCare System
Research Consent Form

Subject Identification

General Template
Version Date: October 2014

contact you without your permission and will not use or share your information for any
mailing or marketing list. However, once your information is shared outside Partners, we
cannot control all the ways that others use or share it and cannot promise that it will
remain private.
Because research is an ongoing process, we cannot give you an exact date when we will
either destroy or stop using or sharing your health information.
The results of this research study may be published in a medical book or journal or used
to teach others. However, your name or other identifying information will not be used
for these purposes without your specific permission.

Your Privacy Rights
You have the right not to sign this form that allows us to use and share your health
information for research; however, if you don’t sign it, you can’t take part in this research
study.

You have the right to withdraw your permission for us to use or share your health
information for this research study. If you want to withdraw your permission, you must
notify the person in charge of this research study in writing. Once permission is
withdrawn, you cannot continue to take part in the study.

If you withdraw your permission, we will not be able to take back information that has
already been used or shared with others.

You have the right to see and get a copy of your health information that is used or shared
for treatment or for payment. To ask for this information, please contact the person in
charge of this research study. You may only get such information after the research is
finished.

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Partners HealthCare System
Research Consent Form

Subject Identification

General Template
Version Date: October 2014

Informed Consent and Authorization
Statement of Person Giving Informed Consent and Authorization

▪
▪
▪
▪

I have read this consent form.
This research study has been explained to me, including risks and possible benefits (if any), other
possible treatments or procedures, and other important things about the study.
I have had the opportunity to ask questions.
I understand the information given to me.

Signature of Subject:
I give my consent to take part in this research study and agree to allow my health information to
be used and shared as described above.

Subject

Date

Time (optional)

Signature of Study Doctor or Person Obtaining Consent:
Statement of Study Doctor or Person Obtaining Consent
•
•

I have explained the research to the study subject.
I have answered all questions about this research study to the best of my ability.

Study Doctor or Person Obtaining Consent Date
Consent Form Version: 07/08/2016

Page 12 of 12

Time (optional)

Appendix C2
IRB Review Request Approval

Appendix C3

Certificates of IRB and SAS Training

Certification for Canadian Researchers

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